The DOI matches its Crossref record; 280 of the 280 references it cites were found in a registry or an index; 5 of the 249 sources its claims cite were checked for support.
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things
Open accessAbstract
In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the Internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas, including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of AI of things (AIoT). This article presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: 1) perceiving; 2) learning; 3) reasoning; and 4) behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews artificial intelligence of things (AIoT), arguing that deep learning can empower IoT systems to be faster, smarter, greener, and safer across cloud, fog, and edge computing. It organizes progress around perceiving, learning, reasoning, and behaving, then summarizes applications, challenges, and opportunities.
Key findings
- The paper presents a tri-tier AIoT computing architecture—cloud, fog, and edge—and argues that AI/deep learning can improve IoT perception, learning, reasoning, and behavior.
- It cites scale figures: 50 billion IoT-connected devices by 2025 generating 79.4 zettabytes of data, 99.4% of physical objects still unconnected, and an IoT sector contribution of $2.7 to $6.2 trillion to the global economy by 2025.
- It proposes a taxonomy of AI for IoT: perceiving (e.g., image classification, object detection/tracking, semantic segmentation, biometrics, speech, machine translation), learning (e.g., unsupervised/semi-supervised, transfer, domain adaptation, few-/zero-shot, reinforcement, federated learning), reasoning (knowledge graphs, causal reasoning), and behaving (control, interaction).
- It surveys AIoT applications in smart security, transportation, healthcare, education, industry, grids, agriculture, and cities/homes/buildings, and lists challenges including multi-modal heterogeneous data, edge deep learning, computational scheduling, data monopoly, security/privacy, and data-center energy consumption.
Methods
- Design: a comprehensive survey/literature review of AIoT, not an empirical study; the authors state they “carefully select” topics and “only outline the trend of the research progress” rather than diving into details.
- Data/sample: no primary dataset or experiment; the paper reviews prior literature and summarizes representative benchmark datasets in tables across perception, 3D perceiving, auditory perception, NLP/multimedia, etc.
- Main analysis: qualitative synthesis organized by a proposed AIoT architecture and a new taxonomy of perceiving, learning, reasoning, and behaving, followed by application domains and challenges/opportunities.
Limitations
- The paper does not state a formal limitations section.
- Evident from the text: as a survey, it provides no new experiments, datasets, or quantitative validation of its own; its claims depend on the cited literature.
- Evident from the text: coverage is selective and explicitly excludes networking technologies (“beyond the scope of this survey”), and the authors note they only outline trends rather than provide detailed treatment.
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Claims the paper makes
143 Not checked yet · 87 No basis given · 82 Own interpretation · 12 Sources not found · 2 Supported so far · 2 Partly supported · accuracy not yet measured
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In the paper
“Domain adaptation could be useful for AIoT applications when deploying models to new scenarios or new working modes of machines [128], [203], [204], [205], [206], e.g., 'synthetic → real', 'daytime → nighttime' or 'clear → rainy'.”
What the sources say
- Not checked yetcites [128] [128] Q. Wang, J. Gao, W. Lin, and Y. Yuan, 'Learning from synthetic data for crowd counting in the wild,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8198-8207. - source
- Not checked yetcites [203] [203] X. Pan, Y. You, Z. Wang, and C. Lu, 'Virtual to real reinforcement learning for autonomous driving,' arXiv preprint arXiv:1704.03952 , 2017. - source
- Supportedcites [204] [204] K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige et al. , 'Using simulation and domain adaptation to improve efficiency of deep robotic grasping,' in Proceedings of the IEEE International Conference on Robotics and Automation , 2018, pp. 4243-4250. - source
The Related Work section identifies simulation as the source domain and the real world as the target domain. The paper's Introduction describes adapting synthetic images for a physical robot, directly supporting the ‘synthetic → real’ example for which this source is cited. (LLM, conf=0.91)
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In the paper
“However, this centralized architecture faces significant challenges in the context of the massive numbers of sensors used across various applications.”
What the sources say
- Not matchedcites [14] [14] J. Macaulay, L. Buckalew, and G. Chung, 'Internet of things in logistics: A collaborative report by dhl and cisco on implications and use cases for the logistics industry,' DHL Trend Research and Cisco Consulting Services , 2015. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [15] [15] M. Shirer and C. MacGillivray, 'The growth in connected iot devices is expected to generate 79.4 zb of data in 2025, according to a new idc forecast,' 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“In addition to these challenges created by massive numbers of sensors, another challenge arises through their heterogeneous nature [23] including scalar sensors, vector sensors, and multimedia sensors as summarized in Table I.”
What the sources say
- Not checked yetcites [23] [23] C. W. Chen, 'Internet of video things: Next generation iot with visual sensors,' IEEE Internet of Things Journal , 2020. - source
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In the paper
“However, this paradigm of using handcrafted features and shallow models is unsuited to modern IoT applications with multimedia sensors.”
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In the paper
“Second, handcrafted features are usually vulnerable to noise and different types of variance (e.g., illumination, viewpoint) in data, limiting their representation and discrimination capacity.”
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In the paper
“Deploying DNNs into cloud platforms, fog nodes, and edge devices in IoT systems enables the construction of an intelligent hybrid computing architecture capable of leveraging the power of deep learning to process massive quantities of data and extract structured semantic information with low latency.”
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In the paper
“Moreover, with modern computational devices, e.g., graphics processing units (GPUs) and tensor processing units (TPUs), DNNs can efficiently and automatically discover discriminative feature representations from large-scale labeled or unlabeled datasets in a supervised or unsupervised manner [30].”
What the sources say
- Not checked yetcites [30] [30] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, 'Imagenet: A large-scale hierarchical image database,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248-255. - source
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In the paper
“Third, feature design and model training are separate, without joint optimization.”
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In the paper
“Therefore, advances in deep learning have paved a clear way for improving the perceiving ability of IoT systems with large numbers of heterogeneous sensors.”
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In the paper
“Nevertheless, model training can still be scheduled on fog nodes by leveraging federated learning [60].”
What the sources say
- Not checked yetcites [60] [60] Q. Yang, Y. Liu, T. Chen, and Y. Tong, 'Federated machine learning: Concept and applications,' ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 1-19, 2019. - source
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- Probably not a substantial claim. 245
- It may state two separate claims as one. 112
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In the paper
“There has recently been rapid progress in deep learning, with these positive effects also impacting IoT data processing, e.g., streaming data analysis [55], mobile multimedia processing [56], manufacturing inspection [57], and health monitoring.”
What the sources say
- Not matchedcites [55] [55] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, 'Deep learning for iot big data and streaming analytics: A survey,' IEEE Communications Surveys & Tutorials , vol. 20, no. 4, pp. 2923-2960, 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [56] [56] K. Ota, M. S. Dao, V. Mezaris, and F. G. D. Natale, 'Deep learning for mobile multimedia: A survey,' ACM Transactions on Multimedia Computing, Communications, and Applications , vol. 13, no. 3s, pp. 1-22, 2017. - source
- Supportedcites [57] [57] L. Li, K. Ota, and M. Dong, 'Deep learning for smart industry: Efficient manufacture inspection system with fog computing,' IEEE Transactions on Industrial Informatics , vol. 14, no. 10, pp. 4665-4673, 2018. - source
The citation [57] is attached to manufacturing inspection, not the other listed applications. The paper’s abstract (p. 2) describes a deep-learning-based inspection system for industrial IoT sensor data, and the Introduction (p. 2) identifies manufacture inspection as its application. (LLM, conf=0.93)
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In the paper
“The fully CNN with an encoder-decoder structure has become the de facto paradigm for semantic segmentation [75], [76], since it can learn discriminative and multi-resolution features through cascaded convolution blocks while preserving spatial correspondence.”
What the sources say
- Partly supportedcites [75] [75] O. Ronneberger, P. Fischer, and T. Brox, 'U-net: Convolutional networks for biomedical image segmentation,' in Proceedings of the International Conference on Medical Image Computing and ComputerAssisted Intervention , 2015, pp. 234-241. - sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [76] [76] J. Long, E. Shelhamer, and T. Darrell, 'Fully convolutional networks for semantic segmentation,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431-3440. - sourceExperimental — accuracy not yet measured
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In the paper
“For example, the recently proposed SimCLR method defines a context-based contrasting task for self-supervised learning [34], obtaining comparable performance as fully supervised models.”
What the sources say
- Partly supportedcites [34] [34] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, 'A simple framework for contrastive learning of visual representations,' arXiv preprint arXiv:2002.05709 , 2020. - sourceExperimental — accuracy not yet measured
Section 2.1 (p. 2) describes SimCLR as a self-supervised contrastive task using augmented views of the same image. The Abstract (p. 1) says its ImageNet result matches a supervised ResNet-50. The description “context-based” is imprecise: Appendix C (p. 20) distinguishes SimCLR from methods that explicitly define a context-prediction task. (LLM, conf=0.83)
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In the paper
“Besides, the architecture of the network matters. Even with fewer model parameters and computational complexity, the recently proposed networks such as ResNet and DenseNet outperform previous ones such as VGGNet.”
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In the paper
“As can be seen, with the increase of network depth and the number of parameters, the representation capacity also increases, leading to lower top1 classification error on the ImageNet dataset.”
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In the paper
“Transmitting this huge amount of data requires massive bandwidth, and cloud processing and sending the results back to end devices leads to high latency.”
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None of the sources cited for this claim have been checked yet.
In the paper
“The IoT represents a key infrastructure for supporting various applications [4], e.g., smart homes [5], [6], smart transportation [7], [8], smart grids [9], and smart healthcare [10], [11].”
What the sources say
- Not checked yetcites [4] [4] L. Da Xu, W. He, and S. Li, 'Internet of things in industries: A survey,' IEEE Transactions on Industrial Informatics , vol. 10, no. 4, pp. 22332243, 2014. -
- Not checked yetcites [5] [5] X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin, 'Smart community: An internet of things application,' IEEE Communications Magazine , vol. 49, no. 11, pp. 68-75, 2011. -
- Not checked yetcites [6] [6] B. L. R. Stojkoska and K. V. Trivodaliev, 'A review of internet of things for smart home: Challenges and solutions,' Journal of Cleaner Production , vol. 140, pp. 1454-1464, 2017. -
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In the paper
“Based on reports from Cisco [14] and IDC [15], 50 billion devices will be IoT connected by 2025, generating 79.4 zettabytes of data.”
What the sources say
- Not matchedcites [14] [14] J. Macaulay, L. Buckalew, and G. Chung, 'Internet of things in logistics: A collaborative report by dhl and cisco on implications and use cases for the logistics industry,' DHL Trend Research and Cisco Consulting Services , 2015. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [15] [15] M. Shirer and C. MacGillivray, 'The growth in connected iot devices is expected to generate 79.4 zb of data in 2025, according to a new idc forecast,' 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“To address this issue, 'fog computing', coined by Cisco [16], aims to bring storage, computation, and networking capacity to the edge of the network (e.g., to distributed fog nodes such as routers) in proximity to the devices.”
What the sources say
- Not matchedcites [16] [16] F. Bonomi, 'Connected vehicles, the internet of things, and fog computing,' in The Eighth ACM International Workshop on Vehicular Inter-Networking , 2011, pp. 13-15. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“A typical IoT architecture has three layers [13]: a perception layer, a network layer, and an application layer.”
What the sources say
- Not matchedcites [13] [13] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, 'A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,' IEEE Internet of Things Journal , vol. 4, no. 5, pp. 1125-1142, 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“In conventional IoT solutions, data collected from sensors are transmitted to the cloud computing platform through the networks for further processing and analysis before delivering the results/commands to end devices/actuators.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, edge cases exist in the real world that may not be seen in the training set nor defined in the label set, resulting in degeneration of a pre-trained model.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“This heralds the era of the artificial intelligence of things (AIoT), where AI encounters IoT.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Advanced communication technologies such as Wi-Fi 6 and 5G and AI technologies will enable mass connection.
None of the sources cited for this claim have been checked yet.
In the paper
“Fog computing offers the advantages of low latency and high computational capacity for IoT applications [17], [18].”
What the sources say
- Not checked yetcites [17] [17] M. Chiang and T. Zhang, 'Fog and iot: An overview of research opportunities,' IEEE Internet of Things Journal , vol. 3, no. 6, pp. 854864, 2016. - source
- Not checked yetcites [18] [18] J. Ni, K. Zhang, X. Lin, and X. S. Shen, 'Securing fog computing for internet of things applications: Challenges and solutions,' IEEE Communications Surveys & Tutorials , vol. 20, no. 1, pp. 601-628, 2017. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, IoT applications based on specific things, e.g., the Internet of Vehicles (IoV) [7] and Internet of Video Things (IoVT) [23] have also been rapidly developed.”
What the sources say
- Not checked yetcites [7] [7] J. Contreras-Castillo, S. Zeadally, and J. A. Guerrero-Iba˜ nez, 'Internet of vehicles: Architecture, protocols, and security,' IEEE Internet of Things Journal , vol. 5, no. 5, pp. 3701-3709, 2017. - source
- Not checked yetcites [23] [23] C. W. Chen, 'Internet of video things: Next generation iot with visual sensors,' IEEE Internet of Things Journal , 2020. - source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“'Edge computing' has also recently been proposed by further deploying computing capacity on edge devices in proximity to sensors and actuators [19], [20].”
What the sources say
- Not matchedcites [19] [19] N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie, 'Mobile edge computing: A survey,' IEEE Internet of Things Journal , vol. 5, no. 1, pp. 450-465, 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [20] [20] J. Pan and J. McElhannon, 'Future edge cloud and edge computing TABLE IX LIST OF ABBREVIATIONS. | Abbreviation | Description | |----------------|------------------------------------------| | 5G | 5th Generation Mobile Networks | | AI | Artificial Intelligence | | AIoT | Artificial Intelligence of Things | | AR/VR | Augmented Reality / Virtual Reality | | ASIC | Application-Specific Integrated Circuit | | ASR | Automatic Speech Recognition | | CNN | Convolutional Neural Networks | | CTC | Connectionist Temporal Classification | | DA | Domain Adaptation | | DNN | Deep Neural Network | | DRL
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Massive sensor data must be processed to extract useful information before being used for further analysis and decision-making.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deep learning has facilitated progress in these areas, suggesting that deep learning can be similarly leveraged to improve IoT system learning.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Moreover, the context of IoT sensors can provide auxiliary information to help understand sensor data. Therefore, various context-aware computing methods have been proposed for IoT [54].”
What the sources say
- Not checked yetcites [54] [54] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, 'Context aware computing for the internet of things: A survey,' IEEE Communications Surveys & Tutorials , vol. 16, no. 1, pp. 414-454, 2013. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another example is in industry, where the operating modes of machines may drift or change due to fatigue or wear and tear. Consequently, models trained for the initial mode cannot adapt to this variation, leading to a performance loss.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, multimedia sensor data are high-dimensional and unstructured (semantics are unavailable without additional processing), so it is difficult to design handcrafted features for them without domain knowledge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Perceiving and understanding dynamic and complex environments from sensor data is fundamental to IoT applications providing useful services to users.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“According to Cisco's white paper [43], 99.4% of physical objects are still unconnected.”
What the sources say
- Not matchedcites [43] [43] J. Bradley, J. Barbier, and D. Handler, 'Embracing the internet of everything to capture your share of $14.4 trillion,' White Paper, Cisco , vol. 318, 2013. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, to interact with the environment and humans, an IoT system should be able to reason and behave.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“These issues are related to some well-known machine learning research topics including fewshot learning [31], zero-shot learning [32], meta-learning [33], unsupervised learning [34], semi-supervised learning [35], transfer learning [36], and domain adaptation [37], [38].”
What the sources say
- Not matchedcites [31] [31] Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, 'Generalizing from a few examples: A survey on few-shot learning,' ACM Computing Surveys , vol. 53, no. 3, pp. 1-34, 2020. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [32] [32] W. Wang, V. W. Zheng, H. Yu, and C. Miao, 'A survey of zero-shot learning: Settings, methods, and applications,' ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 1-37, 2019. - source
- Not checked yetcites [33] [33] C. Finn, P. Abbeel, and S. Levine, 'Model-agnostic meta-learning for fast adaptation of deep networks,' in Proceedings of the 34th International Conference on Machine Learning , 2017. - source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These data may contain some biometric information such as faces, voice, or fingerprints.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Edge computing can process and analyze data on premises and make decisions instantly, thereby benefitting latency-sensitive IoT applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Deep neural networks (DNNs) have been widely used in many areas and have achieved excellent performance in many applications including speech recognition [24], face recognition [25], image classification [26], object detection [27], semantic segmentation [28], natural language processing [29]”
What the sources say
- Not checked yetcites [24] [24] H. Sak, A. Senior, and F. Beaufays, 'Long short-term memory recurrent neural network architectures for large scale acoustic modeling,' in Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association , 2014. - source
- Not checked yetcites [25] [25] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, 'Deepface: Closing the gap to human-level performance in face verification,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 1701-1708. - source
- Not matchedcites [26] [26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' in Advances in Neural Information Processing Systems , 2012, pp. 1097-1105. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Cyberattacks on IoT systems may result in data leakage, so data security and privacy have become a critical concern in IoT applications [18].”
What the sources say
- Not checked yetcites [18] [18] J. Ni, K. Zhang, X. Lin, and X. S. Shen, 'Securing fog computing for internet of things applications: Challenges and solutions,' IEEE Communications Surveys & Tutorials , vol. 20, no. 1, pp. 601-628, 2017. -
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although an IoT's perception system is a critical component of the architecture, simply adapting to and interacting with the dynamic and complex world is insufficient.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Data mining and machine learning approaches have been used for IoT data processing and analysis [52], [53].”
What the sources say
- Not checked yetcites [52] [52] C.-W. Tsai, C.-F. Lai, M.-C. Chiang, and L. T. Yang, 'Data mining for internet of things: A survey,' IEEE Communications Surveys & Tutorials , vol. 16, no. 1, pp. 77-97, 2013. - source
- Not matchedcites [53] [53] M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, 'Machine learning for internet of things data analysis: A survey,' Digital Communications and Networks , vol. 4, no. 3, pp. 161-175, 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Massive user data are collected via ubiquitous connected sensors, which may be transmitted and stored in the cloud through IoT networks.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These application scenarios could benefit from recent advances in causal inference and discovery [39], graphbased reasoning [40], reinforcement learning [41], and speech recognition and synthesis [24], [42].”
What the sources say
- Not matchedcites [39] [39] Y. Zhang, S. Qian, Q. Fang, and C. Xu, 'Multi-modal knowledgeaware hierarchical attention network for explainable medical question answering,' in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1089-1097. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [40] [40] X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T.-S. Chua, 'Explainable reasoning over knowledge graphs for recommendation,' in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5329-5336. - source
- Not checked yetcites [41] [41] J. Zhang, B. Hao, B. Chen, C. Li, H. Chen, and J. Sun, 'Hierarchical reinforcement learning for course recommendation in moocs,' in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 435-442. - source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, access control [58] and trust management [59] approaches have been reviewed to protect the security and privacy of IoT.”
What the sources say
- Not checked yetcites [58] [58] J. Qiu, Z. Tian, C. Du, Q. Zuo, S. Su, and B. Fang, 'A survey on access control in the age of internet of things,' IEEE Internet of Things Journal , 2020. - source
- Not checked yetcites [59] [59] Z. Yan, P. Zhang, and A. V. Vasilakos, 'A survey on trust management for internet of things,' Journal of Network and Computer Applications , vol. 42, pp. 120-134, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The last few years has witnessed a renaissance in artificial intelligence (AI) assisted by deep learning.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Open Neural Network Exchange (ONNX) 5 offers this feature by defining an open format built to represent machine learning models, which has been supported by TensorFlow and Pytorch.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“State-of-the-art methods can be categorized into two groups: two-stage methods and one-stage methods.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides, Field-Programmable Gate Arrays (FPGA) have also been used for DNN acceleration due to their low power consumption and high throughput.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides, fog computing can provide continuity of service without the need for the Internet, which is important for specific AIoT applications with an unstable Internet connection, e.g., in agriculture, mining, and shipping domains.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Its performance can be improved further by exploiting and enhancing the feature hierarchy of deep convolutional networks [80], employing non-local attention [81], and leveraging the reciprocal relationship between detection and segmentation via hybrid task cascade [82].”
What the sources say
- Not checked yetcites [80] [80] T.-Y. Lin, P. Doll´ ar, R. Girshick, K. He, B. Hariharan, and S. Belongie, 'Feature pyramid networks for object detection,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2117-2125. - source
- Not checked yetcites [81] [81] X. Wang, R. Girshick, A. Gupta, and K. He, 'Non-local neural networks,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7794-7803. - source
- Not checked yetcites [82] [82] K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang et al. , 'Hybrid task cascade for instance segmentation,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4974-4983. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides, some popular applications in smartphones also benefit from the advances in this area for recognizing flowers and birds, and food items and calories.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Lightweight networks are appealing to AIoT applications where DNNs are deployed on edge devices.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although functioning similarly to cloud computing, fog computing offers a key advantage, i.e., low latency, since it is closer to devices.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is noteworthy that the edge things and fog nodes are always distributed while the cloud is centralized in the AIoT network topology.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, some computationally efficient networks like MobileNet have been proposed by leveraging depth-wise convolutions [65], point-wise convolutions [66], or binary operations [67].”
What the sources say
- Not checked yetcites [65] [65] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, 'Mobilenets: Efficient convolutional neural networks for mobile vision applications,' arXiv preprint arXiv:1704.04861 , 2017. - source
- Not checked yetcites [66] [66] J. Zhang, Y. Cao, Y. Wang, C. Wen, and C. W. Chen, 'Fully pointwise convolutional neural network for modeling statistical regularities in natural images,' in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 984-992. - source
- Not checked yetcites [67] [67] M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, 'Xnor-net: Imagenet classification using binary convolutional neural networks,' in Proceedings of the European Conference on Computer Vision . Springer, 2016, pp. 525-542. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Empowering things with the perceiving ability, i.e., understanding the environment using various sensors, is fundamental for AIoT systems.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With the off-the-shelf deep learning tools and scalable computing hardware, it is easy to set up the production environment on the cloud, where deep neural networks are trained and deployed to process the massive amounts of data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To this end, different open-source deep learning frameworks have been developed, from the beginners like Caffe 1 and MatConvNet 2 to the popular TensorFlow 3 and PyTorch 4 .”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Caffe2 improves it accordingly, which has been later merged into PyTorch.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Caffe is implemented in C++ with Python and Matlab interfaces and well-known for its speed but does not support distributed computation and mobile deployment.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The cloud enables AIoT enterprises to use computing resources virtually via the Internet instead of building their physical infrastructure on premises.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Several machine learning processors have also been developed for fog and edge computing, e.g., Google Edge TPU and NVIDIA Jetson Nano.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Detection of specific category of objects such as pedestrian, car, traffic-sign, and the license plate has been widely studied, which are useful for improving the perceiving ability of AIoT systems for traffic and public safety surveillance and autonomous driving [70].”
What the sources say
- Not checked yetcites [70] [70] C. Chen, A. Seff, A. Kornhauser, and J. Xiao, 'Deepdriving: Learning affordance for direct perception in autonomous driving,' in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 2722-2730. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“A well-known baseline model is Mask R-CNN which adopts an extra branch for object mask prediction in parallel with the existing one for bounding box regression [79].”
What the sources say
- Not checked yetcites [79] [79] K. He, G. Gkioxari, P. Doll´ ar, and R. Girshick, 'Mask r-cnn,' in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2961-2969. - source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They also support for deploying models into mobile devices by enabling model compression/quantization and hardware acceleration.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides, object detection is a crucial technique for video data structuring in many AIoT systems using visual sensors, which aims to extract and organize compact structured semantic information from video data for further retrieval, verification, statistics, and analysis at low transmission, storage, and computation cost.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The edge computing layer may function like the perception layer in [13] and smart visual sensing block in [23]. It also supports control and execution over sensors and actuators. Thereby, this layer aims to empower AIoT systems with the ability to perceive and behave.”
What the sources say
- Not checked yetcites [13] [13] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, 'A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,' IEEE Internet of Things Journal , vol. 4, no. 5, pp. 1125-1142, 2017. - source
- Not checked yetcites [23] [23] C. W. Chen, 'Internet of video things: Next generation iot with visual sensors,' IEEE Internet of Things Journal , 2020. - source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, it is common to deploy multiple different models into cloud platforms, fog nodes, and edge devices in an AIoT system to build an intelligent hybrid computing architecture.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While GPU is initially developed for accelerating image rendering on display devices, the generalpurpose GPU turns the massive computational power of its shader pipeline into general-purpose computing power (e.g., for massive vector operations), which has sparked the deep learning revolution along with DNN and big data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A great advantage of edge computing over fog and cloud computing is the reduction of latency and network bandwidth since it can process data into compact structured information on-site before transmission, which is especially useful for AIoT applications using multimedia sensors.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The other advantage of fog computing is the protection of data security and privacy since data can be held within the LAN.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Boundary refinement aims to obtain sharp boundaries between different categories in the segmentation map, which can be achieved by using conditional random field as the postprocessing technique on the predicted probability maps [28].”
What the sources say
- Not checked yetcites [28] [28] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, 'Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 4, pp. 834848, 2017. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It can provide flexible, scalable, and reliable resources including computation, storage, and network for enabling various AIoT applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, due to its limited computation capacity, only lightweight DNNs can run on edge devices.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The features like dynamic computation graphs and automatic computation of gradients in TensorFlow and PyTorch have made them easy to use and popular.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By intelligently offloading part of the computation workload from edge devices to the fog nodes and cloud, it is expected to achieve low latency while leveraging deep learning capacities for processing massive amounts of data.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, research topics including neural network architecture design or search for mobile setting and network pruning/compression/quantization have attracted increasing attention recently.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Classical machine learning methods based on hand-crafted features have been surpassed by DNNs [26] on large-scale benchmark datasets like ImageNet [30], sparking a wave of research on the architecture of DNNs.”
What the sources say
- Not matchedcites [26] [26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' in Advances in Neural Information Processing Systems , 2012, pp. 1097-1105. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [30] [30] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, 'Imagenet: A large-scale hierarchical image database,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248-255. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Recently, an application-specific integrated circuit (ASIC) named TPU is designed by Google specifically for neural network machine learning.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The fog and cloud computing layers mainly aim to empower AIoT systems with the ability of learning and reasoning since they can access massive amounts of data and have vast computation resources.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another feature is that it can leverage all the data from the registered devices in an AIoT application, which is useful for training deep models with better representation and generalization ability.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Image recognition can be very useful in many AIoT applications, such as smart education tools or toys, which help and teach children to explore the world with cameras.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are other deep learning frameworks like MXNet 6 , Theano 7 , PaddlePaddle 8 , and neural network inference computing framework for mobile devices like ncnn 9 .”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“A simple but strong baseline model is proposed in [84], which adds a semantic segmentation branch into the Mask R-CNN framework and uses a shared feature pyramid network backbone [80].”
What the sources say
- Not matchedcites [84] [84] A. Kirillov, R. Girshick, K. He, and P. Doll´ ar, 'Panoptic feature pyramid networks,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6399-6408. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [80] [80] T.-Y. Lin, P. Doll´ ar, R. Girshick, K. He, B. Hariharan, and S. Belongie, 'Feature pyramid networks for object detection,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2117-2125. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The methods for object detection from images have been revolutionized by DNNs.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Lots of operations in the neural network such as convolution can be computed in parallel on GPU, significantly reducing the training and inference time.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Besides, network compression such as pruning and quantization can be used to obtain lightweight models from heavy models, which will be reviewed in Section III-A17.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“An image can be decomposed into the reflectance map and illumination map based on the Retinex theory [145].”
What the sources say
- Not checked yetcites [145] [145] E. H. Land, 'The retinex theory of color vision,' Scientific american , vol. 237, no. 6, pp. 108-129, 1977. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, a differentiable architecture named neural graph optimizer is proposed for global pose graph optimization [136]. Together with a local pose estimation model, it achieves a complete end-to-end neural network solution for SLAM.”
What the sources say
- Not checked yetcites [136] [136] E. Parisotto, D. Singh Chaplot, J. Zhang, and R. Salakhutdinov, 'Global pose estimation with an attention-based recurrent network,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 237-246. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nevertheless, due to the prevalence of cameras and great progress in deep learning and computer vision, visual hand gesture recognition has the vast potential, which can be categorized into two groups, i.e., static ones and dynamic ones.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Cameras are prevalent.
None of the sources cited for this claim have been checked yet.
In the paper
“Estimation of human pose and recognition of action can be very useful in many real-world AIoT scenarios, such as rehabilitation exercises monitoring and assessment [120], dangerous behavior monitoring [121], and human-machine interaction (HMI).”
What the sources say
- Not matchedcites [120] [120] D. Gonz´ alez-Ortega, F. D´ ıaz-Pernas, M. Mart´ ınez-Zarzuela, and M. Ant´ on-Rodr´ ıguez, 'A kinect-based system for cognitive rehabilitation exercises monitoring,' Computer Methods and Programs in Biomedicine , vol. 113, no. 2, pp. 620-631, 2014. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [121] [121] A. Kashevnik, I. Lashkov, and A. Gurtov, 'Methodology and mobile application for driver behavior analysis and accident prevention,' IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 6, pp. 2427-2436, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Face detection, as a specific subarea of object detection, benefits from the recent success of deep learning in generic object detection.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Different geometric cues have been exploited for formulating the optimization constraints in optimization-based methods or loss functions in learning-based methods [157], such as lines and vanishing points.”
What the sources say
- Not checked yetcites [157] [157] Z. Xue, N. Xue, G.-S. Xia, and W. Shen, 'Learning to calibrate straight lines for fisheye image rectification,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 1643-1651. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although CNN has powerful representation capacity, special effort has to be made to address the challenges including occlusions and dynamic objects, as well as the scale issue (per-frame ambiguity and temporally inconsistent).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Hand gesture recognition can be very useful for interactions with things in AIoT systems, e.g., non-contact control of television and car infotainment system, and communication with the speech and hearing impaired [125].”
What the sources say
- Not checked yetcites [125] [125] T. Starner, J. Weaver, and A. Pentland, 'Real-time american sign language recognition using desk and wearable computer based video,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 20, no. 12, pp. 1371-1375, 1998. - source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Recently, generative adversarial networks (GAN) have been used to generate style-transferred images for bridging the domain gap between different datasets [111].”
What the sources say
- Not checked yetcites [111] [111] L. Wei, S. Zhang, W. Gao, and Q. Tian, 'Person transfer gan to bridge domain gap for person re-identification,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 79-88. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Besides, a DCNN-based palmprint verification system named DeepMPV is proposed for mobile payment in [104].”
What the sources say
- Not checked yetcites [104] [104] Y. Zhang, L. Zhang, X. Liu, S. Zhao, Y. Shen, and Y. Yang, 'Pay by showing your palm: A study of palmprint verification on mobile platforms,' in Proceedings of the IEEE International Conference on Multimedia and Expo . IEEE, 2019, pp. 862-867. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, most CNN-based methods directly regress the crowd density map, in which the ground truth is constructed by placing Gaussian density maps at the head regions.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although extra efforts are needed to build practical person re-identification systems, one can leverage the idea of humanin-the-loop artificial intelligence to achieve high performance with low labor effort.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with fingerprint, palmprint has abundant features and can be captured using common built-in cameras of mobile phones rather than sensitive sensors.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In practice, the choice of a specific biometric recognition solution depends on sensors, usage scenarios, latency, and power consumption.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The widely used calibration method is proposed by Z. Zhang in [156] based on planar patterns at a few different orientations, where radial lens distortion is modelled.”
What the sources say
- Not checked yetcites [156] [156] Z. Zhang, 'A flexible new technique for camera calibration,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 22, no. 11, pp. 1330-1334, 2000. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When images are captured in the low-light and haze environment, it comes to the more challenging case, i.e., nighttime image dehazing.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Depth estimation, pose estimation, VO/VIO, and SLAM constitute the important 3D perceiving ability of AIoT, which could be very useful in smart transportation [137], smart industry [138], smart agriculture [139], [140], [141], smart cities and homes [142], [143], [144].”
What the sources say
- Not checked yetcites [137] [137] X. Shao, L. Zhang, T. Zhang, Y. Shen, H. Li, and Y. Zhou, 'A tightlycoupled semantic slam system with visual, inertial and surround-view sensors for autonomous indoor parking,' in Proceedings of the 28th ACM International Conference on Multimedia , 2020. - source
- Not checked yetcites [138] [138] R. Krug, T. Stoyanov, V. Tincani, H. Andreasson, R. Mosberger, G. Fantoni, and A. J. Lilienthal, 'The next step in robot commissioning: Autonomous picking and palletizing,' IEEE Robotics and Automation Letters , vol. 1, no. 1, pp. 546-553, 2016. - source
- Not checked yetcites [139] [139] F. A. Cheein, G. Steiner, G. P. Paina, and R. Carelli, 'Optimized eif-slam algorithm for precision agriculture mapping based on stems detection,' Computers and Electronics in Agriculture , vol. 78, no. 2, pp. 195-207, 2011. - source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Images captured in a haze environment are in low contrast due to the haze attenuation and scattering effects.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Haze attenuation and scattering cause low contrast in captured images.
None of the sources cited for this claim have been checked yet.
In the paper
“Recovering the clear image from a single hazy input is also an illposed problem, which can be addressed by both prior-based and learning-based methods [149], [150], [151].”
What the sources say
- Not checked yetcites [149] [149] K. He, J. Sun, and X. Tang, 'Single image haze removal using dark channel prior,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 12, pp. 2341-2353, 2010. - source
- Not checked yetcites [150] [150] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, 'Dehazenet: An end-to-end system for single image haze removal,' IEEE Transactions on Image Processing , vol. 25, no. 11, pp. 5187-5198, 2016. - source
- Not checked yetcites [151] [151] J. Zhang and D. Tao, 'Famed-net: A fast and accurate multi-scale endto-end dehazing network,' IEEE Transactions on Image Processing , vol. 29, pp. 72-84, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Deep learning has advanced this area by learning more representative feature [87], devising better representation of text proposals [88], and using large-scale synthetic dataset [89].”
What the sources say
- Not checked yetcites [87] [87] M. Jaderberg, A. Vedaldi, and A. Zisserman, 'Deep features for text spotting,' in Proceedings of the European Conference on Computer Vision , 2014, pp. 512-528. - source
- Not checked yetcites [88] [88] X. Liu, D. Liang, S. Yan, D. Chen, Y. Qiao, and J. Yan, 'Fots: Fast oriented text spotting with a unified network,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5676-5685. - source
- Not matchedcites [89] [89] A. Gupta, A. Vedaldi, and A. Zisserman, 'Synthetic data for text localisation in natural images,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2315-2324. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Cai et al. propose the first deep CNN model for image dehazing, which outperforms traditional prior-based methods by leveraging the powerful representation capacity of CNNs [150].”
What the sources say
- Not checked yetcites [150] [150] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, 'Dehazenet: An end-to-end system for single image haze removal,' IEEE Transactions on Image Processing , vol. 25, no. 11, pp. 5187-5198, 2016. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Since lowlight images usually contain noises that will be amplified after enhancement, some robust Retinex models have been proposed to account for noise and estimate reflectance, illumination, and noise simultaneously [147], [148].”
What the sources say
- Not checked yetcites [147] [147] M. Li, J. Liu, W. Yang, X. Sun, and Z. Guo, 'Structure-revealing lowlight image enhancement via robust retinex model,' IEEE Transactions on Image Processing , vol. 27, no. 6, pp. 2828-2841, 2018. - source
- Not checked yetcites [148] [148] A. Zhu, L. Zhang, Y. Shen, Y. Ma, S. Zhao, and Y. Zhou, 'Zero-shot restoration of underexposed images via robust retinex decomposition,' - in Proceedings of 2020 IEEE International Conference on Multimedia and Expo . IEEE, 2020, pp. 1-6. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Low-light images usually contain noise that will be amplified after enhancement.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For face recognition, significant progress has been achieved in the last decade, mainly owing to deep representation learning and metric learning.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is a typical ill-posed problem to obtain the reflectance and illumination from a single image.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To address this issue, a representation learning method using triplet loss is proposed to directly learn discriminative and compact face embedding [99].”
What the sources say
- Not checked yetcites [99] [99] F. Schroff, D. Kalenichenko, and J. Philbin, 'Facenet: A unified embedding for face recognition and clustering,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 815-823. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Person re-identification has vast potential for AIoT applications such as smart security in an uncontrolled and non-contact environment, where other biometric recognition techniques are not applicable.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Images captured in a low-light environment are in low visibility and difficult to see details due to insufficient incident light or underexposure.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rectification methods can be categorized as camera calibration-based methods and distortion model-based methods.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the video surveillance scenario, it is necessary to count the crowd in both indoor and outdoor areas and prevent crowd congestion and accident.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Face recognition is one of the most widely used perceiving techniques for identity verification and access control in various AIoT applications, e.g., smart cities and smart homes.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Recently, depth estimation from monocular video together with camera pose estimation has attracted a lot of attention.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Face landmark detectors are usually lightweight and run very fast, which are very useful for latency-sensitive AIoT applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, Liu et al. propose an online camera pose optimization method for the surround view system [158], which is composed of several fisheye cameras around the vehicle.”
What the sources say
- Not checked yetcites [158] [158] X. Liu, L. Zhang, Y. Shen, S. Zhang, and S. Zhao, 'Online camera pose optimization for the surround-view system,' in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 383-391. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Although top-down methods still dominate the leaderboard of public benchmark datasets like MS COCO 10 , they are usually slower than bottom-up methods [112].”
What the sources say
- Not checked yetcites [112] [112] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, 'Realtime multi-person 2d pose estimation using part affinity fields,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7291-7299. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although text detection is related to generic object detection, it is a different and challenging problem: 1) while generic objects have regular shapes, text may be in variable length and shape depending on the number of characters and their orientation; 2) the appearance of same text may change significantly due to fonts, styles as well as background context.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Since it is costly to collect and annotate crowd images, synthetic datasets can be used and have demonstrated its value for this task, i.e., either being used in the pretraining-finetuning scheme or by domain adaptation [128].”
What the sources say
- Not checked yetcites [128] [128] Q. Wang, J. Gao, W. Lin, and Y. Yuan, 'Learning from synthetic data for crowd counting in the wild,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8198-8207. -
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Besides, SLAM can be used for lane departure warning, lanekeeping, high-precision map construction and update [137].”
What the sources say
- Not checked yetcites [137] [137] X. Shao, L. Zhang, T. Zhang, Y. Shen, H. Li, and Y. Zhou, 'A tightlycoupled semantic slam system with visual, inertial and surround-view sensors for autonomous indoor parking,' in Proceedings of the 28th ACM International Conference on Multimedia , 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Given two or more fisheye cameras with calibrated parameters, a panorama image can be obtained from their images by image stitching.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, end-toend modeling of text detection and recognition has achieved impressive performance [90], [91].”
What the sources say
- Not checked yetcites [90] [90] H. Li, P. Wang, and C. Shen, 'Towards end-to-end text spotting with convolutional recurrent neural networks,' in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 5238-5246. - source
- Not checked yetcites [91] [91] J. Liu, Z. Chen, B. Du, and D. Tao, 'Asts: A unified framework for arbitrary shape text spotting,' IEEE Transactions on Image Processing , vol. 29, pp. 5924-5936, 2020. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“VO and VIO are always used ad the front-end in a SLAM system, where the back-end refers to the nonlinear optimization of the pose graph, aiming to obtain globally consistent and drift-free pose estimation results.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Among them, fusing multi-modal data at the feature level is most prevalent, which can be further categorized into three groups, i.e., early fusion [186], late fusion [185], and fusion at multiple levels [85], [182].”
What the sources say
- Not checked yetcites [186] [186] Z. Xu, S. Liu, J. Shi, and C. Lu, 'Outdoor rgbd instance segmentation with residual regretting learning,' IEEE Transactions on Image Processing , vol. 29, pp. 5301-5309, 2020. - source
- Not checked yetcites [185] [185] A. Wang, J. Lu, J. Cai, T.-J. Cham, and G. Wang, 'Large-margin multimodal deep learning for rgb-d object recognition,' IEEE Transactions on Multimedia , vol. 17, no. 11, pp. 1887-1898, 2015. - source
- Not checked yetcites [85] [85] Z. Chen, J. Zhang, and D. Tao, 'Progressive lidar adaptation for road detection,' IEEE/CAA Journal of Automatica Sinica , vol. 6, no. 3, pp. 693-702, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, RNN, especially Long Short-Term Memory (LSTM), is used to model the long-range dependencies in the speech sequence and decode the text sequentially [24].”
What the sources say
- Not checked yetcites [24] [24] H. Sak, A. Senior, and F. Beaufays, 'Long short-term memory recurrent neural network architectures for large scale acoustic modeling,' in Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association , 2014. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Neural machine translation (NMT) based on deep learning has made rapid progress in recent years, outperforming the traditional statistical MT methods or example-based MT methods by leveraging the powerful representation capacity and largescale training data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“for another, RNN processes data in a sequential manner, which is parallel-unfriendly.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Recent studies on cross-media matching and retrieval try to align both domains semantically by leveraging deep learning, especially adversarial learning [177].”
What the sources say
- Not checked yetcites [177] [177] B. Wang, Y. Yang, X. Xu, A. Hanjalic, and H. T. Shen, 'Adversarial cross-modal retrieval,' in Proceedings of the 25th ACM International Conference on Multimedia , 2017, pp. 154-162. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Traditional ASR models are based on hand-crafted features like cepstral coefficient and Hidden Markov Model (HMM) [159], which have been revolutionized by the deep neural network for end-to-end modeling without the need of domain knowledge for feature engineering, HMM design, as well as explicit dependency assumption.”
What the sources say
- Not matchedcites [159] [159] L. Bahl, P. Brown, P. De Souza, and R. Mercer, 'Maximum mutual information estimation of hidden markov model parameters for speech recognition,' in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing , vol. 11, 1986, pp. 49-52. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The surround view system can capture a 360 ◦ view around the vehicle, which is useful in IoV for the advanced driver assistant system and crowd-sourcing high-precision map update.”
What the sources say
- Not checked yetcites [158] [158] X. Liu, L. Zhang, Y. Shen, S. Zhang, and S. Zhao, 'Online camera pose optimization for the surround-view system,' in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 383-391. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“There are three ways of fusing multi-modal data, i.e., at the input level [181], at the feature level [185], [186], [85], [182], [183], and at the output level [184], respectively.”
What the sources say
- Not checked yetcites [181] [181] L. Li, B. Qian, J. Lian, W. Zheng, and Y. Zhou, 'Traffic scene segmentation based on rgb-d image and deep learning,' IEEE Transactions on Intelligent Transportation Systems , vol. 19, no. 5, pp. 1664-1669, 2017. - source
- Not matchedcites [185] [185] A. Wang, J. Lu, J. Cai, T.-J. Cham, and G. Wang, 'Large-margin multimodal deep learning for rgb-d object recognition,' IEEE Transactions on Multimedia , vol. 17, no. 11, pp. 1887-1898, 2015. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [184] [184] J. Liu, Y. Li, S. Song, J. Xing, C. Lan, and W. Zeng, 'Multi-modality multi-task recurrent neural network for online action detection,' IEEE Transactions on Circuits and Systems for Video Technology , vol. 29, no. 9, pp. 2667-2682, 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, ASR can be used in the smart input system [163], automatic transcription system, smart voice assistant [164], [165], computer-assisted speech rehabilitation and language teaching [166].”
What the sources say
- Not checked yetcites [163] [163] F. Longo, L. Nicoletti, and A. Padovano, 'Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises' performance,' Annual Reviews in Control , vol. 47, pp. 221-236, 2019. - source
- Not checked yetcites [164] [164] J. M. Owens, S. B. McLaughlin, and J. Sudweeks, 'On-road comparison of driving performance measures when using handheld and voicecontrol interfaces for mobile phones and portable music players,' SAE International Journal of Passenger Cars-Mechanical Systems , vol. 3, no. 2010-01-1036, pp. 734-743, 2010. - source
- Not checked yetcites [166] [166] K. Angelov, B. Bringert, and A. Ranta, 'Speech-enabled hybrid multilingual translation for mobile devices,' in Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics , 2014, pp. 41-44. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Network compression is an effective technique to improve the efficiency of DNNs for AIoT applications with limited computational budgets.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Later, attention mechanism is used to attend to all source words (i.e., global attention) or only part of them (i.e., local attention) when decoding at each step of RNN [173], [174], [175].”
What the sources say
- Not checked yetcites [173] [173] D. Bahdanau, K. Cho, and Y. Bengio, 'Neural machine translation by jointly learning to align and translate,' in Proceedings of the 3rd International Conference on Learning Representations , 2015. - source
- Not checked yetcites [174] [174] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , 'Google's neural machine translation system: Bridging the gap between human and machine translation,' arXiv preprint arXiv:1609.08144 , 2016. - source
- Not matchedcites [175] [175] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, 'Attention is all you need,' in Advances in Neural Information Processing Systems , 2017, pp. 59986008. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To address the first issue, the connectionist temporal classification is proposed by directly maximizing the probabilities of the correct label sequence in a differentiable way [92].”
What the sources say
- Not checked yetcites [92] [92] A. Graves, S. Fern´ andez, F. Gomez, and J. Schmidhuber, 'Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,' in Proceedings of the 23rd International Conference on Machine Learning , 2006, pp. 369-376. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Speaker recognition is one of the important means for identity identification, which has many applications in various AIoT domains, for example, automatic transcription system for multi-person meetings, personalized recommendation by smart voice assistants [170], and audio forensics [171].”
What the sources say
- Not checked yetcites [170] [170] H. Feng, K. Fawaz, and K. G. Shin, 'Continuous authentication for voice assistants,' in Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking , 2017, pp. 343-355. - source
- Not checked yetcites [171] [171] A. Ross, S. Banerjee, and A. Chowdhury, 'Security in smart cities: A brief review of digital forensic schemes for biometric data,' Pattern Recognition Letters , 2020. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It can facilitate down-stream tasks given the compact coded features as well as support human-in-the-loop inspection and intervention, therefore having vast potential for supporting many AIoT applications.”
What the sources say
- Not checked yetcites [188] [188] L.-Y. Duan, J. Liu, W. Yang, T. Huang, and W. Gao, 'Video coding for machines: A paradigm of collaborative compression and intelligent analytics,' arXiv preprint arXiv:2001.03569 , 2020. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Besides, speaker recognition can be integrated with face recognition for access control.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Recently, unsupervised NMT has also been studied, which could be trained on monolingual corpora.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To mitigate the other issue, the Transformer architecture is devised using scaled dot-product attention and multi-head attention [160].”
What the sources say
- Not checked yetcites [160] [160] L. Dong, S. Xu, and B. Xu, 'Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition,' in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , 2018, pp. 5884-5888. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, a multi-branch group fusion module is proposed to fuse features from RGB and thermal infrared images at different levels in [182], since the semantic information and visual details differ at different levels.”
What the sources say
- Not checked yetcites [182] [182] Q. Zhang, N. Huang, L. Yao, D. Zhang, C. Shan, and J. Han, 'Rgbt salient object detection via fusing multi-level cnn features,' IEEE Transactions on Image Processing , vol. 29, pp. 3321-3335, 2019. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It is noteworthy that a novel idea named video coding for machines is proposed recently [188], which attempts to bridge the gap between feature coding for machine vision and video coding for human vision.”
What the sources say
- Not checked yetcites [188] [188] L.-Y. Duan, J. Liu, W. Yang, T. Huang, and W. Gao, 'Video coding for machines: A paradigm of collaborative compression and intelligent analytics,' arXiv preprint arXiv:2001.03569 , 2020. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Unsupervised representation learning has shown promising performance for many down-stream language tasks by learning context-aware and informative embeddings, e.g., BERT [29].”
What the sources say
- Not checked yetcites [29] [29] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, 'Bert: Pre-training of deep bidirectional transformers for language understanding,' in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2019, pp. 4171-4186. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Previously speaker recognition methods are dominated by the i-vector representation and probabilistic linear discriminant analysis framework [168], where i-vector refers to extracting low-dimensional speaker embeddings from sufficient statistics.”
What the sources say
- Not checked yetcites [168] [168] N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, 'Frontend factor analysis for speaker verification,' IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788-798, 2010. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, several end-to-end deep speaker recognition models have been devised [169], achieving better performance than i-vector baselines.”
What the sources say
- Not checked yetcites [169] [169] J. S. Chung, A. Nagrani, and A. Zisserman, 'Voxceleb2: Deep speaker recognition,' Proc. Interspeech 2018 , pp. 1086-1090, 2018. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Besides, the authors in [85] leverage the residual learning idea to fuse the multilevel RGB image features and Lidar features via a residual structure in a cascaded manner.”
What the sources say
- Not checked yetcites [85] [85] Z. Chen, J. Zhang, and D. Tao, 'Progressive lidar adaptation for road detection,' IEEE/CAA Journal of Automatica Sinica , vol. 6, no. 3, pp. 693-702, 2019. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The computing paradigm could be on-device edge computing (e.g., off-line mode of smart voice assistant), fog computing with a powerful computing device and sound pickup system (e.g., automatic transcription system for conferences), as well as the cloud computing with acceptable latency (e.g., on-line mode of smart voice assistant).”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For example, leveraging BERT as contextual embedding has been proved useful for NMT by borrowing informative context from the pre-trained model [176].”
What the sources say
- Not checked yetcites [176] [176] J. Zhu, Y. Xia, L. Wu, D. He, T. Qin, W. Zhou, H. Li, and T. Liu, 'Incorporating bert into neural machine translation,' in Proceedings of the International Conference on Learning Representations , 2019. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Another research topic that is close to AIoT is multimedia coding, which has also been advanced by deep learning [187].”
What the sources say
- Not checked yetcites [187] [187] D. Liu, Y. Li, J. Lin, H. Li, and F. Wu, 'Deep learning-based video coding: A review and a case study,' ACM Computing Surveys , vol. 53, no. 1, pp. 1-35, 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Attention can be useful for learning context features related to the target and achieve joint alignment and translation, showing better performance for long sentences.”
What the sources say
- Not checked yetcites [173] [173] D. Bahdanau, K. Cho, and Y. Bengio, 'Neural machine translation by jointly learning to align and translate,' in Proceedings of the 3rd International Conference on Learning Representations , 2015. - source
- Not checked yetcites [174] [174] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , 'Google's neural machine translation system: Bridging the gap between human and machine translation,' arXiv preprint arXiv:1609.08144 , 2016. - source
- Not matchedcites [175] [175] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, 'Attention is all you need,' in Advances in Neural Information Processing Systems , 2017, pp. 59986008. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“To address this issue, disentangled representation learning has been proposed [178], which tries to maximize the mutual information between feature embeddings from different modalities and separate modality-exclusive features from them.”
What the sources say
- Not checked yetcites [178] [178] W. Guo, H. Huang, X. Kong, and R. He, 'Learning disentangled representation for cross-modal retrieval with deep mutual information estimation,' in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1712-1720. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for one thing, extra effort is needed to presegment training sequences so that the classification loss can be calculated at each point in the sequence independently”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The prevalent architecture for NMT is the encoder-decoder [172].”
What the sources say
- Not checked yetcites [172] [172] K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, 'On the properties of neural machine translation: Encoder-decoder approaches,' in Proceedings of the 8th Workshop on Syntax, Semantics and Structure in Statistical Translation , 2014. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, real-time ASR systems have been developed either using on-device computing or in a cloud-assisted manner [161], [162].”
What the sources say
- Not checked yetcites [161] [161] Y.-S. Chang, S.-H. Hung, N. J. Wang, and B.-S. Lin, 'Csr: A cloudassisted speech recognition service for personal mobile device,' in Proceedings of the International Conference on Parallel Processing , 2011, pp. 305-314. - source
- Not checked yetcites [162] [162] Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang et al. , 'Streaming end-to-end speech recognition for mobile devices,' in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , 2019, pp. 6381-6385. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“By using them with RGB images as input, cross-modal perceiving has attracted increasing attention in real-world applications, e.g., scene parsing for autonomous driving [181], [85], object detection and tracking in low-light scenarios [182], [183], and action recognition [184].”
What the sources say
- Not checked yetcites [181] [181] L. Li, B. Qian, J. Lian, W. Zheng, and Y. Zhou, 'Traffic scene segmentation based on rgb-d image and deep learning,' IEEE Transactions on Intelligent Transportation Systems , vol. 19, no. 5, pp. 1664-1669, 2017. - source
- Not checked yetcites [182] [182] Q. Zhang, N. Huang, L. Yao, D. Zhang, C. Shan, and J. Han, 'Rgbt salient object detection via fusing multi-level cnn features,' IEEE Transactions on Image Processing , vol. 29, pp. 3321-3335, 2019. - source
- Not checked yetcites [184] [184] J. Liu, Y. Li, S. Song, J. Xing, C. Lan, and W. Zeng, 'Multi-modality multi-task recurrent neural network for online action detection,' IEEE Transactions on Circuits and Systems for Video Technology , vol. 29, no. 9, pp. 2667-2682, 2018. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ASR is very useful in many AIoT applications since speech is one of the most important non-contact interaction modes.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Speech is one of the most important non-contact interaction modes.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It mainly included four kinds of techniques, i.e., network pruning, network quantization, low-rank factorization, and knowledge distillation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A speaker recognition system is composed of three modules, i.e., speech acquisition and production, feature representation and selection, and pattern matching and classification [167].”
What the sources say
- Not checked yetcites [167] [167] J. P. Campbell, 'Speaker recognition: A tutorial,' Proceedings of the IEEE , vol. 85, no. 9, pp. 1437-1462, 1997. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, FL can be used to improve the perceiving and learning ability of AIoT devices such as connected vehicles for autonomous driving [199] and wearable devices for health monitoring [222], [197].”
What the sources say
- Not checked yetcites [199] [199] X. Liang, Y. Liu, T. Chen, M. Liu, and Q. Yang, 'Federated transfer reinforcement learning for autonomous driving,' arXiv preprint arXiv:1910.06001 , 2019. - source
- Not checked yetcites [222] [222] T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, and W. Shi, 'Federated learning of predictive models from federated electronic health records,' International Journal of Medical Informatics , vol. 112, pp. 59-67, 2018. - source
- Not checked yetcites [197] [197] A. Saeed, F. D. Salim, T. Ozcelebi, and J. Lukkien, 'Federated self-supervised learning of multi-sensor representations for embedded intelligence,' IEEE Internet of Things Journal , 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Domain adaptation has been used in many computer vision tasks including classification, object detection, and especially semantic segmentation [37], where obtaining the dense pixellevel annotations in the target domain is costly and timeconsuming.”
What the sources say
- Not checked yetcites [37] [37] Q. Zhang, J. Zhang, W. Liu, and D. Tao, 'Category anchor-guided unsupervised domain adaptation for semantic segmentation,' in Advances in Neural Information Processing Systems , 2019, pp. 435-445. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The architecture of FL usually consists of a central server (or collaborator) and many distributed client devices.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most NAS methods fall into three categories, i.e., evolutionary methods, reinforcement learning-based methods, and gradient-based methods.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, it has been almost the de facto practice to fine-tune the models pre-trained on ImageNet in different down-stream tasks, e.g., object detection and semantic segmentation, for faster convergence and better generalization.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Recently, a mobile domain adaptation framework is proposed for edge computing in AIoT [38] by knowledge distillation from the teacher model on the server to the student model on the edge device.”
What the sources say
- Not checked yetcites [38] [38] J. Yang, H. Zou, S. Cao, Z. Chen, and L. Xie, 'Mobileda: Towards edge domain adaptation,' IEEE Internet of Things Journal , 2020. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Network quantization compresses the original network by reducing the number of bits required for each weight, which significantly reduces memory use and float point operations with a slight loss of accuracy.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DNN has a strong representation capacity for learning compact and discriminative feature representation from the high-dimensional image and video data, which enables RL to deal with previously intractable problems by learning better policy and value function.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Federated learning was initially proposed to address the learning problem in which datasets are distributed across multiple devices and not allowed to be leaked to others [220].”
What the sources say
- Not checked yetcites [220] [220] J. Koneˇ cn` y, H. B. McMahan, F. X. Yu, P. Richt´ arik, A. T. Suresh, and D. Bacon, 'Federated learning: Strategies for improving communication efficiency,' arXiv preprint arXiv:1610.05492 , 2016. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Another hot research sub-area is self-supervised learning, which learns discriminative visual representation by solving predefined pretext tasks [194].”
What the sources say
- Not checked yetcites [194] [194] L. Jing and Y. Tian, 'Self-supervised visual feature learning with deep neural networks: A survey,' IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By contrast, gradient-based methods adopt a continuous relaxation of the architecture representation. Therefore, the optimization of neural architecture can be conducted in a continuous space using gradient descent, which is orders of magnitude faster.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“FL offers a general learning framework to use massive distributed and isolated data while preserving data security and privacy, which is particularly appealing to AIoT applications in the context of edge computing [221], [197].”
What the sources say
- Not checked yetcites [221] [221] Q. Wu, K. He, and X. Chen, 'Personalized federated learning for intelligent iot applications: A cloud-edge based framework,' IEEE Computer Graphics and Applications , 2020. - source
- Not checked yetcites [197] [197] A. Saeed, F. D. Salim, T. Ozcelebi, and J. Lukkien, 'Federated self-supervised learning of multi-sensor representations for embedded intelligence,' IEEE Internet of Things Journal , 2020. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“There are two main groups of DRL methods including deep Q-network [210] and policy gradient-based methods such as asynchronous advantage actor-critic [211].”
What the sources say
- Not checked yetcites [210] [210] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , 'Human-level control through deep reinforcement learning,' Nature , vol. 518, no. 7540, pp. 529-533, 2015. - source
- Not matchedcites [211] [211] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, 'Asynchronous methods for deep reinforcement learning,' in Proceedings of the International Conference on Machine Learning , 2016, pp. 1928-1937. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that different from the aforementioned semi-supervised learning where labeled and unlabeled data are usually drawn from the same distribution, transfer learning does not require the data distributions of the source and the target domains to be identical.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In a recent study [36], a computational taxonomic map is discovered for transfer learning between twenty-six visual tasks, providing valuable empirical insights, e.g., what tasks transfer well to other target tasks, and how to reuse supervision among related tasks to reduce the demand for labeled data while achieving same performance.”
What the sources say
- Not checked yetcites [36] [36] A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik, and S. Savarese, 'Taskonomy: Disentangling task transfer learning,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3712-3722. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Network pruning can be carried out at different levels of granularity, e.g., weight pruning, neuron pruning, filter pruning, and channel pruning based on the magnitude of weights or responses calculated by L 1 /L 2 norm [189].”
What the sources say
- Not checked yetcites [189] [189] Y. He, X. Zhang, and J. Sun, 'Channel pruning for accelerating very deep neural networks,' in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1389-1397. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, rapid progress has been made by incorporating deep learning in RL (i.e., DRL) [209].”
What the sources say
- Not checked yetcites [209] [209] K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, 'Deep reinforcement learning: A brief survey,' IEEE Signal Processing Magazine , vol. 34, no. 6, pp. 26-38, 2017. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Recently, various GAN models have been proposed which can generate high-resolution and visually realistic images from random vectors.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While actor-critic based methods directly optimize the cumulative reward, a surrogate objective function can be used to address the distribution shift problem in policy gradient based RL methods.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thereby, empowering things with learning ability is important for AIoT so that it can update and evolve in response to the variations.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“DRL can empower things with the ability to interact with and adapt to the dynamic world, making it useful in many AIoT applications (Figure 9), such as autonomous driving in smart transportation [203], [199], 3D-landmark detection of CT scans [212] and robot control [213] in smart healthcare, course recommendation in smart education [41], real-time scheduling for smart factory [214], load”
What the sources say
- Not checked yetcites [203] [203] X. Pan, Y. You, Z. Wang, and C. Lu, 'Virtual to real reinforcement learning for autonomous driving,' arXiv preprint arXiv:1704.03952 , 2017. - source
- Not matchedcites [212] [212] F.-C. Ghesu, B. Georgescu, Y. Zheng, S. Grbic, A. Maier, J. Hornegger, and D. Comaniciu, 'Multi-scale deep reinforcement learning for realtime 3d-landmark detection in ct scans,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 1, pp. 176-189, 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [41] [41] J. Zhang, B. Hao, B. Chen, C. Li, H. Chen, and J. Sun, 'Hierarchical reinforcement learning for course recommendation in moocs,' in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 435-442. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In real-world AIoT systems, there are always many related tasks involved, e.g., object detection and tracking, and semantic segmentation in video surveillance. Therefore, finding the transfer learning dependencies across these tasks and leveraging such prior knowledge to learn better models, are of practical value for AIoT [199], [200], [201], [202].”
What the sources say
- Not checked yetcites [199] [199] X. Liang, Y. Liu, T. Chen, M. Liu, and Q. Yang, 'Federated transfer reinforcement learning for autonomous driving,' arXiv preprint arXiv:1910.06001 , 2019. - source
- Not checked yetcites [200] [200] M. D'Incecco, S. Squartini, and M. Zhong, 'Transfer learning for nonintrusive load monitoring,' IEEE Transactions on Smart Grid , vol. 11, no. 2, pp. 1419-1429, 2019. - source
- Not checked yetcites [201] [201] S. Bargoti and J. Underwood, 'Deep fruit detection in orchards,' in Proceedings of the IEEE International Conference on Robotics and Automation , 2017, pp. 3626-3633. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: In real-world AIoT systems, there are always many related tasks involved.
In the paper
“Therefore, ZSL can be formulated as learning a mapping from the data space to the semantic space [32].”
What the sources say
- Not checked yetcites [32] [32] W. Wang, V. W. Zheng, H. Yu, and C. Miao, 'A survey of zero-shot learning: Settings, methods, and applications,' ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 1-37, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other cases, classes may change over time or new classes may emerge, e.g., electric bicycle and tricar, where ZSL can be leveraged to adapt to the variations.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For example, the recent BigBiGAN model can learn discriminative visual representation with good transferring performance on down-stream tasks, by devising an encoder to learn an inverse mapping from data to the latent space [193].”
What the sources say
- Not checked yetcites [193] [193] J. Donahue and K. Simonyan, 'Large scale adversarial representation learning,' in Advances in Neural Information Processing Systems , 2019, pp. 10 542-10 552. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In real-world AIoT applications, there are always some rare cases that need to be recognized by AI models, e.g., a car collision, cyber attack, machine fault. However, the collection and annotation of large-scale such cases are usually very difficult. Thereby, FSL can be used to learn suitable models in these scenarios [207].”
What the sources say
- Not checked yetcites [207] [207] N. Passalis, A. Iosifidis, M. Gabbouj, and A. Tefas, 'Hyperspherebased weight imprinting for few-shot learning on embedded devices,' IEEE Transactions on Neural Networks and Learning Systems , 2020. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: In real-world AIoT applications, there are always some rare cases that need to be recognized by AI models.
- Rests on an assumption: Collection and annotation of large-scale rare cases are usually very difficult.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accordingly, the models are expected to have learned a high-level understanding of the semantics of training data.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, a mixed-precision model quantization method has been proposed by leveraging the power of NAS [190], where different bit-widths are assigned to different layers/channels.”
What the sources say
- Not checked yetcites [190] [190] Z. Cai and N. Vasconcelos, 'Rethinking differentiable search for mixed-precision neural networks,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 23492358. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Visual domain adaptation methods try to learn domain-invariant representations by matching the distributions between source and target domains at the appearance level, feature level, or output level, thereby reducing the domain shift.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“ZSL can be useful in some AIoT application scenarios. For example, in fault diagnosis [208], some specific types of faults may occur in the future and should be recognized, but no training instances belonging to them have ever been collected previously.”
What the sources say
- Not checked yetcites [208] [208] L. Feng and C. Zhao, 'Fault description based attribute transfer for zero-sample industrial fault diagnosis,' IEEE Transactions on Industrial Informatics , 2020. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, an iterative training scheme is proposed in [35], where the trained student model is used as the teacher model at the subsequent training round.”
What the sources say
- Not checked yetcites [35] [35] Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le, 'Self-training with noisy student improves imagenet classification,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 687-10 698. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Instead of manually designing the network, NAS aims to automatically search the architecture from a predefined search space [192].”
What the sources say
- Not checked yetcites [192] [192] T. Elsken, J. H. Metzen, and F. Hutter, 'Neural architecture search: A survey,' Journal of Machine Learning Research , vol. 20, pp. 1-21, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One classical way of reasoning causality is via randomized controlled trial by evaluating the outcomes from the treatment group and control group, which is however costly and time-consuming.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: One classical way of reasoning causality via randomized controlled trial is costly and time-consuming.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some well-known KGs such as WordNet, Freebase, YAGO, NELL have been constructed and used in many applications via knowledge reasoning.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, a lightweight solution is proposed in [241] by block-based logic transformation, which not only reduces feature size but also preserves the original feature, therefore appealing to resource-limited AIoT devices and resistant to potential attacks.”
What the sources say
- Not checked yetcites [241] [241] W. Yang, S. Wang, G. Zheng, J. Yang, and C. Valli, 'A privacypreserving lightweight biometric system for internet of things security,' IEEE Communications Magazine , vol. 57, no. 3, pp. 84-89, 2019. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, to capture salient and high-resolution humans and vehicles, active cameras can be used in AIoT systems by adjusting the orientation and focal length according to target location [234], [235].”
What the sources say
- Not checked yetcites [234] [234] J. Denzler, M. Zobel, and H. Niemann, 'Information theoretic focal length selection for real-time active 3-d object tracking,' in Proceedings of the IEEE International Conference on Computer Vision , vol. 1, 2003, pp. 400-400. - source
- Not checked yetcites [235] [235] E. Sommerlade and I. Reid, 'Information-theoretic active scene exploration,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2008, pp. 1-7. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multi-robot systems have been used in many AIoT applications such as smart logistics and precision agriculture, where decentralized control methods and coordination strategies are proposed for achieving and maintaining formations.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, there are two kinds of control in autonomous driving systems, i.e., lateral control (steering) for autonomic turning and lane-keeping, and longitudinal control (brake and throttle) for autonomic braking and forward collision avoidance.”
plainn = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Empowering AIoT with such reasoning abilities is important for making smart and explainable decisions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Monitoring dangerous driver behaviors is crucial for preventing traffic accidents.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Recently, deep reinforcement learning is leveraged to learn driving policy directly from visual input (e.g., front-view images).”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among them, the self-driving car is a typical example empowered by AI, which integrates various perceiving, learning, reasoning, and behaving abilities together.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The study in [164] shows that voice control allows drivers to keep watching out-car environment and demands lower mental load. By contrast, handheld controls may distract driver's attention and require higher mental demand, probably leading to dangerous driving behaviors.”
What the sources say
- Not checked yetcites [164] [164] J. M. Owens, S. B. McLaughlin, and J. Sudweeks, 'On-road comparison of driving performance measures when using handheld and voicecontrol interfaces for mobile phones and portable music players,' SAE International Journal of Passenger Cars-Mechanical Systems , vol. 3, no. 2010-01-1036, pp. 734-743, 2010. - source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In the public safety or traffic video surveillance scenarios, the active cameras can change its orientation and focal length by pan-tilt-zoom control to track and focus on specific targets [234], [235].”
What the sources say
- Not matchedcites [234] [234] J. Denzler, M. Zobel, and H. Niemann, 'Information theoretic focal length selection for real-time active 3-d object tracking,' in Proceedings of the IEEE International Conference on Computer Vision , vol. 1, 2003, pp. 400-400. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [235] [235] E. Sommerlade and I. Reid, 'Information-theoretic active scene exploration,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2008, pp. 1-7. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Knowledge reasoning is useful for many down-stream tasks which can be categorized into in-KG and out-of-KG.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We humans have the ability of causal reasoning such as causal inference and causal discovery.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: We humans have the ability of causal reasoning such as causal inference and causal discovery.
In the paper
“Moreover, the research in [231] shows that identifying the causal factors of driving fatigue can be used for triggering corresponding countermeasures.”
What the sources say
- Not checked yetcites [231] [231] J. F. May and C. L. Baldwin, 'Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies,' Transportation Research Part F: Traffic Psychology and Behaviour , vol. 12, no. 3, pp. 218-224, 2009. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, sleep-related fatigue is resistant to most interventions while task-related fatigue (e.g., distraction) can be counteracted effectively.”
What the sources say
- Not checked yetcites [231] [231] J. F. May and C. L. Baldwin, 'Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies,' Transportation Research Part F: Traffic Psychology and Behaviour , vol. 12, no. 3, pp. 218-224, 2009. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIoT can also enable in-car driver monitoring and interactions with the infotainment system.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They are crucial for building explainable AIoT systems based DNNs, which have been widely used in many perceiving tasks as described in Section III-A.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As reviewed in Section III, the progress of AI shows a great potential to empower the connected things in AIoT systems with the ability for perceiving, learning, reasoning, and behaving.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Smart transportation enabled by AIoT covers traffic participants (e.g., smart internet of vehicles [256]), traffic infrastructures [257], and industry applications (e.g., smart connected logistics [258]).”
What the sources say
- Not checked yetcites [256] [256] H. Jiang, Z. Zhang, J. Dang, and L. Wu, 'A novel 3-d massive mimo channel model for vehicle-to-vehicle communication environments,' IEEE Transactions on Communications , vol. 66, no. 1, pp. 79-90, 2017. - source
- Not checked yetcites [257] [257] D. Jiang, L. Huo, Z. Lv, H. Song, and W. Qin, 'A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking,' IEEE Transactions on Intelligent Transportation Systems , vol. 19, no. 10, pp. 3305-3319, 2018. source
- Not checked yetcites [258] [258] C. Lee, Y. Lv, K. Ng, W. Ho, and K. Choy, 'Design and application of internet of things-based warehouse management system for smart logistics,' International Journal of Production Research , vol. 56, no. 8, pp. 2753-2768, 2018. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To address it, Zhang et al. propose a style translation-based method for cross-domain person re-identification in camera sensor networks [206], which can reduce domain shift and learn domain-invariant”
What the sources say
- Not checked yetcites [206] [206] Z. Zhang, Y. Wang, and S. Liu, 'Cross-domain person re-identification using dual generation learning in camera sensor networks,' Ad Hoc Networks , vol. 97, p. 102019, 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that one crucial feature of knowledge reasoning is explainable, which has the potential to be incorporated with deep learning and mitigate the interpretability issue in AIoT applications.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting AIoT systems will have a huge impact on the economic sectors and our living environments, such as security, transportation, healthcare, education, industry, energy, agriculture, as well as our homes and cities.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, carrying out the training in the real-world is unaffordable.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“InKG applications include graph refinement (e.g., completion and error detection), triple/entity classification, and entity resolution. Out-of-KG applications include relation extraction, question answering, and recommendation, which are related to specific AIoT scenarios, such as network forensics analysis in smart security [224], smart assistants in smart healthcare and agriculture [39], [225],”
What the sources say
- Not checked yetcites [224] [224] W. Wang and T. E. Daniels, 'Building evidence graphs for network forensics analysis,' in Proceedings of the 21st Annual Computer Security Applications Conference , 2005, pp. 11-pp. - source
- Not checked yetcites [39] [39] Y. Zhang, S. Qian, Q. Fang, and C. Xu, 'Multi-modal knowledgeaware hierarchical attention network for explainable medical question answering,' in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1089-1097. - source
- Not checked yetcites [225] [225] Y. Chen, J. Kuang, D. Cheng, J. Zheng, M. Gao, and A. Zhou, 'Agrikg: an agricultural knowledge graph and its applications,' in Proceedings of the International Conference on Database Systems for Advanced Applications , 2019, pp. 533-537. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, deep reinforcement learning has been used for autonomous robot control, e.g., self-driving vehicle [203], [199], medical robot [213], mobile service robot.”
What the sources say
- Not checked yetcites [203] [203] X. Pan, Y. You, Z. Wang, and C. Lu, 'Virtual to real reinforcement learning for autonomous driving,' arXiv preprint arXiv:1704.03952 , 2017. - source
- Not checked yetcites [199] [199] X. Liang, Y. Liu, T. Chen, M. Liu, and Q. Yang, 'Federated transfer reinforcement learning for autonomous driving,' arXiv preprint arXiv:1910.06001 , 2019. - source
- Not checked yetcites [213] [213] B. Thananjeyan, A. Garg, S. Krishnan, C. Chen, L. Miller, and K. Goldberg, 'Multilateral surgical pattern cutting in 2d orthotropic gauze with deep reinforcement learning policies for tensioning,' in Proceedings of the IEEE International Conference on Robotics and Automation , 2017, pp. 2371-2378. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, learning causality from observational data has attracted much attention [229].”
What the sources say
- Not checked yetcites [229] [229] R. Guo, L. Cheng, J. Li, P. R. Hahn, and H. Liu, 'A survey of learning causality with data: Problems and methods,' ACM Computing Surveys , vol. 53, no. 4, pp. 1-37, 2020. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The goal of smart security is to ensure the security of our physical world and cyberspace, which can be achieved with the help of various AIoT systems. One of the most important features of them is human-centric perceiving , which can recognize the identities of individuals and analyze their behaviors to prevent illegal activities.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Beyond the aforementioned techniques for individual analysis, estimating the crowd density and monitoring the crowd flow is also very important for public safety [128], e.g., avoid deadly accidents of trampling and crushing of pedestrians.”
What the sources say
- Not checked yetcites [128] [128] Q. Wang, J. Gao, W. Lin, and Y. Yuan, 'Learning from synthetic data for crowd counting in the wild,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8198-8207. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In our real-world, there is much knowledge carried by web information, medical records, financial transactions, etc., which can be used to reason the answer to a question or infer patient cohorts.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: In our real-world, there is much knowledge carried by web information, medical records, financial transactions, etc., which can be used to reason.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The behaving ability is also very important for AIoT systems passively responding to environment variations and raised requests, or actively exploring the unknown.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The behaving ability is important for AIoT systems.
None of the sources cited for this claim have been checked yet.
In the paper
“The self-driving system should perceive the driving environment, such as detecting road [85], traffic-sign [259], pedestrian [260], and car [261], estimating the intention of cars and pedestrians and predicting their trajectories [244], [245]. Besides, it should also measure the pose and location of landmarks (e.g., trafficsigns) for SLAM [137].”
What the sources say
- Not checked yetcites [85] [85] Z. Chen, J. Zhang, and D. Tao, 'Progressive lidar adaptation for road detection,' IEEE/CAA Journal of Automatica Sinica , vol. 6, no. 3, pp. 693-702, 2019. - source
- Not checked yetcites [259] [259] Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, 'Traffic-sign detection and classification in the wild,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2110-2118. source
- Not matchedcites [260] [260] Z. Chen, W. Ouyang, T. Liu, and D. Tao, 'A shape transformationbased dataset augmentation framework for pedestrian detection,' arXiv preprint arXiv:1912.07010 , 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Recently, knowledge graph embedding based methods have attracted significant attention, aiming to embed the entities and relations in a KG into continuous vector spaces such that the reasoning can be done by leveraging translational distance models and semantic matching models [223].”
What the sources say
- Not checked yetcites [223] [223] Q. Wang, Z. Mao, B. Wang, and L. Guo, 'Knowledge graph embedding: A survey of approaches and applications,' IEEE Transactions on Knowledge and Data Engineering , vol. 29, no. 12, pp. 2724-2743, 2017. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The control algorithms are task-oriented and specifically designed in different AIoT systems regarding their physical structures and sensors.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Different representation learning, multi-task learning, and meta-learning methods have been proposed for causal inference under the potential outcome framework.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“In [227], a knowledge graph-based digital twin model is introduced which is composed of four parts, i.e., feature extraction, ontology creation, knowledge graph generation, and semantic relation extraction. It can extract and infer knowledge from large scale production line data and enhance manufacturing process management via semantic relation reasoning.”
What the sources say
- Not checked yetcites [227] [227] A. Banerjee, R. Dalal, S. Mittal, and K. P. Joshi, 'Generating digital twin models using knowledge graphs for industrial production lines,' UMBC Information Systems Department , 2017. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Recently, the concept of precision agriculture becomes popular, referring to observing, measuring, and responding to crop variability through sensors, autonomous agricultural machines, and geographic information systems, which can be achieved by AIoT in smart agriculture.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, a convolutional sparse autoencoder-based unsupervised learning method has been proposed for power transmission line fault diagnosis based on voltage and current signals [250]. It can detect faults within 7 ms latency, which is appealing to real-world applications.”
What the sources say
- Not checked yetcites [250] [250] K. Chen, J. Hu, and J. He, 'Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder,' IEEE Transactions on Smart Grid , vol. 9, no. 3, pp. 1748-1758, 2016. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Furthermore, to preserve the privacy of the households while intelligently managing their load scheduling, a distributed deep reinforcement learning method is proposed inspired by the idea of federated learning [216], where the action networks are located at distributed households and the critic network is located at an aggregator from a trusted third party.”
What the sources say
- Not checked yetcites [216] [216] H.-M. Chung, S. Maharjan, Y. Zhang, and F. Eliassen, 'Distributed deep reinforcement learning for intelligent load scheduling in residential smart grid,' IEEE Transactions on Industrial Informatics , 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Recently, a semi-supervised deep learning method is proposed based on autoencoder and generative adversarial networks, which can effectively detect false data injection attacks in smart grids.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, crop counting and yield estimation is an important topic of precision agriculture [202], [141].”
What the sources say
- Not checked yetcites [202] [202] S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor, and V. Kumar, 'Counting apples and oranges with deep learning: A data-driven approach,' IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 781-788, 2017. - source
- Not checked yetcites [141] [141] X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das, and V. Kumar, 'Robust fruit counting: Combining deep learning, tracking, and structure from motion,' in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 1045-1052. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, Bousmalis et al. propose a deep robotic grasping model named GraspGAN [204], which bridges the domain gap between synthetic images and real-world ones via the pixel-level image translation and a feature-level domain classifier.”
What the sources say
- Not checked yetcites [204] [204] K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige et al. , 'Using simulation and domain adaptation to improve efficiency of deep robotic grasping,' in Proceedings of the IEEE International Conference on Robotics and Automation , 2018, pp. 4243-4250. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI technologies can empower AIoT things to help children and students recognize new species, learn native or foreign languages, choose personalized learning resources, and help those with visual impairments learn through interactions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, industrial robots have been widely used in the production line of smart factories, e.g., for drilling and grasping.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Recently, online healthcare services or medical assistant robots can offer convenient information-query and auxiliary diagnosis services.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“To increase the safety, speed, and accuracy of autonomous picking and palletizing, Krug et al. propose a novel grasp representation scheme allowing redundancy in the gripper pose placement [138].”
What the sources say
- Not checked yetcites [138] [138] R. Krug, T. Stoyanov, V. Tincani, H. Andreasson, R. Mosberger, G. Fantoni, and A. J. Lilienthal, 'The next step in robot commissioning: Autonomous picking and palletizing,' IEEE Robotics and Automation Letters , vol. 1, no. 1, pp. 546-553, 2016. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, unmanned aerial vehicles (UAVs) connected to the control center via the cellular network are used for damage classification and estimation of power distribution poles [248].”
What the sources say
- Not checked yetcites [248] [248] M. M. Hosseini, A. Umunnakwe, M. Parvania, and T. Tasdizen, 'Intelligent damage classification and estimation in power distribution poles using unmanned aerial vehicles and convolutional neural networks,' IEEE Transactions on Smart Grid , 2020.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A speech-to-speech multilingual translation system on mobile devices are proposed in [166], which includes three modules, i.e., speech recognition, language translation, and text-to-speech synthesis. It can work in offline mode and offers grammatical information that is useful for language learners.”
What the sources say
- Not matchedcites [166] [166] K. Angelov, B. Bringert, and A. Ranta, 'Speech-enabled hybrid multilingual translation for mobile devices,' in Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics , 2014, pp. 41-44. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, it is important to predict and diagnose machine faults from sensor data to reduce PCB defects, thereby increasing production efficiency and reducing losses.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“UVAs are used for capturing images of crops and fruits and sending them to the cloud for further counting [202].”
What the sources say
- Not checked yetcites [202] [202] S. W. Chen, S. S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C. J. Taylor, and V. Kumar, 'Counting apples and oranges with deep learning: A data-driven approach,' IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 781-788, 2017. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Digital Twin, i.e., a mirror digital representation of a physical system, has demonstrated great value for smart factories in Industry 4.0, e.g., monitoring the manufacturing process, diagnosing the fault, and preventing downtime.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Besides, some 'industrial stethoscopes' are designed to recognize and localize fault sound sources in visual scenes via cameras and multiple microphones, where the algorithm can run on edge devices for real-time monitoring.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For monitoring, both wearable devices with motion sensors [262] and cameras [263] can be used for human activity recognition.”
What the sources say
- Not checked yetcites [262] [262] T. Q. Trung and N.-E. Lee, 'Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoring and personal healthcare,' Advanced materials , vol. 28, no. 22, pp. 4338-4372, 2016. - source
- Not checked yetcites [263] [263] X. Yang and Y. Tian, 'Super normal vector for human activity recognition with depth cameras,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 5, pp. 1028-1039, 2016. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For examination, deep learning has been used for medical image understanding such as 3Dlandmark detection in CT scans [212] and semantic segmentation [75].”
What the sources say
- Not matchedcites [212] [212] F.-C. Ghesu, B. Georgescu, Y. Zheng, S. Grbic, A. Maier, J. Hornegger, and D. Comaniciu, 'Multi-scale deep reinforcement learning for realtime 3d-landmark detection in ct scans,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 1, pp. 176-189, 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [75] [75] O. Ronneberger, P. Fischer, and T. Brox, 'U-net: Convolutional networks for biomedical image segmentation,' in Proceedings of the International Conference on Medical Image Computing and ComputerAssisted Intervention , 2015, pp. 234-241. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Recently, deep learning-based methods have been proposed for automatic real-time visual defect inspection [264].”
What the sources say
- Not checked yetcites [264] [264] Y. Gao, J. Lin, J. Xie, and Z. Ning, 'A real-time defect detection method for digital signal processing of industrial inspection applications,' IEEE Transactions on Industrial Informatics , 2020. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Besides, since it is difficult to collect and annotate edge samples in the industrial context, zero-/few-shot learning is also worth further study.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, causal analysis of the product defects based on data and knowledge is also of practical importance.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIoT can be a critical part of implementing digital twins where the connected sensors and actuators can collect real-time data from production lines and send them to the digital twin running in the cloud (Figure 12).”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a complement of on-campus learning, online learning via Massive Open Online Course (MOOC) platforms (e.g., Coursera) has become very popular.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Recently, a service-oriented digital twin model is proposed in [163], which uses an ontology-oriented knowledge structure to represent the knowledge about the manufacturing system from the sensing data. It also designs a vocal interaction system for knowledge retrieval based on speech recognition and text-tospeech synthesis.”
What the sources say
- Not checked yetcites [163] [163] F. Longo, L. Nicoletti, and A. Padovano, 'Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises' performance,' Annual Reviews in Control , vol. 47, pp. 221-236, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For example, multi-sensory interactive maps are designed [247], which can help children to acquire skills via interactions, e.g., touching, listening, tasting, and scenting.”
What the sources say
- Not checked yetcites [247] [247] E. Brule, G. Bailly, A. Brock, F. Valentin, G. Denis, and C. Jouffrais, 'Mapsense: multi-sensory interactive maps for children living with visual impairments,' in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems , 2016, pp. 445-457.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For crop management and pest/disease control, agricultural knowledge retrieval and answering system will be helpful.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, AI technologies can enable an intelligent analysis of data and help to make smart decisions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The path planning [140] and self-localization and navigation [139] abilities of UAVs are critical to complete the above tasks, which have been studied in the agricultural scenario.”
What the sources say
- Not checked yetcites [140] [140] B. H. Y. Alsalam, K. Morton, D. Campbell, and F. Gonzalez, 'Autonomous uav with vision based on-board decision making for remote sensing and precision agriculture,' in Proceedings of the IEEE Aerospace Conference , 2017, pp. 1-12. - source
- Not checked yetcites [139] [139] F. A. Cheein, G. Steiner, G. P. Paina, and R. Carelli, 'Optimized eif-slam algorithm for precision agriculture mapping based on stems detection,' Computers and Electronics in Agriculture , vol. 78, no. 2, pp. 195-207, 2011. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“To address this issue, a detection-tracking-counting based method is proposed [141], which can reject outliers and double-counted fruits.”
What the sources say
- Not checked yetcites [141] [141] X. Liu, S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, C. Qu, C. J. Taylor, J. Das, and V. Kumar, 'Robust fruit counting: Combining deep learning, tracking, and structure from motion,' in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 1045-1052. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A typical example of AIoT application in the smart industry is the Printed Circuit Board (PCB) manufacturing. There are three scenarios that are related to AIoT systems with different sensors and devices, i.e., manufacturing, visual defect inspection, and machine fault diagnosis.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, PCB defect inspection carried out by workers manually is laborious and time-consuming.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIoT in the smart grids can be used for grid fault diagnosis, load monitoring and scheduling, and cyber-attack detection.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“To this end, an agricultural knowledge graph, namely AgriKG [225], is automatically constructed by leveraging NLP and deep learning techniques.”
What the sources say
- Not checked yetcites [225] [225] Y. Chen, J. Kuang, D. Cheng, J. Zheng, M. Gao, and A. Zhou, 'Agrikg: an agricultural knowledge graph and its applications,' in Proceedings of the International Conference on Database Systems for Advanced Applications , 2019, pp. 533-537. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, a weight imprinting-based few-shot learning method is proposed in [207], which can be used in edge devices to recognize new bird and animal species with only a few labeled samples.”
What the sources say
- Not checked yetcites [207] [207] N. Passalis, A. Iosifidis, M. Gabbouj, and A. Tefas, 'Hyperspherebased weight imprinting for few-shot learning on embedded devices,' IEEE Transactions on Neural Networks and Learning Systems , 2020. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Recently, a two-stream network with an attention mechanism is proposed to directly localize a sound source in images [249].”
What the sources say
- Not checked yetcites [249] [249] A. Senocak, T.-H. Oh, J. Kim, M.-H. Yang, and I. So Kweon, 'Learning to localize sound source in visual scenes,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4358-4366.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For plant grow control, an AIoT system is set up in a tomato greenhouse [217], which is composed of a wireless sensor network and cloud computing center empowered by AI technologies. Deep reinforcement learning is used for obtaining the optimal control policy on the illumination, nutrition, and ventilation conditions in the greenhouse.”
What the sources say
- Not checked yetcites [217] [217] A. Somov, D. Shadrin, I. Fastovets, A. Nikitin, S. Matveev, O. Hrinchuk et al. , 'Pervasive agriculture: Iot-enabled greenhouse for plant growth control,' IEEE Pervasive Computing , vol. 17, no. 4, pp. 65-75, 2018. - source
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For load monitoring and electric vehicle charging scheduling, transfer learning [200] and deep reinforcement learning [215] methods are proposed.”
What the sources say
- Not checked yetcites [200] [200] M. D'Incecco, S. Squartini, and M. Zhong, 'Transfer learning for nonintrusive load monitoring,' IEEE Transactions on Smart Grid , vol. 11, no. 2, pp. 1419-1429, 2019. - source
- Not checked yetcites [215] [215] Z. Wan, H. Li, H. He, and D. Prokhorov, 'Model-free real-time ev charging scheduling based on deep reinforcement learning,' IEEE Transactions on Smart Grid , vol. 10, no. 5, pp. 5246-5257, 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIoT systems for smart healthcare cover several phases including monitoring, examination, surgery, and rehabilitation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, reasoning based on knowledge graphs and causal analysis is a challenging but active research area, having the potential to enable AIoT systems to approach human-level cognitive intelligence.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deep learning has rapidly progressed in many perceiving areas and enables many AIoT applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, the gender of speakers, sound types, loudness, sound directions are displayed as indicator icons [252], specifically designed for the hearing impaired;”
What the sources say
- Not checked yetcites [252] [252] D. Jain, L. Findlater, J. Gilkeson, B. Holland, R. Duraiswami, D. Zotkin, C. Vogler, and J. E. Froehlich, 'Head-mounted display visualizations to support sound awareness for the deaf and hard of hearing,' in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems , 2015, pp. 241-250. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the context of unsupervised learning and other machine learning topics like reinforcement learning, deep learning has attracted an increasing amount of attention and is useful for further improving the intelligence of AIoT systems to handle dynamic and complex environments.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“3) visual-navigation systems for the visually impaired [142], [143], [144], share some basic features like localization, obstacle recognition, path planning of SLAM for autonomous driving in smart transportation and smart agriculture (Section IV-B and Section IV-G), but also have some differences.”
What the sources say
- Not checked yetcites [142] [142] Y. H. Lee and G. Medioni, 'Rgb-d camera based wearable navigation system for the visually impaired,' Computer Vision and Image Understanding , vol. 149, pp. 3-20, 2016. - source
- Not checked yetcites [143] [143] H.-C. Wang, R. K. Katzschmann, S. Teng, B. Araki, L. Giarr´ e, and D. Rus, 'Enabling independent navigation for visually impaired people through a wearable vision-based feedback system,' in Proceedings of the IEEE International Conference on Robotics and Automation , 2017, pp. 6533-6540. - source
- Not checked yetcites [144] [144] R. K. Katzschmann, B. Araki, and D. Rus, 'Safe local navigation for visually impaired users with a time-of-flight and haptic feedback device,' IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 26, no. 3, pp. 583-593, 2018. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“4) sound visualization systems for the hearing impaired are related to the industrial stethoscopes for localizing fault sound sources in the grid (Section IV-F), but also offer different features.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specifically, when scheduling computation across different resources, the following factors should be taken into account: data type and volume, network bandwidth, processing latency, performance accuracy, energy consumption, and data security and privacy in each specific application scenario.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, edge devices are limited by their limited computational and storage resources.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deploying deep CNN models on edge devices is crucial for real-time data stream processing and low latency in AIoT systems.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For example, the video coding scheme for machines [188] is promising for facilitating downstream computer vision tasks and requires further study.”
What the sources say
- Not checked yetcites [188] [188] L.-Y. Duan, J. Liu, W. Yang, T. Huang, and W. Gao, 'Video coding for machines: A paradigm of collaborative compression and intelligent analytics,' arXiv preprint arXiv:2001.03569 , 2020. -
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thereby, how to design or automatically search lightweight, computationally efficient, and hardware-friendly DNN architectures is of practical value but still remains challenging.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the structural representation is task-oriented and should be calculated at the network edge to minimize bandwidth and latency, which represents another challenge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To respond to the dynamic environment, AIoT behaves via control and interaction, where deep learning has demonstrated its value in improving control accuracy and enabling multi-modal interactions.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An efficient coding scheme can be used to reduce network bandwidth and transmission latency.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As described in Section II-A, a typical AIoT architecture contains heterogeneous computing resources including cloud centers, fog nodes, and edge devices.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The recently proposed federated learning framework (please refer to Section III-B5) is a representative and promising solution that allows data to be stored locally in distributed devices.”
What the sources say
- Not checked yetcites Section III-B5
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Deep learning requires large-scale data generated by different things from different users in the context of AIoT.
- Rests on an assumption: Individuals may worry about data security and privacy if data are transmitted to and stored in the cloud.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The AI perceiving technologies mentioned in Section III-A could be used to extract compact and structural representations from the data, which would be transmission and storage friendly.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, a dynamic adaptive scheduling strategy would deal with unbalanced data flow and user demands over time.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Recently, an event-based gesture recognition system is proposed by using dynamic vision sensors and a neurosynaptic event-based processor [124], which can identify gestures with a low latency energy consumption;”
What the sources say
- Not checked yetcites [124] [124] A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza et al. , 'A low power, fully event-based gesture recognition system,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7243-7252. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The three-tier computing architecture of AIoT provides different computing resources for deep learning whilst also posing new challenges, e.g., in the design and search of lightweight models and computation scheduling within the three-tier architecture.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Recently, event-based sensors and neuromorphic processors have been proposed [124].”
What the sources say
- Not checked yetcites [124] [124] A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza et al. , 'A low power, fully event-based gesture recognition system,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7243-7252. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“5) the energy management and optimization systems in smart buildings [255], [253] can be regarded as the extension of smart grids AIoT (Section IV-F).”
What the sources say
- Not checked yetcites [255] [255] J. Pan, R. Jain, S. Paul, T. Vu, A. Saifullah, and M. Sha, 'An internet of things framework for smart energy in buildings: designs, prototype, and experiments,' IEEE Internet of Things Journal , vol. 2, no. 6, pp. 527-537, 2015. source
- Not checked yetcites [253] [253] E. Mocanu, D. C. Mocanu, P. H. Nguyen, A. Liotta, M. E. Webber, M. Gibescu, and J. G. Slootweg, 'On-line building energy optimization using deep reinforcement learning,' IEEE Transactions on Smart Grid , vol. 10, no. 4, pp. 3698-3708, 2018. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In real-world AIoT systems, some intense computation may be needed to offload to the fog node or cloud center from the edge devices, thereby creating a computational scheduling challenge.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AIoT systems contain massive numbers of heterogeneous sensors that generate a large data stream of different formats, sizes, and timestamps, thereby significantly challenging further processing, transmission, and storage.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For example, event-based cameras only record pixels that change in brightness, thereby reducing redundant data generation and transmission.”
What the sources say
- Not checked yetcites [124] [124] A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza et al. , 'A low power, fully event-based gesture recognition system,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7243-7252. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Smart cities/homes/buildings AIoT is related to those in the aforementioned smart sectors and can be empowered by similar AI technologies.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
Both academia and industry have invested heavily in AIoT, and various AIoT applications have now been developed, providing services and creating value.
“Both academia and industry have invested heavily in AIoT, and various AIoT applications have now been developed, providing services and creating value.”
The paper relies on this without support.
Advanced communication technologies such as Wi-Fi 6 and 5G and AI technologies will enable mass connection.
“Advanced communication technologies such as Wi-Fi 6 (IEEE 802.11ax standard) and 5G and AI technologies will enable mass connection.”
The paper relies on this without support.
Object trackers usually run much faster than object detectors.
“Object trackers usually run much faster than object detectors, which can be deployed on edge devices of AIoT applications such as video surveillance and autonomous driving for object trajectory generation and motion prediction.”
The paper relies on this without support.
Text is very common in real-world scenes.
“Since the text is very common in real-world scenes, e.g., traffic sign, nameplate, information board, text spotting can serve as a useful tool in many AIoT applications for 'reading' text information from scene images”
The paper relies on this without support.
Cameras are prevalent.
“Nevertheless, due to the prevalence of cameras and great progress in deep learning and computer vision, visual hand gesture recognition has the vast potential”
The paper relies on this without support.
Low-light images usually contain noise that will be amplified after enhancement.
“Since lowlight images usually contain noises that will be amplified after enhancement, some robust Retinex models have been proposed to account for noise and estimate reflectance, illumination, and noise simultaneously [147], [148].”
The paper relies on this without support.
Haze attenuation and scattering cause low contrast in captured images.
“Images captured in a haze environment are in low contrast due to the haze attenuation and scattering effects.”
The paper relies on this without support.
Deep learning models can be deployed on edge/fog/cloud with acceptable latency and power.
“One possible solution is to leverage the hybrid computation architecture (see Section II-A) by deploying object tracker on edge devices while deploying object detectors on the fog nodes or cloud”
The paper relies on this without support.
The real world is dynamic and complex.
“Since the real world is dynamic and complex, using a fixed model in AIoT systems can not adapt to the variations, probably leading to a performance loss.”
The paper relies on this without support.
Speech is one of the most important non-contact interaction modes.
“ASR is very useful in many AIoT applications since speech is one of the most important non-contact interaction modes.”
The paper relies on this without support.
Annotating large-scale data can be prohibitively expensive and time-consuming.
“Since annotating large-scale data can be prohibitively expensive and time-consuming, USL and SSL can be useful for continually improving models in AIoT systems by harvesting the large-scale unlabeled data collected by massive numbers of sensors [196].”
The paper relies on this without support.
In real-world AIoT systems, there are always many related tasks involved.
“In real-world AIoT systems, there are always many related tasks involved, e.g., object detection and tracking, and semantic segmentation in video surveillance.”
The paper relies on this without support.
In real-world AIoT applications, there are always some rare cases that need to be recognized by AI models.
“In real-world AIoT applications, there are always some rare cases that need to be recognized by AI models, e.g., a car collision, cyber attack, machine fault.”
The paper relies on this without support.
Collection and annotation of large-scale rare cases are usually very difficult.
“However, the collection and annotation of large-scale such cases are usually very difficult.”
The paper relies on this without support.
The amount of unlabeled data is much larger than that of labeled data.
“Usually, the amount of unlabeled data is much larger than that of labeled data.”
The paper relies on this without support.
One classical way of reasoning causality via randomized controlled trial is costly and time-consuming.
“One classical way of reasoning causality is via randomized controlled trial by evaluating the outcomes from the treatment group and control group, which is however costly and time-consuming.”
The paper relies on this without support.
In our real-world, there is much knowledge carried by web information, medical records, financial transactions, etc., which can be used to reason.
“In our real-world, there is much knowledge carried by web information, medical records, financial transactions, etc., which can be used to reason the answer to a question or infer patient cohorts.”
The paper relies on this without support.
We humans have the ability of causal reasoning such as causal inference and causal discovery.
“We humans have the ability of causal reasoning such as causal inference and causal discovery.”
The paper relies on this without support.
The behaving ability is important for AIoT systems.
“The behaving ability is also very important for AIoT systems passively responding to environment variations and raised requests, or actively exploring the unknown.”
The paper relies on this without support.
Traditional camera sensors continually generate dense data once opened, which are further fed into deep CNNs for GPU processing.
“Traditional camera sensors continually generate dense data once opened, which are further fed into deep CNNs for further GPU processing.”
The paper relies on this without support.
Generally, all pixels are used in the calculation, resulting in high computational costs.
“Generally, all the pixels are used in the calculation, resulting in high computational costs.”
The paper relies on this without support.
In embodied AI, it is difficult and/or costly to train models in the real world.
“In embodied AI, it is difficult and/or costly to train models in the real world, e.g., autonomous driving, robot arm control, and robot navigation.”
The paper relies on this without support.
3D virtual platforms are very useful for cost-effectively training deep learning models, especially for deep reinforcement learning.
“which are very useful for cost-effectively training deep learning models, especially for deep reinforcement learning.”
The paper relies on this without support.
The critical issue of domain shift between the virtual and physical environments must be addressed before deploying the trained model into real-world scenarios.
“Nevertheless, the critical issue of domain shift between the virtual and physical environments must be addressed before deploying the trained model into real-world scenarios.”
The paper relies on this without support.
Transfer learning and domain adaptation have attracted significant attention to address domain shift in unsupervised learning and reinforcement learning.
“Recently, transfer learning and domain adaptation have attracted significant attention to address this issue in the setting of both unsupervised learning and reinforcement learning.”
The paper relies on this without support.
Deep learning model performance is largely determined by large-scale training data.
“Deep learning model performance is largely determined by large-scale training data.”
The paper relies on this without support.
Humans learn new concepts based not only on data but on prior knowledge.
“However, humans learn new concepts based not only on data but on prior knowledge.”
The paper relies on this without support.
Prior knowledge can be very useful for training deep learning models in a data-efficient way.
“Likewise, prior knowledge can be very useful for training deep learning models in a data-efficient way.”
The paper relies on this without support.
Attribute-based class description enables zero-shot learning for new concepts via attribute transfer.
“For example, attribute-based class description enables zeroshot learning for new concepts via attribute transfer.”
The paper relies on this without support.
Knowledge can be extracted from unstructured data to build knowledge graphs, learn knowledge embedding representations, and for reasoning.
“Knowledge can be extracted from unstructured data to build knowledge graphs, learn knowledgeembedding representations, and for reasoning.”
The paper relies on this without support.
Integrated with deep learning (e.g., graph neural networks), knowledge graphs are a useful approach in many areas such as question and answer systems and fault/disease diagnosis, opening up promising research avenues towards human-level cognition intelligence.
“Integrated with deep learning (e.g., graph neural networks), this is a useful approach in many areas such as question and answer systems and fault/disease diagnosis, opening up promising research avenues towards human-level cognition intelligence.”
The paper relies on this without support.
Deep learning requires large-scale data generated by different things from different users in the context of AIoT.
“Deep learning requires large-scale data generated by different things from different users in the context of AIoT.”
The paper relies on this without support.
Individuals may worry about data security and privacy if data are transmitted to and stored in the cloud.
“Individuals may worry about data security and privacy if data are transmitted to and stored in the cloud.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Regarding the technical challenges related to DL-based AIoT, they include [13]:•Multimodal heterogeneous data processing, transmission, and storage pertaining to the massive numbers of heterogeneous sensors and the vast data streams of different formats, sizes, and timestamps they generate.•Limited computational and storage resources in relation to using DL models for real-time data stream”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [13]
In the citing paper
“DL has attracted increased attention and has proven useful for improving the intelligence of AIoT applications to handle dynamic and complex environments in the context of unsupervised and reinforcement learning methods [13].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [13]
In the citing paper
“Additionally, AIoT addresses the challenges associated with transmitting the rapid torrent of data from distributed sensor network infrastructure [12,13].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [13]
In the citing paper
“Moreover, Zhang and Tao [13] synthesize several studies on the application of AIoT using DL in smart transportation (e.g., traffic participants, traffic infrastructures, connected logistics, and in-car driver behavior monitoring) as well as smart grids (grid fault diagnosis, building management and optimization, load monitoring and scheduling, and cyber-attack detection).”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [13]
Citations
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1 retracted
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Metadata
- Authors
- Zhang, J., & Tao, D.
- Affiliations
- The University of Sydney (AU)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-05-15
- Volume
- 8
- Issue
- 10
- Pages
- 7789-7817
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3109340983
- Semantic Scholar
- 3e3df220673388402b6b114eab68a9c5396210b1
- Cited by
- 774
- References
- 271
- Citations by year
- 2026: 86 · 2025: 170 · 2024: 165 · 2023: 141 · 2022: 139 · 2021: 69
- FWCI
- 41.36
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council Projects (FL-170100117, DP-180103424, IH-180100002)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- AIoT, Internet of Things, deep learning, fog computing, edge computing, cloud computing, heterogeneous data processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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