Check computer science papers: citations, claims and retractions

Establishing and evaluating trustworthy AI: overview and research challenges

Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 59

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

Kim, G., & Cho, K. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 59

On Developing Smart Transportation Applications in Fog Computing Paradigm

Giang, N. K., Leung, V. C. M., & Lea, R. (2016) · Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

IoT and Edge/Fog ComputingVehicular Ad Hoc Networks (VANETs)

Cited by 59

Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access

Xiao, Z., Liao, Q. V., Zhou, M., Grandison, T., & Li, Y. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces

Expert finding and Q&A systemsMisinformation and Its Impacts

Cited by 59

On the Intrinsic Dimensionality of Image Representations

Gong, S., Boddeti, V. N., & Jain, A. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face and Expression RecognitionAdvanced Neural Network Applications

Cited by 59

Psychophysical customization of directional transfer functions for virtual sound localization

Middlebrooks, J. C., Macpherson, E. A., & Onsan, Z. A. (2000) · The Journal of the Acoustical Society of America

Speech and Audio ProcessingHuman auditory perception and evaluation

Cited by 59

A Powerful Generative Model Using Random Weights for the Deep Image Representation

He, K., Wang, Y., & Hopcroft, J. (2016) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 59

Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks

Khan, A., Sun, L., & Ifeachor, E. (2009) · Journal of Multimedia

Image and Video Quality AssessmentNetwork Traffic and Congestion Control

Cited by 59

A new data mining based network Intrusion Detection model

Gudadhe, M., Prasad, P., & Kapil Wankhade, L. (2010) · 2010 International Conference on Computer and Communication Technology (ICCCT)

Network Security and Intrusion DetectionNetwork Packet Processing and Optimization

Cited by 59

Human learning and learning analytics in the age of artificial intelligence.

Istenič Starčič, A. (2019) · British Journal of Educational Technology

Intelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Cited by 59

Automation of Language Sample Analysis

Liu, H., MacWhinney, B., Fromm, D., & Lanzi, A. (2023) · Journal of Speech, Language, and Hearing Research

Speech Recognition and SynthesisLanguage Development and Disorders

Cited by 59

FlashExtract: a framework for data extraction by examples

Le, V., & Gulwani, S. (2014) · ACM SIGPLAN Notices

Web Data Mining and AnalysisAdvanced Database Systems and Queries

Cited by 59

SciCap: Generating Captions for Scientific Figures

Hsu, T.-Y., Giles, C. L., & Huang, T.-H. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 59

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Wang, Z., Wang, X., An, B., Yu, D., & Chen, C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 59

Traditional Chinese medicine knowledge graph construction based on large language models

Zhang, Y., & Hao, Y. (2024) · Electronics

Advanced Graph Neural NetworksSemantic Web and Ontologies

Cited by 59

EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities

Li, N., Gao, C., Li, M., Li, Y., & Liao, Q. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multi-Agent Systems and NegotiationComplex Systems and Time Series Analysis

Cited by 59

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision

Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X. (2018) · Artificial Intelligence in Medicine

Visual Attention and Saliency DetectionNeuroscience and Neural Engineering

Cited by 59

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

Yu, Q., Li, J., Wei, L., Pang, L., Ye, W., Qin, B., Tang, S., Tian, Q., & Zhuang, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 59

Graph Based Framework for Malicious Insider Threat Detection

Gamachchi, A., Sun, L., & Boztas, S. (2017) · Proceedings of the Annual Hawaii International Conference on System Sciences

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 59

A federated learning framework for healthcare IoT devices

Yuan, B., Ge, S., & Xing, W. (2020) · arXiv

Privacy-Preserving Technologies in DataInternet of Things and AI

Cited by 59

Artificial intelligence and the affective labour of understanding: The intimate moderation of a language model

Perrotta, C., Selwyn, N., & Ewin, C. (2024) · New Media & Society

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 59

Inference under information constraints I: Lower bounds from chi-square contraction

Acharya, J., Canonne, C. L., & Tyagi, H. (2020) · IEEE Transactions on Information Theory

Privacy-Preserving Technologies in DataDistributed Sensor Networks and Detection Algorithms

Cited by 59

Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge Computing Environment

Mudassar, M., Zhai, Y., & Lejian, L. (2022) · IEEE Internet of Things Journal

Distributed systems and fault toleranceIoT and Edge/Fog Computing

Cited by 59

I/O-efficient techniques for computing pagerank

Chen, Y.-Y., Gan, Q., & Suel, T. (2002) · Proceedings of the eleventh international conference on Information and knowledge management

Graph Theory and AlgorithmsInformation Retrieval and Search Behavior

Cited by 59