Check computer science papers: citations, claims and retractions

Usability metric for mobile application: a goal question metric (GQM) approach

Hussain, A., & Ferneley, E. (2008) · Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services

Usability and User Interface DesignSoftware Engineering Techniques and Practices

Cited by 70

Multimodal emotion recognition: A comprehensive review, trends, and challenges

Ramaswamy, M. P. A., & Palaniswamy, S. (2024) · WIREs Data Mining and Knowledge Discovery

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 70

Sensitivity of wireless network simulations to a two-state markov model channel approximation

McDougall, J., & Miller, S. (2004) · GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489)

Wireless Networks and ProtocolsAdvanced Wireless Communication Techniques

Cited by 70

Long-term annual mapping of four cities on different continents by applying a deep information learning method to landsat data

Lyu, H., Lu, H., Mou, L., Li, W., Wright, J., Li, X., Li, X., Zhu, X., Wang, J., Yu, L., et al. (2018) · Remote Sensing

Remote-Sensing Image ClassificationRemote Sensing and Land Use

Cited by 70

AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models

Wang, Q., Guo, Y., Wang, X., Ji, T., Yu, L., & Li, P. (2020) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 70

Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories

Al-Hashedi, A., Al-Fuhaidi, B., Mohsen, A. M., Ali, Y., Gamal Al-Kaf, H. A., Al-Sorori, W., & Maqtary, N. (2022) · Applied Computational Intelligence and Soft Computing

Sentiment Analysis and Opinion MiningMisinformation and Its Impacts

Cited by 70

Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies

Ulnicane, I., Eke, D. O., Knight, W., Ogoh, G., & Stahl, B. C. (2021) · Interdisciplinary Science Reviews

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 70

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices

Mathur, A., Zhang, T., Bhattacharya, S., Velickovic, P., Joffe, L., Lane, N. D., Kawsar, F., & Lio, P. (2018) · 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

Context-Aware Activity Recognition SystemsMobile Crowdsensing and Crowdsourcing

Cited by 70

Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction

Lee, G.-H., & Lee, S.-W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face recognition and analysis3D Shape Modeling and Analysis

Cited by 70

Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

Wang, J., Sun, Q., Li, X., & Gao, M. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingMultimodal Machine Learning Applications

Cited by 70

How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 70

A cognitive architecture for robot self-consciousness

Chella, A., Frixione, M., & Gaglio, S. (2008) · Artificial Intelligence in Medicine

Psychiatry, Mental Health, NeurosciencePhilosophy and Theoretical Science

Cited by 70

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

Li, J., Chen, J., Ren, R., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Deep learning in selected cancers’ image analysis—a survey

Debelee, T. G., Kebede, S. R., Schwenker, F., & Shewarega, Z. M. (2020) · Journal of Imaging

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 70

Exploratory versus explanatory visual learning analytics: Driving teachers’ attention through educational data storytelling

Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., & Conati, C. (2018) · Journal of Learning Analytics

Data Visualization and AnalyticsOnline Learning and Analytics

Cited by 70

Selective annotation makes language models better few-shot learners

Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 70

Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs

Balloccu, S., Schmidtová, P., Lango, M., & Dusek, O. (2024) · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change

Valmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., & Kambhampati, S. (2022) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 70

The fundamentals of eye tracking part 1: the link between theory and research question

Hessels, R. S., Nuthmann, A., Nyström, M., Andersson, R., Niehorster, D. C., & Hooge, I. T. C. (2024) · Behavior Research Methods

Gaze Tracking and Assistive TechnologyVisual Attention and Saliency Detection

Cited by 70

Designing Co-Creative AI for Public Spaces

Long, D., Jacob, M., & Magerko, B. (2019) · Proceedings of the 2019 on Creativity and Cognition

Innovative Human-Technology InteractionArt, Technology, and Culture

Cited by 70

AI for Security and Security for AI

Bertino, E., Kantarcioglu, M., Akcora, C. G., Samtani, S., Mittal, S., & Gupta, M. (2021) · Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 70

LLM in the Shell: Generative Honeypots

Sladić, M., Valeros, V., Catania, C., & Garcia, S. (2024) · 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

Network Security and Intrusion DetectionSecurity and Verification in Computing

Cited by 70

Horizontal Innovation Networks - by and for Users

von Hippel, E. (2002) · SSRN Electronic Journal

Open Source Software InnovationsDigital Platforms and Economics

Cited by 70

Extractive text summarization system to aid data extraction from full text in systematic review development

Bui, D. D. A., Del Fiol, G., Hurdle, J. F., & Jonnalagadda, S. (2016) · Journal of Biomedical Informatics

Advanced Text Analysis TechniquesMeta-analysis and systematic reviews

Cited by 70