Check computer science papers: citations, claims and retractions

Recognizing Emotions in Video Using Multimodal DNN Feature Fusion

Williams, J., Kleinegesse, S., Comanescu, R., & Radu, O. (2018) · Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 132

Blocking Learner Error States in a Training-Wheels System

Carroll, J. M., & Carrithers, C. (1984) · Human Factors: The Journal of the Human Factors and Ergonomics Society

Usability and User Interface DesignHuman-Automation Interaction and Safety

Cited by 132

HPatches: A benchmark and evaluation of handcrafted and learned local descriptors

Balntas, V., Lenc, K., Vedaldi, A., Tuytelaars, T., Matas, J., & Mikolajczyk, K. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 132

Quantifying query ambiguity

Cronen-Townsend, S., & Croft, W. B. (2002) · Proceedings of the second international conference on Human Language Technology Research -

Information Retrieval and Search BehaviorNatural Language Processing Techniques

Cited by 132

Factual Error Correction for Abstractive Summarization Models

Cao, M., Dong, Y., Wu, J., & Cheung, J. C. K. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 132

Discovering Characteristic Actions from On-Body Sensor Data

Minnen, D., Starner, T., Essa, I., & Isbell, C. (2006) · 2006 10th IEEE International Symposium on Wearable Computers

Context-Aware Activity Recognition SystemsTime Series Analysis and Forecasting

Cited by 132

Charting the Landscape of Online Cryptocurrency Manipulation

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M., & Ferrara, E. (2020) · IEEE Access

Spam and Phishing DetectionCybercrime and Law Enforcement Studies

Cited by 132

On the security of a class of diffusion mechanisms for image encryption

Zhang, L. Y., Liu, Y., Pareschi, F., Zhang, Y., Wong, K.-W., Rovatti, R., & Setti, G. (2018) · IEEE Transactions on Cybernetics

Chaos-based Image/Signal EncryptionCryptographic Implementations and Security

Cited by 132

Learning Language Games through Interaction

Wang, S. I., Liang, P., & Manning, C. D. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 132

A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education

Doughty, J., Wan, Z., Bompelli, A., Qayum, J., Wang, T., Zhang, J., Zheng, Y., Doyle, A., Sridhar, P., Agarwal, A., et al. (2024) · Proceedings of the 26th Australasian Computing Education Conference

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 132

Interacting with computers by voice: automatic speech recognition and synthesis

O'Shaughnessy, D. (2003) · Proceedings of the IEEE

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 132

Cognitive Networks: Towards Self‐Aware Networks

(2007) · Wiley eBooks

Cognitive Radio Networks and Spectrum SensingSoftware-Defined Networks and 5G

Cited by 132

Experimenting with musically motivated convolutional neural networks

Pons, J., Lidy, T., & Serra, X. (2016) · 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)

Music and Audio ProcessingMusic Technology and Sound Studies

Cited by 132

Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking

Sharma, N., Liao, Q. V., & Xiao, Z. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Information Retrieval and Search BehaviorMisinformation and Its Impacts

Cited by 132

Teachers’ perspective on artificial intelligence education: An initial investigation

Polak, S., Schiavo, G., & Zancanaro, M. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 132

What’s mine is a hologram? How shared augmented reality augments psychological ownership

Carrozzi, A., Chylinski, M., Heller, J., Hilken, T., Keeling, D. I., & de Ruyter, K. (2019) · Journal of Interactive Marketing

Augmented Reality ApplicationsConsumer Behavior in Brand Consumption and Identification

Cited by 132

Resource efficient mobile computing using cloudlet infrastructure

Jararweh, Y., Tawalbeh, L., Ababneh, F., & Dosari, F. (2013) · 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks

IoT and Edge/Fog ComputingCloud Computing and Remote Desktop Technologies

Cited by 132

Internal consistency of fault-tolerant quantum error correction in light of rigorous derivations of the quantum Markovian limit

Alicki, R., Lidar, D. A., & Zanardi, P. (2006) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 132

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

Nie, Y., Wang, S., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 132

Blockchain network slice broker in 5G: Slice leasing in factory of the future use case

Backman, J., Yrjola, S., Valtanen, K., & Mammela, O. (2017) · 2017 Internet of Things Business Models, Users, and Networks

Software-Defined Networks and 5GBlockchain Technology Applications and Security

Cited by 132

From Gaming to Computational Thinking: An Adaptive Educational Computer Game-Based Learning Approach

Hooshyar, D., Pedaste, M., Yang, Y., Malva, L., Hwang, G.-J., Wang, M., Lim, H., & Delev, D. (2021) · Journal of Educational Computing Research

Teaching and Learning ProgrammingEducational Games and Gamification

Cited by 132

Overcoming the Lack of Parallel Data in Sentence Compression

Filippova, K., & Altun, Y. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesAdvanced Text Analysis Techniques

Cited by 132

Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association

Yu, Q., Medioni, G., & Cohen, I. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition

Video Surveillance and Tracking MethodsTarget Tracking and Data Fusion in Sensor Networks

Cited by 132

Language models are an effective representation learning technique for electronic health record data

Steinberg, E., Jung, K., Fries, J. A., Corbin, C. K., Pfohl, S. R., & Shah, N. H. (2021) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 132