Check computer science papers: citations, claims and retractions

An efficient solution to the five-point relative pose problem

Nister, D. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 279

Reservoir Computing Using Diffusive Memristors

Midya, R., Wang, Z., Asapu, S., Zhang, X., Rao, M., Song, W., Zhuo, Y., Upadhyay, N., Xia, Q., & Yang, J. J. (2019) · Advanced Intelligent Systems

Neural Networks and Reservoir ComputingAdvanced Memory and Neural Computing

Cited by 279

A new chaotic key-based design for image encryption and decryption

Jui-Cheng, & Guo, J.-I. (2002) · 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)

Chaos-based Image/Signal EncryptionComputer Science and Engineering

Cited by 279

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

Hutchinson, B., Smart, A., Hanna, A., Denton, R., Greer, C., Kjartansson, O., Barnes, P., & Mitchell, M. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 279

Obstacles to State Preparation and Variational Optimization from Symmetry Protection

Bravyi, S., Kliesch, A., Koenig, R., & Tang, E. (2019) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 279

The Whiteness of AI

Cave, S., & Dihal, K. (2020) · Philosophy & Technology

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 279

Performance Analysis of a Hyperledger Fabric Blockchain Framework: Throughput, Latency and Scalability

Kuzlu, M., Pipattanasomporn, M., Gurses, L., & Rahman, S. (2019) · 2019 IEEE International Conference on Blockchain (Blockchain)

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 279

A Survey of Handwritten Character Recognition with MNIST and EMNIST

Baldominos, A., Saez, Y., & Isasi, P. (2019) · Applied Sciences

Handwritten Text Recognition TechniquesAdvanced Neural Network Applications

Cited by 279

Differentiable learning of quantum circuit Born machines

Liu, J.-G., & Wang, L. (2018) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 279

ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation

Dai, X., Zhang, P., Wu, B., Yin, H., Sun, F., Wang, Y., Dukhan, M., Hu, Y., Wu, Y., Jia, Y., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 279

Agency plus automation: Designing artificial intelligence into interactive systems

Heer, J. (2019) · Proceedings of the National Academy of Sciences

Usability and User Interface DesignData Visualization and Analytics

Cited by 279

Automatic identification of artifacts in electrodermal activity data

Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Emotion and Mood RecognitionHeart Rate Variability and Autonomic Control

Cited by 279

GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking

Huang, L., Zhao, X., & Huang, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 279

Non‐Rigid Registration Under Isometric Deformations

Huang, Q., Adams, B., Wicke, M., & Guibas, L. J. (2008) · Computer Graphics Forum

3D Shape Modeling and AnalysisMedical Image Segmentation Techniques

Cited by 279

Natural Information Processing Systems

Sweller, J., & Sweller, S. (2006) · Evolutionary Psychology

Cognitive Computing and NetworksCognitive Science and Education Research

Cited by 279

Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

Teramoto, A., Tsukamoto, T., Kiriyama, Y., & Fujita, H. (2017) · BioMed Research International

AI in cancer detectionDigital Imaging for Blood Diseases

Cited by 279

Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 279

Introducing Time into RDF

Gutierrez, C., Hurtado, C., & Vaisman, A. (2007) · IEEE Transactions on Knowledge and Data Engineering

Semantic Web and OntologiesLogic, Reasoning, and Knowledge

Cited by 279

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., et al. (2019) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 279

Chaos-based random number generators. Part II: practical realization

Stojanovski, T., Pihl, J., & Kocarev, L. (2001) · IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

Chaos-based Image/Signal EncryptionChaos control and synchronization

Cited by 279

Halving the cost of quantum addition

Gidney, C. (2018) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 279

Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts

Brayne, S., & Christin, A. (2021) · Social Problems

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 279

Global Viewing of Heterogeneous Data Sources

Castano, S., & De Antonellis, V. (2001) · IEEE Transactions on Knowledge and Data Engineering

Semantic Web and OntologiesAdvanced Database Systems and Queries

Cited by 279

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Hartvigsen, T., Gabriel, S., Palangi, H., Sap, M., Ray, D., & Kamar, E. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion Mining

Cited by 279