Introduction to algorithms

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472

Ethical Implications and Accountability of Algorithms

Martin, K. (2019) · Journal of Business Ethics · doi:10.1007/s10551-018-3921-3 · Cited by 757

A tutorial in connectome analysis: Topological and spatial features of brain networks

Kaiser, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.025 · Cited by 371

Algorithmic governance: Developing a research agenda through the power of collective intelligence

Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., Felzmann, H., Haklay, M., Khoo, S.-M., Morison, J., et al. (2017) · Big Data & Society · doi:10.1177/2053951717726554 · Cited by 369

A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023) · ACM Computing Surveys · doi:10.1145/3527848 · Cited by 301

Exponential Stabilization of Memristive Neural Networks With Time Delays

Wu, A., & Zeng, Z. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2219554 · Cited by 295

A critical review of algorithms in HRM: Definition, theory, and practice

Cheng, M. M., & Hackett, R. D. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2019.100698 · Cited by 283

Semantics-based obfuscation-resilient binary code similarity comparison with applications to software plagiarism detection

Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635900 · Cited by 220

Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing

Seele, P., Dierksmeier, C., Hofstetter, R., & Schultz, M. D. (2021) · Journal of Business Ethics · doi:10.1007/s10551-019-04371-w · Cited by 196

Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis

Baumgartner, M., & Thiem, A. (2020) · Sociological Methods & Research · doi:10.1177/0049124117701487 · Cited by 193

Mapping the customer journey: Lessons learned from graph-based online attribution modeling

Anderl, E., Becker, I., von Wangenheim, F., & Schumann, J. H. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.03.001 · Cited by 186

Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

Ahmed, T., Coates, M., & Lakhina, A. (2007) · IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications · doi:10.1109/infcom.2007.79 · Cited by 148

Semantics-Based Obfuscation-Resilient Binary Code Similarity Comparison with Applications to Software and Algorithm Plagiarism Detection

Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2655046 · Cited by 144

Algorithmic Literacy and the Role for Libraries

Ridley, M., & Pawlick-Potts, D. (2021) · Information Technology and Libraries · doi:10.6017/ital.v40i2.12963 · Cited by 111

Toward an artificial intelligence physicist for unsupervised learning

Wu, T., & Tegmark, M. (2019) · Physical Review E · doi:10.1103/physreve.100.033311 · Cited by 99

Selecting Representative Data Sets

Borovicka, T., Jirina, M., Kordik, P., & Jiri, M. (2012) · Advances in Data Mining Knowledge Discovery and Applications · doi:10.5772/50787 · Cited by 94

Discovering outlying aspects in large datasets

Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0453-2 · Cited by 83

Decision tree and random forest implementations for fast filtering of sensor data

Buschjager, S., & Morik, K. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2710627 · Cited by 78

Nature-inspired framework for hyperspectral band selection

Nakamura, R. Y. M., Fonseca, L. M. G., Santos, J. A. D., Torres, R. D. S., Yang, X.-S., & Papa, J. P. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2258351 · Cited by 70

Optimizing Virtual Machine placement in IaaS data centers: Taxonomy, review and open issues

Talebian, H., Gani, A., Sookhak, M., Abdelatif, A. A., Yousafzai, A., Vasilakos, A. V., & Yu, F. R. (2020) · Cluster Computing · doi:10.1007/s10586-019-02954-w · Cited by 72

Better Together? An Evaluation of AI-Supported Code Translation

Weisz, J. D., Muller, M., Ross, S. I., Martinez, F., Houde, S., Agarwal, M., Talamadupula, K., & Richards, J. T. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511157 · Cited by 69

Automated Decision-Making and Big Data: Concerns for People With Mental Illness

Monteith, S., & Glenn, T. (2016) · Current Psychiatry Reports · doi:10.1007/s11920-016-0746-6 · Cited by 62

Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning

Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116113 · Cited by 61

Conceptualizing generative AI as style engines: Application archetypes and implications

Riemer, K., & Peter, S. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102824 · Cited by 63

Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect

Shabbir, S., Zhang, Y., Sun, C., Yue, Z., Xu, W., Zou, L., Chen, F., & Yu, J. (2021) · Journal of Analytical Atomic Spectrometry · doi:10.1039/d1ja00076d · Cited by 42

Rule-based deduplication of article records from bibliographic databases

Jiang, Y., Lin, C., Meng, W., Yu, C., Cohen, A. M., & Smalheiser, N. R. (2014) · Database · doi:10.1093/database/bat086 · Cited by 41

Inteligencia Artificial (y Administración Pública)

Criado, J. I. (2021) · EUNOMÍA. Revista en Cultura de la Legalidad · doi:10.20318/eunomia.2021.6097 · Cited by 38

Solving optimization problems with Rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks

Serret, M. F., Marchand, B., & Ayral, T. (2020) · Physical Review A · doi:10.1103/physreva.102.052617 · Cited by 32

Using entropy measures for comparison of software traces

Miranskyy, A. V., Davison, M., Reesor, R. M., & Murtaza, S. S. (2012) · Information Sciences · doi:10.1016/j.ins.2012.03.017 · Cited by 27

Multi-Level Over-the-Air Aggregation of Mobile Edge Computing Over D2D Wireless Networks

Wang, F., & Lau, V. K. N. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3165658 · Cited by 21

A 3-D energy-harvesting-aware routing scheme for space nanosatellite networks

Wang, J., Zhang, R., Yuan, J., & Du, X. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2803111 · Cited by 17

How Fast Do Algorithms Improve? [Point of View]

Sherry, Y., & Thompson, N. C. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3107219 · Cited by 14

Lower Bounds for Maximum Weighted Cut

Gutin, G., & Yeo, A. (2023) · SIAM Journal on Discrete Mathematics · doi:10.1137/21m1411913 · Cited by 7

Computing Education Research Today

Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7