Property:References

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E
Evaluating the Effects of Social Media on Educational Sustainability in Sweden +
- Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability, 11(6), 1683. - Ebrahimi, P., Khajeheian, D., & Fekete-Farkas, M. (2021). A SEM-NCA approach towards social networks marketing: Evaluating consumers’ sustainable purchase behavior with the moderating role of eco-friendly attitude. International Journal of Environmental Research and Public Health, 18(24), 13276. - Li, J., & Xue, E. (2022). A social networking analysis of education policies of creating world-class universities for higher education sustainability in China. Sustainability, 14(16), 10243.  +
Evaluation of Open Source Robot Simulators for Smart Mobility Applications +
http://www.gcdc.net/ http://en.wikipedia.org/wiki/Research_and_development http://en.wikipedia.org/wiki/Verification_and_validation http://www.robocup.org/ http://www.theroboticschallenge.org/aboutsimulator.aspx http://www.coppeliarobotics.com/ http://gazebosim.org/  +
Evolutionary Behavior Trees for Multi-Agent Task-Oriented Environment +
http://frail.ii.pwr.edu.pl/  +
Explainable AI and poverty prediction +
(1) Lee and Braithwaite (2021), "High-Resolution Poverty Maps in Sub-Saharan Africa", https://arxiv.org/abs/2009.00544 (2) Jean, Burke, Xie, Davis, Lobell, and Ermon (2016), "Combining satellite imagery and machine learning to predict poverty", Science, https://www.science.org/doi/10.1126/science.aaf7894 (3) Roscher, Bohn, Duarte, and Garcke (2020), "Explainable Machine Learning for Scientific Insights and Discoveries", IEEE Access, https://ieeexplore.ieee.org/document/9007737  +
Explainable AI for predictive maintenance in collaboration with Volvo +
1- Adadi, Amina, and Mohammed Berrada. "Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)." IEEE access 6 (2018): 52138-52160. 2- Arrieta, Alejandro Barredo, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion 58 (2020): 82-115. 3- https://dl.acm.org/doi/abs/10.1145/3292500.3332281 4- Cortez, Paulo, and Mark J. Embrechts. "Using sensitivity analysis and visualization techniques to open black box data mining models." Information Sciences 225 (2013): 1-17. 5- Gilpin, Leilani H., et al. "Explaining explanations: An overview of interpretability of machine learning." 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). IEEE, 2018.  +
Explainable Anomaly Detection +
Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54. Jacob, V., Song, F., Stiegler, A., Rad, B., Diao, Y., & Tatbul, N. (2020). Exathlon: A benchmark for explainable anomaly detection over time series. arXiv preprint arXiv:2010.05073. Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A. (2021). A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(5), 1-46. Chatterjee, J., & Dethlefs, N. (2020, September). Temporal causal inference in wind turbine scada data using deep learning for explainable AI. In Journal of Physics: Conference Series (Vol. 1618, No. 2, p. 022022). IOP Publishing. Liu, Y., Ding, K., Lu, Q., Li, F., Zhang, L. Y., & Pan, S. (2024). Towards self-interpretable graph-level anomaly detection. Advances in Neural Information Processing Systems, 36. Deng, A., & Hooi, B. (2021, May). Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 5, pp. 4027-4035). Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., ... & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012-12038. Rad, B., Song, F., Jacob, V., & Diao, Y. (2021, June). Explainable anomaly detection on high-dimensional time series data. In Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems (pp. 2-14).  +
Explainable GNNs for Security Verification of RISC-V Cores +
Reimann, Lennart M., et al. ”Qtflow: Quantitative timing-sensitive information flow for security-aware hardware design on rtl.” 2024 International VLSI Symposium on Technology, Systems and Applications (VLSI TSA). IEEE, 2024. Gosch, Lukas, et al. ”Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks.” Transactions on Machine Learning Research.  +
Exploring, modelling and optimization of home care regions +
Madigan, E. A., & Curet, O. L. (2006). A data mining approach in home healthcare: outcomes and service use. BMC health services research, 6(1), 1. Cheng, B. W., Chang, C. L., & Liu, I. S. (2005). Enhancing care services quality of nursing homes using data mining. Total Quality Management & Business Excellence, 16(5), 575-596. Hirdes, J. P., Poss, J. W., & Curtin-Telegdi, N. (2008). The Method for Assigning Priority Levels (MAPLe): a new decision-support system for allocating home care resources. BMC medicine, 6(1), 1. Harrington, C., Zimmerman, D., Karon, S. L., Robinson, J., & Beutel, P. (2000). Nursing home staffing and its relationship to deficiencies. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(5), S278-S287.  +
F
Fair Conformal Prediction +
https://ojs.aaai.org/index.php/AAAI/article/view/21459  +
Fair representation learning of electronic health records +
Dullerud, N., Roth, K., Hamidieh, K., Papernot, N. and Ghassemi, M., 2022. Is fairness only metric deep? evaluating and addressing subgroup gaps in deep metric learning. arXiv preprint arXiv:2203.12748. Reddy, C., Sharma, D., Mehri, S., Romero-Soriano, A., Shabanian, S. and Honari, S., 2021, June. Benchmarking bias mitigation algorithms in representation learning through fairness metrics. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). Yuan, Y., Xun, G., Suo, Q., Jia, K. and Zhang, A., 2019. Wave2vec: Deep representation learning for clinical temporal data. Neurocomputing, 324, pp.31-42.  +
Federatad Learning (FL) improving security and privacy in tactical networks +
Federated Learning in Intrusion Detection: Advancements, Applications, and Future Directions https://link.springer.com/article/10.1007/s10586-025-05325-w Federated Learning for Anomaly Detection: A Systematic Review on Scalability, Adaptability, and Benchmarking Framework https://www.mdpi.com/1999-5903/17/8/375  +
Federated Learning Aggregation Strategies by Weight Exploration +
Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine 37.3 (2020): 50-60. Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210. Rieke, Nicola, et al. "The future of digital health with federated learning." NPJ digital medicine 3.1 (2020): 1-7. Li, Tian, et al. "Federated optimization in heterogeneous networks." Proceedings of Machine Learning and Systems 2 (2020): 429-450.  +
FirstResponse +
Loreto Susperregi, et al. On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot. Sensors 2013, 13(11), 14687-14713. Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection. Proceedings of Robotics: Science and Systems 2011.  +
Forecast energy consumption in buildings to help Mestro customers save energy +
https://www.youtube.com/watch?v=XAb7faAK7_0 https://www.youtube.com/watch?v=f_d8m8tHcMg  +
Forecasting Industrial IoT Time Series @AlfaLaval +
https://www.youtube.com/watch?v=ZuydOEws92s  +
Foundation Models for Time Series Analysis +
- Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., ... & Wen, Q. (2024, August). Foundation models for time series analysis: A tutorial and survey. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6555-6565). - Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., ... & Rish, I. (2023). Lag-llama: Towards foundation models for time series forecasting. arXiv preprint arXiv:2310.08278. - Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., ... & Wen, Q. (2023). Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728. - Liu, Y., Qin, G., Huang, X., Wang, J., & Long, M. (2024). Autotimes: Autoregressive time series forecasters via large language models. arXiv preprint arXiv:2402.02370. - Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., & Zimmermann, R. (2024, May). Unitime: A language-empowered unified model for cross-domain time series forecasting. In Proceedings of the ACM on Web Conference 2024 (pp. 4095-4106). - Huang, X., Tang, J., & Shen, Y. (2024). Long time series of ocean wave prediction based on PatchTST model. Ocean Engineering, 301, 117572. - Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021, May). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 12, pp. 11106-11115). - Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, 34, 22419-22430. - Wang, Y., Wu, H., Dong, J., Liu, Y., Qiu, Y., Zhang, H., ... & Long, M. (2024). Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072.  +
G
Game-Theoretic Trust Evaluation for Objective Conflict Resolution +
1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016. 2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011. 3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011.  +
Generating synthetic time series data in case of data scarcity +
- Using generative adversarial networks (GAN) to simulate central-place foraging trajectories https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13853 - Data-Driven Crowd Simulation with Generative Adversarial Networks http://rainbow-doc.irisa.fr/pdf/2019_amirian_casa.pdf  +
Generative Approach for Multivariate Signals +
https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf https://openreview.net/pdf?id=Sy2fzU9gl https://www.sciencedirect.com/science/article/pii/S092658051930367X  +
Graph Neural Networks for Traffic Flow Forecasting +
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.  +
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