RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation
(2021)
Presentation / Conference Contribution
Samin, H., Paucar, L. H. G., Bencomo, N., Hurtado, C. M. C., & Fredericks, E. M. (2021, December). RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation. Presented at 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2021, Madrid, Spain, May 18-24, 2021
Outputs (3164)
“Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving (2021)
Presentation / Conference Contribution
Stelling, J., & Atapour-Abarghouei, A. (2021, December). “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USABiases can filter into AI technology without our knowledge. Oftentimes, seminal deep learning networks champion increased accuracy above all else. In this paper, we attempt to alleviate biases encountered by semantic segmentation models in urban driv... Read More about “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving.
Constructing a crowdsourced linked open knowledge base of Chinese history (2021)
Presentation / Conference Contribution
Sturgeon, D. (2021, September). Constructing a crowdsourced linked open knowledge base of Chinese history. Presented at 2021 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC), Taipei, TaiwanThis paper introduces a crowdsourced approach to knowledge base construction for historical data based upon annotation of historical source materials. Building on an existing digital library of premodern Chinese texts and adapting techniques from oth... Read More about Constructing a crowdsourced linked open knowledge base of Chinese history.
Locating Datacenter Link Faults with a Directed Graph Convolutional Neural Network. (2021)
Presentation / Conference Contribution
Kenning, M. P., Deng, J., Edwards, M., & Xie, X. (2021, December). Locating Datacenter Link Faults with a Directed Graph Convolutional Neural Network. Presented at ICPRAM
Byzantine Dispersion on Graphs (2021)
Presentation / Conference Contribution
Molla, A. R., Mondal, K., & Moses Jr., W. K. (2021, December). Byzantine Dispersion on Graphs. Presented at 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Quickest Change Detection in the Presence of Transient Adversarial Attacks (2021)
Presentation / Conference Contribution
Vasantam, T., Towsley, D., & Veeravalli, V. V. (2021, December). Quickest Change Detection in the Presence of Transient Adversarial Attacks. Presented at 2021 55th Annual Conference on Information Sciences and Systems (CISS)
7th International Conference on Systems and Informatics (2021)
Presentation / Conference Contribution
(2021, November). 7th International Conference on Systems and Informatics. Presented at 7th International Conference on Systems and Informatics, Chongqing, China
Bi-projection-based Foreground-aware Omnidirectional Depth Prediction (2021)
Presentation / Conference Contribution
Feng, Q., Shum, H. P., & Morishima, S. (2023, September). Bi-projection-based Foreground-aware Omnidirectional Depth Prediction. Presented at Visual Computing 2021, OnlineDue to the increasing availability of commercial 360- degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality. Regarding existing methods, so... Read More about Bi-projection-based Foreground-aware Omnidirectional Depth Prediction.
Modeling Autonomic Systems in the time of ML, DevOps and Microservices (2021)
Presentation / Conference Contribution
Bencomo, N. (2021, December). Modeling Autonomic Systems in the time of ML, DevOps and Microservices. Presented at ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS 2021 Companion, Fukuoka, Japan, October 10-15, 2021
Efficient Deterministic Leader Election for Programmable Matter (2021)
Presentation / Conference Contribution
Dufoulon, F., Kutten, S., & Moses Jr., W. K. (2021, December). Efficient Deterministic Leader Election for Programmable Matter. Presented at Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing