Outputs (200)

Natural Language Explanations for Machine Learning Classification Decisions (2023)
Presentation / Conference Contribution
Burton, J., Al Moubayed, N., & Enshaei, A. (2023, June). Natural Language Explanations for Machine Learning Classification Decisions. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

This paper addresses the challenge of providing understandable explanations for machine learning classification decisions. To do this, we introduce a dataset of expert-written textual explanations paired with numerical explanations, forming a data-to... Read More about Natural Language Explanations for Machine Learning Classification Decisions.

Distributed MIS in O(log log n) Awake Complexity (2023)
Presentation / Conference Contribution
Dufoulon, F., Moses Jr., W. K., & Pandurangan, G. (2023, June). Distributed MIS in O(log log n) Awake Complexity. Presented at PODC '23: 2023 ACM Symposium on Principles of Distributed Computing, Orlando, Florida

Maximal Independent Set (MIS) is one of the fundamental and most well-studied problems in distributed graph algorithms. Even after four decades of intensive research, the best known (randomized) MIS algorithms have O(log n) round complexity on genera... Read More about Distributed MIS in O(log log n) Awake Complexity.

Uniting General-Graph and Geometric-Based Radio Networks via Independence Number Parametrization (2023)
Presentation / Conference Contribution
Davies, P. (2023, June). Uniting General-Graph and Geometric-Based Radio Networks via Independence Number Parametrization. Presented at PODC 2023: ACM Symposium on Principles of Distributed Computing, Orlando, Florida

In the study of radio networks, the tasks of broadcasting (propagating a message throughout the network) and leader election (having the network agree on a node to designate ‘leader’) are two of the most fundamental global problems, and have a long h... Read More about Uniting General-Graph and Geometric-Based Radio Networks via Independence Number Parametrization.

Optimal Message-Passing with Noisy Beeps (2023)
Presentation / Conference Contribution
Davies, P. (2023, June). Optimal Message-Passing with Noisy Beeps. Presented at PODC 2023: ACM Symposium on Principles of Distributed Computing, Orlando, Florida

Beeping models are models for networks of weak devices, such as sensor networks or biological networks. In these networks, nodes are allowed to communicate only via emitting beeps: unary pulses of energy. Listening nodes only the capability of carrie... Read More about Optimal Message-Passing with Noisy Beeps.

Health-5G: A Mixed Reality-Based System for Remote Medical Assistance in Emergency Situations (2023)
Journal Article
García, F. M., Moraleda, R., Schez-Sobrino, S., Monekosso, D. N., Vallejo, D., & Glez-Morcillo, C. (2023). Health-5G: A Mixed Reality-Based System for Remote Medical Assistance in Emergency Situations. IEEE Access, 11, https://doi.org/10.1109/ACCESS.2023.3285420

Mixed reality is the combination of virtual and augmented reality to interactively and believably merge physical and computer-generated environments. This paper discusses the design of Health-5G, a scalable mixed reality-based system that facilitates... Read More about Health-5G: A Mixed Reality-Based System for Remote Medical Assistance in Emergency Situations.

The Complexity of Matching Games: A Survey (2023)
Journal Article
Benedek, M., Biro, P., Johnson, M., Paulusma, D., & Ye, X. (2023). The Complexity of Matching Games: A Survey. Journal of Artificial Intelligence Research, 77, 459-485. https://doi.org/10.1613/jair.1.14281

Matching games naturally generalize assignment games, a well-known class of cooperative games. Interest in matching games has grown recently due to some breakthrough results and new applications. This state-of-the-art survey provides an overview of m... Read More about The Complexity of Matching Games: A Survey.

Compliance Checking of Cloud Providers: Design and Implementation (2023)
Journal Article
Barati, M., Adu-Duodu, K., Rana, O., Aujla, G. S., & Ranjan, R. (2023). Compliance Checking of Cloud Providers: Design and Implementation. Distributed Ledger Technologies: Research and Practice, 2(2), 1-20. https://doi.org/10.1145/3585538

The recognition of capabilities supplied by cloud systems is presently growing. Collecting or sharing healthcare data and sensitive information especially during the Covid-19 pandemic has motivated organizations and enterprises to leverage the upside... Read More about Compliance Checking of Cloud Providers: Design and Implementation.

Finding Matching Cuts in H-Free Graphs (2023)
Journal Article
Lucke, F., Paulusma, D., & Ries, B. (2023). Finding Matching Cuts in H-Free Graphs. Algorithmica, 85(10), 3290-3322. https://doi.org/10.1007/s00453-023-01137-9

The well-known NP-complete problem MATCHING CUT is to decide if a graph has a matching that is also an edge cut of the graph. We prove new complexity results for MATCHING CUT restricted to H-free graphs, that is, graphs that do not contain some fixed... Read More about Finding Matching Cuts in H-Free Graphs.

GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment (2023)
Journal Article
Ahmad, H., & Aujla, G. S. (2023). GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment. Computers and Electrical Engineering, 109, https://doi.org/10.1016/j.compeleceng.2023.108747

With cloud-hosted web applications becoming ubiquitous, the security risks presented for user personal data that is migrated to the cloud are at an all-time high. When using a cloud-hosted web application, users only ever interact with web interfaces... Read More about GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment.

Hierarchical Graph Convolutional Networks for Action Quality Assessment (2023)
Journal Article
Zhou, K., Ma, Y., Shum, H. P., & Liang, X. (online). Hierarchical Graph Convolutional Networks for Action Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, 33(12), 7749 - 7763. https://doi.org/10.1109/TCSVT.2023.3281413

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing work... Read More about Hierarchical Graph Convolutional Networks for Action Quality Assessment.