Skip to main content

Research Repository

Advanced Search

All Outputs (122)

Simulating People Dynamics (2021)
Presentation / Conference Contribution
Saeed, R., Recupero, D. R., & Remagnino, P. (2021, December). Simulating People Dynamics. Presented at 2021 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE) IEEE; Middlesex Univ London; Sapienza Univ Roma; Univ Grenoble Alpes; Queensland Univ Technol; IEEE Syst Man \& Cybernet Soc; IOS Press; Assoc Advancement Artificial Intelligence; Technol

Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction (2021)
Presentation / Conference Contribution
Rainbow, B. A., Men, Q., & Shum, H. P. (2021, October). Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction. Presented at 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia

Predicting the movement trajectories of multiple classes of road users in real-world scenarios is a challenging task due to the diverse trajectory patterns. While recent works of pedestrian trajectory prediction successfully modelled the influence of... Read More about Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction.

Evidence for Teaching Practices that Broaden Participation for Women in Computing (2021)
Presentation / Conference Contribution
Morrison, B. B., Quinn, B. A., Bradley, S., Buffardi, K., Harrington, B., Hu, H. H., Kallia, M., McNeill, F., Ola, O., Parker, M., Rosato, J., & Waite, J. (2021, December). Evidence for Teaching Practices that Broaden Participation for Women in Computing. Presented at Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education

Computing has, for many years, been one of the least demographically diverse STEM fields, particularly in terms of women's participation [12, 36]. The last decade has seen a proliferation of research exploring new teaching techniques and their effect... Read More about Evidence for Teaching Practices that Broaden Participation for Women in Computing.

Investigating the Potential Impact of Values on Requirements and Software Engineering (2021)
Presentation / Conference Contribution
Sutcliffe, A. G., Sawyer, P., Liu, W., & Bencomo, N. (2021, December). Investigating the Potential Impact of Values on Requirements and Software Engineering. Presented at 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Society, ICSE (SEIS) 2021, Madrid, Spain, May 25-28, 2021

Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App (2021)
Presentation / Conference Contribution
Paucar, L. H. G., Bencomo, N., Sutcliffe, A. G., Yue, T., & Mirakhorli, M. (2021, December). Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. Presented at 29th IEEE International Requirements Engineering Conference Workshops, RE 2021 Workshops, Notre Dame, IN, USA, September 20-24, 2021

Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead (2021)
Presentation / Conference Contribution
Bucchiarone, A., Cicchetti, A., Bencomo, N., Loria, E., & Marconi, A. (2021, December). Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead. Presented at 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2021, Madrid, Spain, May 18-24, 2021

From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems (2021)
Presentation / Conference Contribution
Ullauri, J. M. P., García-Domínguez, A., & Bencomo, N. (2021, December). From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems. Presented at ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS 2021 Companion, Fukuoka, Japan, October 10-15, 2021

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications (2021)
Presentation / Conference Contribution
Li, L., Ismail, K. N., Shum, H. P., & Breckon, T. P. (2021, December). DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. Presented at International Conference on 3D Vision, Surrey / Online

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq... Read More about DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications.

An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks (2021)
Presentation / Conference Contribution
Abdollahi, M., Malekinasab, K., Tu, W., & Bag-Mohammadi, M. (2021, December). An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks. Presented at 2021 IEEE 46th Conference on Local Computer Networks (LCN)

An active jammer could severely degrade the communication quality for wireless networks. Since all wireless nodes openly access the shared media, the harsh effects are exaggerated by retransmission attempts of affected devices. Fast and precise detec... Read More about An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks.

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

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

Pri-AwaRE: Tool Support for priority-aware decision-making under uncertainty (2021)
Presentation / Conference Contribution
Samin, H., Bencomo, N., & Sawyer, P. (2021, December). Pri-AwaRE: Tool Support for priority-aware decision-making under uncertainty. Presented at 29th IEEE International Requirements Engineering Conference, RE 2021, Notre Dame, IN, USA, September 20-24, 2021

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

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, Taiwan

This 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.

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, Online

Due 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.

Gradient Origin Networks (2021)
Presentation / Conference Contribution
Bond-Taylor, S., & Willcocks, C. G. (2021, May). Gradient Origin Networks. Presented at International Conference on Learning Representations, Vienna / Virtual

This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder. This is achieved using empirical Bayes to calculate the expectation of the posterior, which is implemented by initialising a... Read More about Gradient Origin Networks.

Singularly Near Optimal Leader Election in Asynchronous Networks (2021)
Presentation / Conference Contribution
Kutten, S., Moses Jr., W. K., Pandurangan, G., & Peleg, D. (2021, December). Singularly Near Optimal Leader Election in Asynchronous Networks. Presented at 35th International Symposium on Distributed Computing (DISC 2021)