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All Outputs (20)

Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities (2025)
Presentation / Conference Contribution
Bruun, A., Van Berkel, N., Raptis, D., & Law, E. L.-C. (2025, April). Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities. Presented at CHI 2025 (Conference on Human Factors in Computing Systems), Yokohama, Japan

Software development relies on collaboration and alignment between a variety of roles, including software developers and user experience designers. The increasing focus on artificial intelligence in today's development projects has given rise to new... Read More about Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities.

SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection (2025)
Presentation / Conference Contribution
Maviya, S., Arnau-González, P., Arevalillo-Herráez, M., & Katsigiannis, S. (2025, February). SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection. Presented at De-factify 4.0 Workshop at 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA

A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments (2025)
Presentation / Conference Contribution
Zhang, K., Zahmatkesh, M., Stefanec, M., Arvin, F., & Hu, J. (2025, March). A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments. Presented at 2025 IEEE International Conference on Industrial Technology, China

This paper proposes a real-time multi-robot navigation method that integrates the Rapidly-exploring Random Tree (RRT) algorithm with the improved Artificial Potential Field (APF) approach. Since traditional path planning methods often face problems s... Read More about A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments.

Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles (2025)
Presentation / Conference Contribution
Aihaiti, A., Arvin, F., & Hu, J. (2025, March). Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles. Presented at 2025 IEEE International Conference on Industrial Technology, Wuhan, China

The safe overtaking of autonomous vehicles has become an important focus in recent robotics and AI research. Considering the scenario of overtaking with oncoming vehicles, this paper proposes a hierarchical framework based on deep reinforcement learn... Read More about Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles.

On the Locality of the Lovász Local Lemma (2025)
Presentation / Conference Contribution
Davies-Peck, P. (2025, June). On the Locality of the Lovász Local Lemma. Presented at 57th Annual ACM Symposium on Theory of Computing (STOC '25), Prague

The Lovász Local Lemma is a versatile result in probability theory, characterizing circumstances in which a collection of n ‘bad events’, each occurring with probability at most p and dependent on a set
of underlying random variables, can be avoided... Read More about On the Locality of the Lovász Local Lemma.

NP-completeness of the combinatorial distance matrix realisation problem (2025)
Presentation / Conference Contribution
Fairbairn, D., Mertzios, G., & Peyerimhoff, N. (2025, December). NP-completeness of the combinatorial distance matrix realisation problem. Presented at 14th International Symposium on Algorithms and Complexity (CIAC 2025), Rome, Italy

The k-CombDMR problem is that of determining whether an n×n distance matrix can be realised by n vertices in some undirected graph with n+k vertices. This problem has a simple solution in the case k=0. In this paper we show that this problem is polyn... Read More about NP-completeness of the combinatorial distance matrix realisation problem.

Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving (2025)
Presentation / Conference Contribution
E, W., Yuan, C., Sun, Y., Gaus, Y., Atapour-Abarghouei, A., & Breckon, T. (2025, May). Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving. Presented at IEEE International Conference on Robotics and Automation (ICRA), Atlanta, USA

We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a benchmark architecture designed to generate Bird-Eye-View (BEV) maps using... Read More about Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving.

Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots (2025)
Presentation / Conference Contribution
Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (2025, May). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots c... Read More about Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots.

Introducing Code Quality at CS1 Level: Examples and Activities (2025)
Presentation / Conference Contribution
Izu, C., Mirolo, C., Börstler, J., Connamacher, H., Crosby, R., Glassey, R., Haldeman, G., Kiljunen, O., Kumar, A. N., Liu, D., Luxton-Reilly, A., Matsumoto, S., Carneiro De Oliveira, E., Russell, S., Shah, A., Izu, C., Mirolo, C., Börstler, J., Connamacher, H., Crosby, R., …Shah, A. (2024, July). Introducing Code Quality at CS1 Level: Examples and Activities. Presented at ITiCSE 2024: Innovation and Technology in Computer Science Education, Milan

Characterising code quality is a challenge that was addressed by a previous ITiCSE Working Group (Börstler et al., 2017). As emerged from that study, educators, developers, and students have different perceptions of the aspects involved. The percepti... Read More about Introducing Code Quality at CS1 Level: Examples and Activities.

Sparse Autoencoders Do Not Find Canonical Units of Analysis (2025)
Presentation / Conference Contribution
Leask, P., Bussmann, B., Pearce, M. T., Isaac Bloom, J., Tigges, C., Al Moubayed, N., Sharkey, L., & Nanda, N. (2025, April). Sparse Autoencoders Do Not Find Canonical Units of Analysis. Presented at The Thirteenth International Conference on Learning Representations, Singapore

A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse autoencoders (SAEs) are a popular method for finding these features in... Read More about Sparse Autoencoders Do Not Find Canonical Units of Analysis.

The Complexity of Diameter on H-free Graphs (2025)
Presentation / Conference Contribution
Oostveen, J. J., Paulusma, D., & van Leeuwen, E. J. (2024, June). The Complexity of Diameter on H-free Graphs. Presented at WG 2024, Gozd Martuljek, Slovenia

Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems (2025)
Presentation / Conference Contribution
Demirbaga, U., Singh Aujla, G., & Sun, H. (2025, June). Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

As the scale of data continues to grow exponentially, managing resource allocation and energy consumption in big data systems becomes increasingly complex and critical. Moreover, with big data systems, energy efficiency is more important daily. In cl... Read More about Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems.

COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks (2025)
Presentation / Conference Contribution
Singh Aujla, G., Jindal, A., Kaur, K., Garg, S., Chaudhary, R., Sun, H., & Kumar, N. (2025, June). COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency o... Read More about COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks.

Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning (2025)
Presentation / Conference Contribution
Milton, T., & Lieck, R. (2024, December). Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning. Presented at 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal

Type 1 diabetes is a chronic metabolic disease that requires regular insulin injections to regulate blood glucose levels. Recently, traditional manual approaches to diabetes management have been revolutionized by the use of continuous glucose monitor... Read More about Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning.

An on-demand resource allocation algorithm for a quantum network hub and its performance analysis (2025)
Presentation / Conference Contribution
Gauthier, S., Vasantam, T., & Vardoyan, G. (2024, September). An on-demand resource allocation algorithm for a quantum network hub and its performance analysis. Presented at QCE24: IEEE International Conference on Quantum Computing and Engineering, Montréal, Québec, Canada

To support the execution of multiple simultaneously-running quantum network applications, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch... Read More about An on-demand resource allocation algorithm for a quantum network hub and its performance analysis.

Calculating the Capacity Region of a Quantum Switch (2025)
Presentation / Conference Contribution
Tillman, I., Vasantam, T., Towsley, D., & Seshadreesan, K. P. (2024, September). Calculating the Capacity Region of a Quantum Switch. Presented at QCE2024: IEEE International Conference on Quantum Computing and Engineering, Montréal, Québec, Canada

Quantum repeaters are necessary to fully realize the capabilities of the emerging quantum internet, especially applications involving distributing entanglement across long distances. A more general notion of this can be called a quantum switch, which... Read More about Calculating the Capacity Region of a Quantum Switch.