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Outputs (117)

Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control (2025)
Presentation / Conference Contribution
Chen, D., Hu, J., Zhang, H., & Chen, B. (2025, June). Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control. Presented at 2025 European Control Conference (ECC), Thessaloniki, Greece

Traffic signal control is essential for managing urban traffic, reducing congestion, and minimizing environmental impact by optimizing both vehicular and pedestrian flow. This paper investigates the application of Reinforcement Learning (RL) in traff... Read More about Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control.

Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow* (2025)
Presentation / Conference Contribution
Nan, F., Li, F., Wang, Z., Tam, G. K. L., Jiang, Z., DongZheng, D., & Yang, B. (2025, April). Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*. Presented at ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion es... Read More about Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*.

Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation (2025)
Journal Article
Wang, Y., Li, M., Liu, J., Leng, Z., Li, F. W. B., Zhang, Z., & Liang, X. (online). Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation. International Journal of Computer Vision, https://doi.org/10.1007/s11263-025-02392-9

We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed... Read More about Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation.

HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches (2025)
Journal Article
Chakraborty, S., Bunnam, T., Arunruerk, J., Agarwal, S., Yu, S., Shafik, R., & Sjalander, M. (online). HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, https://doi.org/10.1109/TCAD.2025.3546855

This paper proposes a comprehensive thermal modeling and simulation framework, HotReRAM, for resistive RAM (ReRAM)-based caches that is verified against a memristor circuit-level model. The simulation is driven by power traces based on cache accesses... Read More about HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches.

Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation (2025)
Journal Article
Alsayed, A., Bana, F., Arvin, F., Quinn, M. K., & Nabawy, M. R. A. (2025). Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation. Aerospace, 12(3), Article 189. https://doi.org/10.3390/aerospace12030189

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downwa... Read More about Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation.

Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems (2025)
Journal Article
Liebherr, M., Enkel, E., Law, E. L.-C., Mousavi, M. R., Sammartino, M., & Sieberg, P. (in press). Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems. International Journal on Software Tools for Technology Transfer,

Trust is a multi-faceted phenomenon traditionally studied in human relations and more recently in human-machine interactions. In the context of AI-enabled systems, trust is about the belief of the user that in a given scenario the system is going to... Read More about Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems.

Semi-supervised Speech Confidence Detection using Psuedo-labelling and Whisper Embeddings (2025)
Presentation / Conference Contribution
Wynn, A., Wang, J., & Tan, X. (2025, July). Semi-supervised Speech Confidence Detection using Psuedo-labelling and Whisper Embeddings. Presented at The 26th International Conference on Artificial Intelligence in Education, Palermo, Italy

Understanding speaker confidence is crucial in educational settings, as it can enhance personalised feedback and improve learning outcomes. This study introduces a novel framework for detecting speaker confidence by integrating human-engineered featu... Read More about Semi-supervised Speech Confidence Detection using Psuedo-labelling and Whisper Embeddings.

1-in-3 vs. Not-All-Equal: Dichotomy of a broken promise (2025)
Journal Article
Ciardo, L., Kozik, M., Krokhin, A., Nakajima, T.-V., & Živný, S. (online). 1-in-3 vs. Not-All-Equal: Dichotomy of a broken promise. ACM Transactions on Computational Logic, https://doi.org/10.1145/3719007

The 1-in-3 and Not-All-Equal satisfiability problems for Boolean CNF formulas are two well-known NP-hard problems. In contrast, the promise 1-in-3 vs. Not-All-Equal problem can be solved in polynomial time. In the present work, we investigate this co... Read More about 1-in-3 vs. Not-All-Equal: Dichotomy of a broken promise.