Institution profile

Lancaster University

Academic institutioneurope · gb
Official website
Research library349linked papers
Opportunities0open roles
Selected work

Representative Papers

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI

Aug 01, 2023ACM J. Comput. Sustain. Soc.

Energy savings from smart home technologies are often undermined by rebound effects—behavioral or systemic compensations triggered by increased efficiency—rendering sustainability gains transient. Method: Through a cross-disciplinary literature mapping analysis across Web of Science, Scopus, IEEE Xplore, Springer, and ACM SIGCHI proceedings, this study systematically identifies research gaps concerning rebound effects in computing, human-computer interaction (HCI), and smart home domains. Contribution/Results: We propose the first classification framework for rebound effects tailored to sustainable HCI, along with corresponding intervention pathways. Findings reveal that current energy-efficiency evaluations routinely neglect rebound mechanisms, while HCI is uniquely positioned to advance rebound identification, computational modeling, and behaviorally informed interventions. This work establishes a theoretical foundation and methodological toolkit for accurately assessing the real-world environmental impact of smart home systems.

12 citations1 influentialRead paper

Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

Jan 03, 2024arXiv.org

Existing VideoQA datasets lack fine-grained modeling of professional sports actions, hindering effective reasoning for descriptive, temporal, causal, and counterfactual questions. To address this, we introduce Sports-QA—the first video question answering benchmark tailored to professional sports scenarios—covering multiple sports disciplines and four categories of complex reasoning tasks. Methodologically, we propose the Auto-Focus Transformer (AFT), which employs an attention-driven dynamic focusing mechanism to adaptively model multi-scale temporal information and integrates joint video–language representation learning. Extensive experiments demonstrate that AFT achieves state-of-the-art performance on Sports-QA, substantially outperforming general-purpose VideoQA models. This work constitutes the first systematic validation of an architecture explicitly designed for fine-grained sports action understanding and dynamic logical reasoning, establishing a new foundation for domain-specific VideoQA research.

10 citations2 influentialRead paper

On Efficient Variants of Segment Anything Model: A Survey

Oct 07, 2024arXiv.org

While the Segment Anything Model (SAM) exhibits strong generalization capability, its substantial computational overhead hinders deployment on resource-constrained edge devices. This work presents a systematic survey of efficient SAM variants tailored for edge deployment. We introduce the first unified evaluation framework spanning diverse hardware platforms—including CPU, GPU, and Edge TPU—and conduct joint accuracy–latency–memory benchmarking on COCO and SA-1B. Our analysis categorizes acceleration techniques along six technical axes: model pruning, knowledge distillation, lightweight attention mechanisms, quantization, module substitution, and hardware-aware compilation—characterizing their Pareto-optimal trade-offs. The core contributions are: (1) an open-source, fully reproducible edge-SAM benchmark; and (2) empirical insights into the applicability domains and fundamental accuracy-efficiency trade-offs of each acceleration strategy—providing both theoretical foundations and practical guidelines for designing lightweight vision foundation models.

7 citationsRead paper

FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions

Jan 08, 2026arXiv.org

This study addresses a critical limitation in current electrospinning modeling approaches, which predict only the average fiber diameter while neglecting the full diameter distribution that profoundly influences scaffold performance. To overcome this, we propose the first distribution-aware machine learning framework capable of accurately predicting the complete fiber diameter spectrum from standard process parameters. Seven models were rigorously trained using nested cross-validation and a leave-one-study-out strategy, and their predictions were interpreted through an integrated multi-dimensional explainability analysis combining SHAP values, variable importance, and three-dimensional parameter maps. Evaluated across multiple biopolymers, nonlinear models achieved R² values exceeding 0.91, with experimental validation confirming strong agreement between predicted and measured diameter distributions, thereby significantly advancing data-driven optimization of scaffold architecture.

1 citationsRead paper

TSTMotion: Training-free Scene-awarenText-to-motion Generation

May 02, 2025

Existing scene-aware text-to-motion generation methods rely on large-scale real motion datasets annotated with scene information, incurring high acquisition costs and exhibiting poor generalization. This paper introduces the first zero-shot, fine-tuning-free scene-aware framework that requires no paired real motion data conditioned on scenes: given only a 3D scene and a textual description, it synthesizes human motion sequences geometrically and semantically consistent with the environment. Our method leverages collaborative reasoning among multimodal foundation models to generate verifiable motion guidance signals; these are integrated via motion-guided injection and latent-space reparameterization to enable zero-shot scene-constrained modeling. Experiments on diverse complex 3D scenes demonstrate substantial improvements in motion plausibility and scene alignment. Moreover, our framework supports plug-and-play integration with mainstream text-to-motion models. Code is publicly available.

1 citationsRead paper
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