http://bura.brunel.ac.uk/handle/2438/8630 Thu, 26 Mar 2026 06:43:59 GMT 2026-03-26T06:43:59Z http://bura.brunel.ac.uk/handle/2438/33018 Title: Dances with robots: navigating power imbalances in behavioural signal exchanges Authors: Hone, K Abstract: This paper examines the role of non-verbal social signals in human–robot interaction, drawing on established findings from human communication research and recent developments in automated social signal processing. I argue that current regulatory approaches, particularly within the EU AI Act, insufficiently address the way robots may use behavioural signals to influence interaction outcomes. Mon, 16 Mar 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33018 2026-03-16T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33017 Title: A Fully Transformer-Based Multimodal Framework for Explainable Breast Cancer Image Segmentation Using Radiology Reports Authors: Adahada, E; Sassoon, I; Hone, K; Li, Y Abstract: We introduce Med-CTX, a fully transformer based multimodal framework for explainable breast cancer ultrasound segmentation. We integrate clinical radiology reports to boost both performance and interpretability. Med-CTX achieves exact lesion delineation by using a dual-branch visual encoder that combines ViT and Swin transformers, as well as uncertainty aware fusion. Clinical language structured with BI-RADS semantics is encoded by BioClinicalBERT and combined with visual features utilising cross-modal attention, allowing the model to provide clinically grounded, model generated explanations. Our methodology generates segmentation masks, uncertainty maps, and diagnostic rationales all at once, increasing confidence and transparency in computer assisted diagnosis. On the BUS-BRA dataset, Med-CTX achieves a Dice score of 90% and an IoU of 82.7%, beating existing baselines U-Net, ViT, and Swin. Clinical text plays a key role in segmentation accuracy and explanation quality, as evidenced by ablation studies that show a-5.4% decline in Dice score and-31% in CIDEr. Med-CTX achieves good multimodal alignment (CLIP score: 85%) and increased confidence calibration (ECE: 3.2%), setting a new bar for trustworthy, multimodal medical architecture. Fri, 12 Sep 2025 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33017 2025-09-12T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33015 Title: “Estimating Software Project Effort Using Analogies”: Reflections After 28 Years Authors: Shepperd, M Abstract: This invited paper is the result of an invitation to write a retrospective article on a “TSE most influential paper” as part of the journal's 50th anniversary. The objective is to reflect on the progress of software engineering prediction research using the lens of a selected, highly cited research paper and 28 years of hindsight. The paper examines (i) what was achieved, (ii) what has endured and (iii) what could have been done differently with the benefit of retrospection. While many specifics of software project effort prediction have evolved, key methodological issues remain relevant. The original study emphasised empirical validation with benchmarks, out-of-sample testing and data/tool sharing. Four areas for improvement are identified: (i) stronger commitment to Open Science principles, (ii) focus on effect sizes and confidence intervals, (iii) reporting variability alongside typical results and (iv) more rigorous examination of threats to validity. Description: A version of the article is available at arXiv:2501.14582v2 [cs.SE] (https://arxiv.org/abs/2501.14582). Comments: 5 pages, invited and accepted IEEE TSE paper for the journal's 50th year anniversary on most influential papers. (This version corrects three typos.). Submission history From: Martin Shepperd: [v1] Fri, 24 Jan 2025 15:44:25 UTC (14 KB); [v2] Thu, 30 Jan 2025 16:44:38 UTC (14 KB). Tue, 27 Jan 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33015 2026-01-27T00:00:00Z http://bura.brunel.ac.uk/handle/2438/32974 Title: Advancing Sustainable Agricultural Practices in Africa with AI: Interdisciplinary Approaches to Inclusivity and Resilience Authors: Abdulhamid, NG; Ogunyemi, A; Perry, M; Bauters, M; Rephisti, J; Sam, S; Maina, SC; Ocheing, M; Muchai, M; Nyairo, S; Gandhi, R; O'Neill, J Abstract: Artificial intelligence (AI) is increasingly positioned as a transformative tool in agriculture, yet existing solutions primarily cater to large-scale farms in the Global North, often overlooking the socio-cultural and infrastructural realities of smallholder farmers in Africa. This workshop interrogates how AI can be reimagined to enhance sustainability and resilience in African agriculture by centering farmer agency, cultural knowledge and community and social practice. Building on HCI and CSCW scholarship, we bring together researchers, AI practitioners, NGOs, agronomists, and community stakeholders to explore locally grounded, inclusive, and ethically responsible AI applications. Key themes include trust and skepticism in AI, the role of local languages and epistemologies in model design, strategies for decolonizing AI development and integrating indigenous knowledge and the application of methodologies such as co-design, and participatory AI. The workshop directly aligns with AfriCHI 2025’s theme, "Re-centering African Wisdom in HCI," by fostering interdisciplinary dialogue that embeds African perspectives into AI research and practice. We welcome a variety of contributions including papers, case studies and hands-on demonstrations. Outcomes include a collaboratively developed research agenda, and a white paper synthesizing insights from the workshop. By prioritizing African epistemologies and farmer-centered innovation, this workshop aims to shift AI discourse, ensuring that AI-driven agricultural technologies are not only technically robust but also culturally resonant and socially just. Tue, 04 Nov 2025 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/32974 2025-11-04T00:00:00Z