http://bura.brunel.ac.uk/handle/2438/8630 2026-06-20T09:35:55Z http://bura.brunel.ac.uk/handle/2438/33466 Title: Bridging human insight and automation: improving alt text generation with human-curated contextual data Authors: Droutsas, N; Spyridonis, F; Daylamani-Zad, D; Glass, PE; Ghinea, G Abstract: The rapid growth of image-based multimedia content on the Web has intensified the challenge of generating high-quality alternative (alt) text descriptions, which is an essential requirement for inclusive online experiences for people with visual impairments. Although recent advances in machine learning (ML) have enabled large-scale automated alt text generation, the accessibility value of such outputs remains limited. This is due to the context-agnostic datasets used to train existing models, resulting in generic descriptions that fail to meet users’ needs in alt text. In this work, we introduce and utilise a human-curated, context-driven dataset of alt text descriptions to train two proof-of-concept ML models aimed at improving alt text quality. We evaluate these models within a controlled, reproducible pipeline and demonstrate that context-aware training leads to statistically significant improvements in human-perceived alt text quality compared to a model trained without contextual inputs. We further examine the role of context-dependent routing and the integration of contextual cues in shaping generated descriptions, both of which are critical but underexplored aspects of alt text accessibility. The findings highlight the value of structured, human-curated contextual data in advancing ML-supported alt text generation and point towards opportunities for hybrid human-AI approaches to inclusive web design. Description: Data availability statement: The data collection protocol using a GWAP is currently under review for separate publication; data and detailed collection protocols will be made publicly available upon acceptance and can be provided upon reasonable request. For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) Licence to any Accepted Author Manuscript version arising from this submission. 2026-06-05T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33458 Title: COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis Authors: Naseem, U; Razzak, I; Khushi, M; Eklund, PW; Kim, J Abstract: Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts. It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest. This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media. This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter. As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020. The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers. Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March. Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic. 2021-01-29T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33435 Title: FlashSAM: Lightweight Vision Model for Multi-UAV Token Communication in Low-Altitude Wireless Networks Authors: Jiang, F; Tu, S; Dong, L; Wang, K; Yang, K; Liu, R; Pan, C; Wang, J Abstract: Token Communication (TokenCom) is a promising paradigm for low-altitude wireless networks, as it focuses on transmitting task-relevant core information, particularly in environments with uncertainty, noise, and stringent bandwidth constraints. However, existing TokenCom systems still face several challenges, including inefficient knowledge base construction, ineffective token encoding, and limited support for multi-user token sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-Unmanned Aerial Vehicle (UAV) To ken Communication (LVM-MTC) system. First, we develop a lightweight Segment Anything Model (SAM), termed FlashSAM, which incorporates a set of lightweight convolutional modules to significantly reduce the number of model parameters. Building on FlashSAM, we construct a Lightweight Knowledge Base (LKB) to enable efficient object-level perception. Next, we design an Efficient Token Codec (ETC) based on the Masked Autoencoder (MAE) architecture. ETC improves compression efficiency at both the pixel and token levels, and provides lightweight token decoding tailored for resource-constrained UAVs. Furthermore, we propose a Multi-UAV Token Sharing (MTS) scheme for multi UAV TokenCom. By measuring token similarity across UAVs, MTS consolidates similar tokens and transmits them through broadcast transmission, thereby further improving transmission efficiency. Finally, simulation results validate the feasibility and effectiveness of the proposed LVM-MTC system. 2026-05-25T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33430 Title: Understanding the search space: Investigations into the nature of software modularisation Authors: Mann, Ashley J. Abstract: A relationship exists between the functionality of software systems and their complex-ity. As the number of features implemented increases, the systems complexity also grows, accompanied by the expansion of the number of artefacts and their intricate in-terrelationships [1]. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London 2026-01-01T00:00:00Z