Abstract
Breast cancer locoregional treatment includes a wide variety of procedures with diverse aesthetic outcomes. The aesthetic assessment of such procedures is typically subjective, hindering the fair comparison between their outcomes, and consequently restricting evidence-based improvements. Most objective evaluation tools were developed for conservative surgery, focusing on asymmetries while ignoring other relevant traits. To overcome these limitations, we propose SiameseOrdinalCLIP, an ordinal classification network based on image-text matching and pairwise ranking optimisation for the aesthetic evaluation of breast cancer treatment. Furthermore, we integrate a concept bottleneck module into the network for increased explainability. Experiments on a private dataset show that the proposed model surpasses the state-of-the-art aesthetic evaluation and ordinal classification networks.

Abstract
As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.

Abstract
One of the main challenges in modern industrial environments is managing the large amount of physical documentation obtained during the production process. Companies increasingly seek to adopt paperless alternatives to promote production efficiency and reduce their industrial environmental impact. On the shop floor, each production line relies on standardised forms to verify parameters and conditions before and after production begins; however, the large volume of paper documentation generated from these records led to the need to develop a digital platform capable of streamlining and digitising forms, enhancing process sustainability and efficiency. The proposed interoperable web application provides various features that allow users to create, customise, submit and approve forms digitally. It also integrates automated notifications and alerts for specific situations, enabling more effective responses to the production process's momentary needs. By unifying all processes related to forms management within a digital infrastructure, this solution aligns with the current industrial paradigm, reducing reliance on paper, optimising workflow efficiency, and incorporating innovative and industrial advancements.

Abstract
This survey provides a comprehensive overview of Video Anomaly Detection (VAD), focusing on identifying robust and interpretable methods for detecting anomalous events. It emphasizes the limitations of traditional supervised and unsupervised approaches and highlights the advantages of weakly supervised learning, in which models operate with minimal or no explicit anomaly annotations. A key contribution of this survey is its analysis of multi-modal data - integrating visual, audio, and textual modalities - for enhanced anomaly detection. It underscores how audio cues enrich visual features and how textual information during training fosters semantically richer representations. This multi-modal approach demonstrates improved generalization, better detection of subtle anomalies, and interpretable explanations of detected events, marking a paradigm shift in the field, Video Anomaly Understanding (VAU). Synthesizing advancements from benchmarks to methodologies, this work advocates for a centralized platform to enable systematic comparisons across diverse datasets, standardized evaluation metrics, and reproducible ablation studies of novel components. Such a framework would streamline the integration of innovations, address version control and foster transparency, bridging the gap between isolated methodological advances and system-level robustness. By prioritizing contextual understanding, causal reasoning, and real-world interpretability, this initiative aims to elevate weakly supervised VAD beyond detection, ensuring models contextualize and explain anomalies in practical deployments.

Abstract
The increasing integration of artificial intelligence (AI) within software engineering is reshaping the profession, leading to opportunities for innovation and productivity. However, while its technical advantages are clear, it is essential to consider the psychological effects AI may have. As software engineers play a crucial role in software development, any impact on their mental well-being can profoundly affect the pace and quality of work. This study aims to investigate the psychological effects of AI adoption on software engineers, focusing on their emotional well-being, motivation, and the broader implications for their professional identity. We conducted a qualitative survey based on online documents, such as blog posts, to identify the diversity of software developers' impressions on using AI for software engineering. We analyzed the selected documents using thematic analysis. The analysis revealed that rather than a single and uniform perception, there is a duality of positive and negative affects towards AI, which could manifest even in the same individual. These affects are generally associated with experiences that also can be positive and negative. These affects can have a profound impact on the motivation and psychological well-being of software developers. Our results provide evidence that practitioners are excited about the increasing use of AI tools, but, at the same time, they fear the consequences of this change leading to negative feelings, such as sadness. These results indicate the need for further research on preparing practitioners for this new wave of changes.

Abstract
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just in 2024. Most studies use publicly available datasets of H&E-IHC pairs from consecutive tissue sections. Recognizing the training challenges, many authors develop complex virtual staining models based on conditional Generative Adversarial Networks but ignore the impact of adversarial loss on the quality of virtual staining. Furthermore, overlooking the issues of model evaluation, they claim improved performance based on metrics such as SSIM and PSNR, which are not sufficiently robust to evaluate the quality of virtually stained images. In this paper, we developed CSSP2P GAN, which we demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation. Furthermore, while iteratively developing our model, we study the impact of the adversarial loss and demonstrate its crucial role in the quality of virtually stained images. Finally, while comparing our model with reference works in the field, we underscore the limitations of the currently used evaluation metrics and demonstrate the superior performance of CSSP2P GAN.