We present Graph Neural ODEs (GNODEs) for modeling tumor microenvironment dynamics with mathematically guaranteed stability and conservation properties. Unlike bulk ODEs that miss spatial heterogeneity or discrete GNNs that inadequately capture continuous biological processes, GNODEs provide continuous-time evolution with explicit adjacency-aware dynamics while maintaining provable trajectory bounds. Our framework ensures: (1) existence and uniqueness of solutions under dynamic graph topology, (2) Lyapunov stability preventing unphysical states like negative cell counts, and (3) exact conservation of biological invariants through architectural constraints. Benchmarking on synthetic tumor data demonstrates that GNODE accurately captures the dynamics of the resistant cell fraction (0.282 predicted vs 0.242 true), whereas graph-free alternatives fail completely (0.000), underscoring the importance of stability-constrained local interactions for modeling emergent resistance.

Computational notebooks are the de facto platform for exploratory data science, offering an interactive programming environment where users can create, modify, and execute code cells in any sequence. However, this flexibility often introduces code quality issues, with prior studies showing that approximately $76 \%$ of public notebooks are non-executable, raising significant concerns about reusability. We argue that the traditional notion of executability—requiring a notebook to run fully and without error—is overly rigid, misclassifying many notebooks and overestimating their non-executability. This paper investigates pathological executability issues in public notebooks under varying notions and degrees of executability. Notebooks, by construction, are incrementally and interactively executed, where each cell execution advances logic toward the notebook’s goal. Even partially improving executability can improve code comprehension and offer a pathway for dynamic analyses. With this insight, we first categorize notebooks into potentially restorable and pathological non-executable notebooks and then measure how removing misconfiguration and superficial execution issues in notebooks can improve their executability (i.e., additional cells executed without error). For instance, we use a Large Language Model (LLM) to generate synthetic input data to restore non-executable notebooks with “FileNotFound” errors. In a dataset of 42,546 popular public notebooks, containing 34,659 non-executable notebooks, only $21.3 \%$ are truly pathologically non-executable. For restorable notebooks, LLM-based methods fully restore $5.4 \%$ of previously nonexecutable notebooks. Among the partially restored, it improves the notebooks’ executability by $\mathbf{4 0. 5 \%}$ and $\mathbf{2 8 \%}$ by installing the correct modules and generating synthetic data. These findings challenge prior assumptions, suggesting that notebooks have higher executability than previously reported, many of which offer valuable partial execution, and that their executability should be evaluated within the interactive notebook paradigm rather than through traditional software executability standards.

Whole slide images (WSIs) are fundamental components of modern pathology, aiding pathologists in diagnosing diseases such as cancer. However, artefacts such as blurry regions, folded tissue, or uneven staining, often introduced during biopsy or slide preparation, can affect the accuracy and efficiency of the diagnostic process. Detecting these artefacts is critical to ensure delivering acceptable image quality, guiding pathologists toward diagnostically relevant regions, and improving reliability. In recent years, deep learning has increasingly complemented traditional quality control procedures by enabling rapid detection and localization of such artefacts. This progress, however, depends on the availability of high-quality datasets for training, validation, and testing of the detection models. To support this need, we introduce a comprehensive benchmark dataset for artefact detection obtained from two separate sources, consisting of selected WSIs from The Cancer Genome Atlas (TCGA), as well as WSIs provided by Universitair Ziekenhuis Brussel (UZB). The collected WSIs were subdivided into smaller tiles and annotated by artefact type, making them well-suited for deep learning model development. We hope this dataset serves as a valuable foundation for researchers developing tools to enhance WSI quality and diagnostic accuracy. The dataset is publicly available at: https://gitlab.com/etrovub/interfere/pathodataset25.

In recent years, there has been a growing interest in and focus on the automatic detection of deceptive behavior. This attention is justified by the wide range of applications that deception detection can have, especially in fields such as criminology. This study specifically aims to contribute to the field of deception detection by capturing transcribed data, analyzing textual data using Natural Language Processing (NLP) techniques, and comparing the performance of conventional models using linguistic features with the performance of Large Language Models (LLMs). In addition, the significance of applied linguistic features has been examined using different feature selection techniques. Through extensive experiments, we evaluated the effectiveness of both conventional and deep NLP models in detecting deception from speech. Applying different models to the Real-Life Trial dataset, a single layer of Bidirectional Long Short-Term Memory (BiLSTM) tuned by early stopping outperformed the other models. This model achieved an accuracy of 93.57% and an F1 score of 94.48%.

Cloud services allow one to perform intense big data calculations without having to own personally a powerful enough machine. Different cloud-based virtual machines, however, offer different processor speeds at different costs, and the most cost-effective machine size may not always be obvious. We investigated different virtual machine sizes on the Microsoft Azure cloud service and also different data exploration methodologies to solve a big data prediction project using Neural Networks. It was found that one may not always get proportionally better performance with higher end expensive virtual machine settings. Direct application of Neural Network on prediction problem typically has a bottleneck in performance. We found the learning and prediction can be made better with data properties and problem nature taken into consideration. Some of our data preparation schemes will be useful for general big data prediction problem with noise or non-uniformly distributed data.

Requirements models are essential not just during system implementation, but also to manage system changes post-implementation. Such models should be supported by a requirements model management framework that allows users to create, manage and evolve models of domains, requirements, code and other design-time artifacts along with traceability links between their elements. We propose a comprehensive framework which delineates the operations and elements necessary, and then describe a tool implementation which supports versioning goal models.

Nowadays, the development of complex computing devices involves a substantial and growing part of software development. A great challenge for engineers is to manage the evolution of a system with several components in the face of mounting complexity due to concurrent hardware and software development. The key limitations of existing change management tools used for the design process of complex computing systems include their inadequacy in representing semantics of design models and inability to manage changes to both hardware designs and associated software components in a cohesive manner. Thus, it is difficult to track the logical interdependencies between the changes to hardware and software components in an embedded computing system over time. This paper presents EmVC, a configuration management system for a hardware software co-design process. EmVC is an illustration of our application of a well-known software engineering approach to the management of embedded systems design artifacts. Our novel component-based change management mechanism is capable of capturing and versioning the underlying logical contents of components in system design models and their associated software artifacts in a cohesive manner.

This paper presents an interactive visualization system, named WebSearchViz, for visualizing the Web search results and acilitating users' navigation and exploration. The metaphor in our model is the solar system with its planets and asteroids revolving around the sun. Location, color, movement, and spatial distance of objects in the visual space are used to represent the semantic relationships between a query and relevant Web pages. Especially, the movement of objects and their speeds add a new dimension to the visual space, illustrating the degree of relevance among a query and Web search results in the context of users' subjects of interest. By interacting with the visual space, users are able to observe the semantic relevance between a query and a resulting Web page with respect to their subjects of interest, context information, or concern. Users' subjects of interest can be dynamically changed, redefined, added, or deleted from the visual space.