Halogens play a fundamental role in drug design, influencing bioactivity, stability, and selectivity. However, their impact on toxicity, particularly genotoxicity, cardiotoxicity, and hepatotoxicity, remains a critical challenge in drug discovery. This study presents HD-GEM (Hybrid Dynamic Graph-based Ensemble Model), a novel machine learning framework integrating graph neural networks, descriptor-based molecular fingerprints, and ensemble meta-learning to predict the toxicity of halogenated aromatic compounds and drug scaffolds. HD-GEM demonstrates superior predictive power compared to conventional machine learning (ML) models and popular toxicity web applications like ProTox, ADMETlab, and admetSAR, achieving high accuracy and Receiver Operating Characteristic-Area Under Curve scores across diverse datasets. Importantly, a node perturbation analysis revealed that carbon, nitrogen, and oxygen atoms within the scaffold dominate toxicity predictions, whereas halogen contributions were minimal, challenging the conventional assumption that halogenation inherently increases toxicity in many pharmacological contexts. Among halogens, iodine-substituted compounds exhibit the lowest toxicity, a trend corroborated across single-, double-, and triple-ring scaffolds. Notably, polyhalogenated scaffolds show reduced toxicity, suggesting a stabilizing effect that mitigates reactive metabolite formation. This study presents an interpretable artificial intelligence-driven framework for toxicity prediction in the context of computational toxicology and cheminformatics. Atom-level and descriptor-based analyses reveal scaffold- and feature-specific contributions to toxicity.

Abstract The rapid and wide dissemination of up‐to‐date, localized information is a central issue during disasters. Being attributed to the original 140‐character length, Twitter provides its users with quick‐posting and easy‐forwarding features that facilitate the timely dissemination of warnings and alerts. However, a concern arises with respect to the terseness of tweets that restricts the amount of information conveyed in a tweet and thus increases a tweetʼs uncertainty. We tackle such concerns by proposing entropy as a measure for a tweetʼs uncertainty. Based on the perspectives of Uncertainty Reduction Theory (URT), we theorize that the more uncertain information of a disaster tweet, the higher the entropy, which will lead to a lower retweet count. By leveraging the statistical and predictive analyses, we provide evidence supporting that entropy validly and reliably assesses the uncertainty of a tweet. This study contributes to improving our understanding of information propagation on Twitter during disasters. Academically, we offer a new variable of entropy to measure a tweetʼs uncertainty, an important factor influencing disaster tweetsʼ retweeting. Entropy plays a critical role to better comprehend URLs and emoticons as a means to convey information. Practically, this research suggests a set of guidelines for effectively crafting disaster messages on Twitter.

Past studies suggest that computer security countermeasures such as security policies, systems, and awareness programs would be effective in preventing computer abuse in organizations. They are based on the general deterrence theory, which posits that when an organization implements countermeasures that threaten abusers, its computer abuse problems would be deterred. However, computer abuse problems persist in many organizations despite these measures. This article proposes a new model of computer abuse that extends the traditional model with the social criminology theories. Focusing on computer abuse within organizations, the model explains the phenomenon through social lenses such as social bonds and social learning. The new model contributes to our theoretical body of knowledge on computer abuse by providing a new angle for approaching the problem. It suggests to practitioners that both technical and social solutions should be implemented to reduce the pervasive computer abuse problems.

Abstract Computer animation and visualization can facilitate communication between the hearing impaired and those with normal speaking capabilities. This paper presents a model of a system that is capable of translating text from a natural language into animated sign language. Techniques have been developed to analyse language and transform it into sign language in a systematic way. A hand motion coding method as applied to the hand motion representation, and control has also been investigated. Two translation examples are also given to demonstrate the practicality of the system.

We describe SIBYL, a system that supports group decision making by representing and managing the qualitative aspects of decision making processes: such as the alternatives, the goals to be satisfied, and the arguments evaluating the alternatives with respect to these goals. We use an example session with SIBYL to illustrate the language, called DRL, that SIBYL uses for representing these qualitative aspects, and the set of services that SIBYL provides using this language. We also compare SIBYL to other systems with similar objectives and discuss the additional benefits that SIBYL provides. In particular, we compare SIBYL to gIBIS, a well-known “tool for exploratory policy discussion”, and claim that SIBYL is mainly a knowledge-based system which uses a semi-formal representation, whereas gIBIS is mainly a hypertext system with semantic types. We conclude with a design heuristic, drawn from our experience with SIBYL, for systems whose goal includes eliciting knowledge from people.

Many computer systems are based on various types of messages, forms, or other objects. When users of such systems need to communicate with people who use different object types, some kind of translation is necessary. In this paper, we explore the space of general solutions to this translation problem and propose a scheme that synthesizes these solutions. After first illustrating the problem in the Object Lens system, we identify two partly conflicting objectives that any translation scheme should satisfy: preservation of meaning and autonomous evolution of group languages. Then we partition the space of possible solutions to this problem in terms of the set theoretic relations between group languages and a common language. This leads to five primary solution classes and we illustrate and evaluate each one. Finally, we describe a composite scheme, called Partially Shared Views, that combines many of the best features of the other schemes. A key insight of the analysis is that partially shared type hierarchies allow “foreign” object types to be automatically translated into their nearest common “ancestor” types. The partial interoperability attained in this way makes possible flexible standards from which people can benefit from whatever agreements they do have without having to agree on everything. Even though our examples deal primarily with extensions to the Object Lens system, the analysis also suggests how other kinds of systems, such as EDI applications, might exploit specialization hierarchies of object types to simplify the translation problem.