A review of modelling and verification approaches for computational biology

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties

The use of the concept of event in enterprise ontologies and requirements engineering literature.

The concept of event is used in a lot of meanings. It can be the possible outcome of doing something (probability theory), it can be a business transaction (accounting), or just a plain happening. In software engineering, the concept of event is also used a lot. It is used to accomplish loose coupling between software components or to realise interaction between different services. There is however not a consensus on the meaning of `an event'. In enterprise ontologies, an event is defined as a happening at one point in time, or as an activity which takes time to complete. In requirement engineering, the same different uses can be found, together with an event as a request for something that needs to be done. These differences can also be found in implementation. All these distinct purposes of the word event make it difficult to integrate and use different requirement engineering techniques. Comparison or transformations between models drawn in different grammars is impossible because of the ambiguity of the concept of event. We define three meanings for an event that are used by enterprise ontologies and requirement engineering techniques: an achievement (happening at one point in time), an activity (happening over time) and a request (a demand for something that needs to be done). We also identify a missing link between real economic events, the events defined in the requirements model and the events used in implementation.Requirements modelling; Enterprise ontology; Process modelling; Dynamic; Event;

Leveraging Large Models for Crafting Narrative Visualization: A Survey

Narrative visualization effectively transforms data into engaging stories, making complex information accessible to a broad audience. Large models, essential for narrative visualization, inherently facilitate this process through their superior ability to handle natural language queries and answers, generate cohesive narratives, and enhance visual communication. Inspired by previous work in narrative visualization and recent advances in large models, we synthesized potential tasks and opportunities for large models at various stages of narrative visualization. In our study, we surveyed 79 papers to explore the role of large models in automating narrative visualization creation. We propose a comprehensive pipeline that leverages large models for crafting narrative visualization, categorizing the reviewed literature into four essential phases: Data, Narration, Visualization, and Presentation. Additionally, we identify nine specific tasks where large models are applied across these stages. This study maps out the landscape of challenges and opportunities in the LM4NV process, providing insightful directions for future research and valuable guidance for scholars in the field.Comment: 20 pages,6 figures, 2 table

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

CONE

MIFTel: a multimodal interactive framework based on temporal logic rules

Human-computer and multimodal interaction are increasingly used in everyday life. Machines are able to get more from the surrounding world, assisting humans in different application areas. In this context, the correct processing and management of signals provided by the environments is determinant for structuring the data. Different sources and acquisition times can be exploited for improving recognition results. On the basis of these assumptions, we are proposing a multimodal system that exploits Allen’s temporal logic combined with a prevision method. The main object is to correlate user’s events with system’s reactions. After post-elaborating coming data from different signal sources (RGB images, depth maps, sounds, proximity sensors, etc.), the system is managing the correlations between recognition/detection results and events in real-time to create an interactive environment for the user. For increasing the recognition reliability, a predictive model is also associated with the proposed method. The modularity of the system grants a full dynamic development and upgrade with custom modules. Finally, a comparison with other similar systems is shown, underlining the high flexibility and robustness of the proposed event management method