Related papers: Rethinking Cybersecurity Ontology Classification a…

Achieving semantic interoperability across heterogeneous experimental data systems remains a major barrier to data-driven scientific discovery. The Analytical Information Markup Language (AnIML), a flexible XML-based standard for analytical…

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To…

AI systems are consistently evolving in terms of both capability and autonomy with an holistic social impact. In this context of proliferation and fast technological evolution, the scientific community is actively engaged to assure…

In response to increasingly sophisticated cyberattacks, a health-based approach is being used to define and assess their impact. Two significant cybersecurity workshops have fostered this perspective, aiming to standardize the understanding…

Ontologies are considered as the backbone of the Semantic Web. With the rising success of the Semantic Web, the number of participating communities from different countries is constantly increasing. The growing number of ontologies…

This paper examines the problems faced by Law Enforcement in searching large quantities of electronic evidence. It examines the use of ontologies as the basis for new forensic software filters and provides a proof of concept tool based on…

Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber…

Recognizing and learning from similar crisis situations is crucial for the development of effective response strategies. This study addresses the challenge of identifying similarities within a wide range of crisis-related information. To…

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for…

Experimental research methods describe standards to safeguard scientific integrity and reputability. These methods have been extensively integrated into traditional scientific disciplines and studied in the philosophy of science. The field…

This dissertation proposes a framework of user-centered security in Natural Language Processing (NLP), and demonstrates how it can improve the accessibility of related research. Accordingly, it focuses on two security domains within NLP…

Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behaviour changes…

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an…

In this paper we propose facilitating ontology development by constant evaluation of steps in the process of ontology development. Existing methodologies for ontology development are complex and they require technical knowledge that…

Smart environments integrates various types of technologies, including cloud computing, fog computing, and the IoT paradigm. In such environments, it is essential to organize and manage efficiently the broad and complex set of heterogeneous…

Despite rapid progress, most of the educational technologies today lack a strong instructional design knowledge basis leading to questionable quality of instruction. In addition, a major challenge is to customize these educational…

This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model. The proposed model moves away from the conventional node-centric framework and focuses on…

As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning…

Reliable failure detection holds paramount importance in safety-critical applications. Yet, neural networks are known to produce overconfident predictions for misclassified samples. As a result, it remains a problematic matter as existing…

Many of the benefits we derive from the Internet require trust in the authenticity of HTTPS connections. Unfortunately, the public key certification ecosystem that underwrites this trust has failed us on numerous occasions. Towards an…