Inês Dutra

Abstract
HAL 9000 is an Intrusion Tolerant Systems (ITSs) Risk Manager, which assesses configuration risks against potential intrusions. It utilizes gathered threat knowledge and remains operational, even in the absence of updated information. Based on its advice, the ITSs can dynamically and proactively adapt to recent threats to minimize and mitigate future intrusions from malicious adversaries.Our goal is to reduce the risk linked to the exploitation of recently uncovered vulnerabilities that have not been classified and/or do not have a script to reproduce the exploit, considering the potential that they may have already been exploited as zero-day exploits. Our experiments demonstrate that the proposed solution can effectively learn and replicate National Vulnerability Database's evaluation process with 99% accuracy. © 2024 IEEE.

Abstract
Bipolar Disorder (BD) is a chronic and severe psychiatric illness presenting with mood alterations, including manic, hypomanic, and depressive episodes. Due to the high clinical heterogeneity and lack of biological validation, both treatment and diagnosis of BD remain problematic and challenging. In this context, this paper proposes a novel intelligent system applied to the diagnosis of BD. First, each patient’s single nucleotide polymorphism (SNP) data is represented by QR codes, which reduces the high dimensionality of the problem and homogenizes the data representation. For the initial tests of the system, the Wellcome Trust Case Control Consortium (WTCCC) dataset was used. The preliminary results are encouraging, with an AUC value of 0.82 and an accuracy of 82%, correctly classifying all cases and most controls. This approach reduces the dimensionality of large amounts of data and can help improve diagnosis and deliver the right treatment to the patient. Furthermore, the architecture of the system is versatile and could be adapted and used to diagnose other diseases where there is also high dimensionality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract

Abstract
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that is currently unsolvable by any Machine Learning method. It demands strong generalization and reasoning capabilities, which are known to be weaknesses of Neural Network-based systems. In this work, we propose a Program synthesis system to solve ARC, Induce Logic Programs for Abstract Reasoning (ILPAR), which casts an ARC problem as a sequence of Inductive Logic Programming (ILP) problems. We have implemented a simple Domain Specific Language (DSL) that corresponds to a small set of object-centric abstractions relevant to ARC. This is Background Knowledge used by ILP to create abstract Logic Programs that provide reasoning capabilities to our system. When solving each ARC task, ILPAR can generalize from a few training examples: pairs of Input-Output grids. The Logic Programs are able to generate objects present in the Output grid and the combination of these can transform an Input grid into an Output grid. We randomly chose some tasks from ARC that do not require more than the small number of primitives we implemented in our DSL and showed that providing only this to ILPAR, it can solve tasks that require each different reasoning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.