Dipartimento di Matematica e Informatica - Tesi di Dottorato
Permanent URI for this collectionhttps://lisa.unical.it/handle/10955/103
Questa collezione raccoglie le Tesi di Dottorato afferenti al Dipartimento di Matematica e Informatica dell'Università della Calabria.
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Item Balancing the average weighted completion times of two classes of jobs: a new scheduling problem(Università della Calabria, 2023-11-29) Avolio, Matteo; Terracina, Giorgio; Fuduli, AntonioExploring a new area of the scheduling theory and inspired by a real application in an academic context, in this thesis we introduce a new single-machine two-agent scheduling problem, aimed at balancing the average weighted completion times of two different classes of jobs, one per agent. Differently from the common multiagent cases, which are generally of the competing type, this problem could be interpreted as a cooperative type problem. In fact, even if the two agents share the same machine, they cooperate to optimize the unique global objective function, in order to balance their average weighted completion times. While for the case with identical jobs and unitary weight we present an exact algorithm providing an optimal solution in linear time, for the general case we prove the NP-hardness of the problem and we propose a mathematical formulation as a variant of the well known quadratic assignment problem. By applying the Glover linearization, we obtain a mixed integer linear program exploited to design a Lagrangian heuristics based on solving, at each iteration, a linear assignment problem. Since the proposed algorithm has revealed to be able to solve instances up to 500 jobs, in order to face larger scale instances (up to 2000 jobs) we also propose a genetic algorithm.Item Expanding the Frontiers in GenAI and XAI: Innovative Architectures and Applications(Università della Calabria, 2024-09-27) Adorneto, Carlo; Greco, Gianluigi; Terracina, GiorgioGenerative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) have attracted significant interest in recent years due to their potential and their capacity to drive and inspire further research. This thesis explores new frontiers in these fields by presenting a collection of innovative architectures and applications. In the realm of GenAI, this work introduces GIDnets, a generative neural network aimed at solving inverse design problems through latent space exploration, showcasing improvements over existing methods. Furthermore, the research explores the application of latent space conditioning and transformers for automatic medical report generation. The thesis also investigates the role of generative agents, based on Large Language Models (LLMs), in agent-based modeling, offering insights into their validation and the emerging challenges. One of the notable challenges addressed is the complexity of opinion diffusion in social environments, highlighting its potential as a promising application scenario for generative agents. In the domain of XAI, this thesis illustrates the impact of computational methods on data interpretation, particularly when data science and Deep Learning (DL) are employed to gain insights in the biomedical field. Despite advancements, explaining DL models remains a debated issue. SHAP (SHapley Additive exPlanations) is demonstrated as a powerful tool for extracting insights from these black-box models and its application in bankruptcy prediction and natural disaster event scenarios will be discussed. Additionally, a new deep learning algorithm based on XAI is proposed for feature selection in genomics. This algorithm utilizes a new SHAP-inspired metric to identify and quantify the impact of genes, significantly enhancing the prediction accuracy for chronic lymphocytic leukemia. The innovative approaches presented in this thesis advance the state-of-the-art in GenAI and XAI, showcasing the potential of these technologies to enable the design of practical solutions across various domains.Item Optimizing Evaluation of Logic Programs: Extended Compilation & Enhanced Rewritings(Università della Calabria, 2023-11-29) Mazzotta, Giuseppe; Terracina, Giorgio; Ricca, FrancescoItem SPARQL-QA: A system for Question Answering over RDF(S) Knowledge Bases(Università della Calabria, 2023-04-29) Borroto Santana, Manuel Alejandro; Terracina, Giorgio; Ricca, FrancescoItem Enhancing DLV for reasoning over streams: the LDSR language and its expressiveness(Università della Calabria, 2024-04-09) Morelli, Maria Concetta; Terracina, Giorgio; Manna, Marco; Perri, SimonaItem Crowdshipping in dynamic pickup and delivery problems(Università della Calabria, 2023-11-23) Stoia, Sara; Terracina, Giorgio; Laganà, Demetrio; Vocaturo, FrancescaItem Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis(Università della Calabria, 2020-01-16) Marzullo, Aldo; Leone, Nicola; Calimeri, Francesco; Terracina, Giorgio;Multiple sclerosis (MS) is a chronic disease of the central nervous system, leading cause of nontraumatic disability in young adults. MS is characterized by inflammation, demyelination and neurodegenrative pathological processes which cause a wide range of symptoms, including cognitive deficits and irreversible disability. Concerning the diagnosis of the disease, the introduction of Magnetic Resonance Imaging (MRI) has constituted an important revolution in the last 30 years. Furthermore, advanced MRI techniques, such as brain volumetry, magnetization transfer imaging (MTI) and diffusion-tensor imaging (DTI) are nowadays the main tools for detecting alterations outside visible brain lesions and contributed to our understanding of the pathological mechanisms occurring in normal appearing white matter. In particular, new approaches based on the representation of MR images of the brain as graph have been used to study and quantify damages in the brain white matter network, achieving promising results. In the last decade, novel deep learning based approaches have been used for studying social networks, and recently opened new perspectives in neuroscience for the study of functional and structural brain connectivity. Due to their effectiveness in analyzing large amount of data, detecting latent patterns and establishing functional relationships between input and output, these artificial intelligence techniques have gained particular attention in the scientific community and is nowadays widely applied in many context, including computer vision, speech recognition, medical diagnosis, among others. In this work, deep learning methods were developed to support biomedical image analysis, in particular for the classification and the characterization of MS patients based on structural connectivity information. Graph theory, indeed, constitutes a sensitive tool to analyze the brain networks and can be combined with novel deep learning techniques to detect latent structural properties useful to investigate the progression of the disease. In the first part of this manuscript, an overview of the state of the art will be given. We will focus our analysis on studies showing the interest of DTI for WM characterization in MS. An overview of the main deep learning techniques will be also provided, along with examples of application in the biomedical domain. In a second part, two deep learning approaches will be proposed, for the generation of new, unseen, MRI slices of the human brain and for the automatic detection of the optic disc in retinal fundus images. In the third part, graph-based deep learning techniques will be applied to the study of brain structural connectivity of MS patients. Graph Neural Network methods to classify MS patients in their respective clinical profiles were proposed with particular attention to the model interpretation, the identification of potentially relevant brain substructures, and to the investigation of the importance of local graph-derived metrics for the classification task. Semisupervised and unsupervised approaches were also investigated with the aim of reducing the human intervention in the pipeline.