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The field of Natural Language Processing (NLP) has experienced significant growth in recent years, largely due to advancements in Deep Learning technology and especially Large Language Models. These improvements have allowed for the development of new models and architectures that have been successfully applied in various real-world applications. Despite this progress, the field of Legal Informatics has been slow to adopt these techniques. In this study, we conducted an extensive literature review of NLP research focused on legislative documents. We present the current state-of-the-art NLP tasks related to Law Consolidation, highlighting the challenges that arise in low-resource languages. Our goal is to outline the difficulties faced by this field and the methods that have been developed to overcome them. Finally, we provide examples of NLP implementations in the legal domain and discuss potential future directions. [ABSTRACT FROM AUTHOR]

Inverse frequent itemset mining can be successfully modelled as an instance of the Probabilistic Satisfiability problem. Given a transaction database we can perform a frequent itemset mining algorithm, like the Apriori algorithm, to obtain useful itemset collections such as frequent or closed itemsets. We then use these itemset collections as frequency constraints in order to reconstruct the original database by solving a linear programming problem. There are cases however, where the reconstructed database is not in direct agreement with the original one. In this study, we analyse the degree of similarity between the original database and the reconstructed one, when different variations of condensed itemset representations are used as the initial frequency constraints. We examine how much the initial database properties and itemset relations have been preserved when we emphasize on the frequent itemset border and assess database quality by measuring database distance metrics. As this solution framework presents increased computational cost, we also consider a heuristic approach that is based on the notion that a transaction can be also considered as an itemset and compare the strengths and weaknesses of each framework when the same conditions apply. We manage to improve the efficiency of existing heuristic based approaches to the problem by utilizing smaller initial itemset collections. Our work contributes in (a) privacy preserving data mining and (b) gain in transaction database storage memory savings. [ABSTRACT FROM AUTHOR]

Educational Data Mining has turned into an effective technique for revealing relationships hidden in educational data and predicting students' learning outcomes. One can analyze data extracted from the students' activity, educational and social behavior, and academic background. The outcomes which are produced are, the following: A personalized learning procedure, a feasible engagement with students' behavior, a predictable interaction of the students with the learning processes and data. In the current work, we apply several supervised methods aiming at predicting the students' academic performance. We prove that the use of the default parameters of learning algorithms on a voting generalization procedure of the three most accurate classifiers, can produce better results than any single tuned learning algorithm. [ABSTRACT FROM AUTHOR]

This work aims to contribute to the field of COVID-19 pandemic analysis. In this research we applied a twofold analysis that focused initially on the country general social-economic and medical characteristics and on top of that in a second level exploring the correlations to the characteristics that affect COVID-19 patients' mortality level. The approach has been applied to large datasets that include country level medical and the socio-economic data according to World Health Organization, the role of the cigarette consumption per capita using open datasets, and the cumulative data of the "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University" for the total number of Cases, Deaths and Recovered. 101 countries including twenty-two (22) features are studied. We have also drilled in the country of Mexico datasets to show case the effectiveness of our approach. We show that our approach can achieve 96% overall accuracy based on the proposed combination approach of macro and micro features. Our approach outdoes previous study results that utilize machine learning to assist medical decision-making in COVID-19 prognosis. We conclude that country social economic and medical characteristics play important role to COVID-19 patients' prognosis and their outcome. [ABSTRACT FROM AUTHOR]

Touristic destinations all around the world are struggling to digitally transform the touristic experience and the touristic products they offer and to capitalize a good experience with new tourists and returning ones. There is a lot of research on digital solutions assisting tourism, but it does not provide a follow-up digital product, therefore depending only on physical gifts, postcards and mementos. In this work, we propose a novel platform that can provide a personalized digital memento or digital gift for every route or tourist destination that can become the digital point of reference of visitors' experience giving new dimensions for commercialization to the existing physical mementos at the gift shops. The purpose of this study is to analyze what comprises a memorable touristic experience and to design and, finally, present a total solution that builds and offers a personal e-souvenir of a touristic experience to the tourist for him to hold, sport and share just using his mobile phone. We propose a digital memento-building platform that includes the personalized experience in visits taking place while in vacations. The visitors are usually taking pictures along routes they follow that later need further organization and processing and that in no way substitutes the physical mementos. In our approach, we propose a solution that generates a unique and personalized e-souvenir through a collage of the selfies and photos creating the digital equivalent of the touristic postcards but in our case personalized with the visitor photos with minimum amount of effort and produced real-time. Our approach is also providing a platform to the photographers and designers of touristic destinations to build and graphically generated memento artifacts—templates specific for one or more destinations or routes. In this way, the approach serves the tourism industry vertically, covering all aspects, i.e., the tourist-visitor, the tourism professional players such as photographers, designers of physical mementos and, of course, the touristic destination providing a digital footprint to server marketing of the destination through sharing on social media and word-of-mouth, of course. To support our approach, we have built and run a fully working prototype in the touristic center of Athens, Greece, with real users and designers for several weeks during summer vacations. The results have been greatly encouraging from end-users and professionals. The study shows that it is possible for various lines of business to come together and work along one another for an improve touristic experience using mobile technologies in a personalized, targeted approach. The touristic destination, graphic designers, photographers, tourist agent specialists, software developers and visitors can all now have a digital personalized memorable gift from the visit. [ABSTRACT FROM AUTHOR]