45 research outputs found

    Multilinguisme et variétés linguistiques en Europe à l’aune de l’intelligence artificielle Multilinguismo e variazioni linguistiche in Europa nell’era dell’intelligenza artificiale Multilingualism and Language Varieties in Europe in the Age of Artificial Intelligence

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    Il presente volume Ăš il frutto di una riflessione interdisciplinare e multilingue maturata attorno a diversi eventi organizzati nell’ambito del panel concernente i diritti e le variazioni linguistiche in Europa nell’era dell’intelligenza artificiale all’interno del progetto Artificial Intelligence for European Integration, promosso dal Centro studi sull’Europa TO-EU dell’UniversitĂ  di Torino e cofinanziato dalla Commissione europea. L’interrogativo iniziale che abbiamo voluto sollevare Ăš se l’IA potesse avere un impatto negativo sulle varietĂ  linguistiche e sul multilinguismo, valore “aggiunto” dell’UE, o se potesse, e in che modo, divenire utile per la promozione di essi. Il volume, interamente inedito, puĂČ dirsi tra i primi ad affrontare, almeno in Europa, questo tipo di tematiche.This book is the outcome of an interdisciplinary multilingual reflection carried out on research into linguistic rights, multilingualism and language varieties in Europe in the age of artificial intelligence. It is part of the Artificial Intelligence for European Integration project, promoted by the Centre of European Studies To-EU of the University of Turin and co-financed by the European Commission. Our aim was to investigate more generally the negative and/or positive outcomes of AI on language varieties and multilingualism, the latter a key value for the EU. The result is a volume of original unpublished research being made generally available for the first time, at least in Europe.Ce livre a Ă©tĂ© Ă©laborĂ© Ă  partir d’une rĂ©flexion interdisciplinaire et multilingue qui a Ă©tĂ© menĂ©e dans le cadre d’une recherche sur les droits, le multilinguisme et les variĂ©tĂ©s linguistiques en Europe Ă  l’aune de l’intelligence artificielle Ă  l’intĂ©rieur du projet Artificial Intelligence for European Integration promu par le Centre d’études europĂ©ennes To-EU de l’UniversitĂ© de Turin et cofinancĂ© par la Commission de l’Union europĂ©enne. Notre propos Ă©tait de rĂ©flĂ©chir plus gĂ©nĂ©ralement sur les consĂ©quences nĂ©gatives et/ou positives de l’IA sur les variĂ©tĂ©s linguistiques et le multilinguisme, ce dernier Ă©tant une valeur de l’UE. Ce que nous proposons par ce numĂ©ro est un livre inĂ©dit qui peut se vanter d’ĂȘtre parmi les premiers Ă  s’occuper de ce type de thĂ©matique, du moins en Europe

    Liber Amicorum Jean-Claude Haelewyck édité par Claude Obsomer pour Ses Septante ans

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    Liber Amicorum Jean-Claude Haelewyck édité par Claude Obsomer pour Ses Septante ans

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    Advances in Computational Intelligence Applications in the Mining Industry

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    This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

    Risk Management for the Future

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    A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases

    Approximate Data Mining Techniques on Clinical Data

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    The past two decades have witnessed an explosion in the number of medical and healthcare datasets available to researchers and healthcare professionals. Data collection efforts are highly required, and this prompts the development of appropriate data mining techniques and tools that can automatically extract relevant information from data. Consequently, they provide insights into various clinical behaviors or processes captured by the data. Since these tools should support decision-making activities of medical experts, all the extracted information must be represented in a human-friendly way, that is, in a concise and easy-to-understand form. To this purpose, here we propose a new framework that collects different new mining techniques and tools proposed. These techniques mainly focus on two aspects: the temporal one and the predictive one. All of these techniques were then applied to clinical data and, in particular, ICU data from MIMIC III database. It showed the flexibility of the framework, which is able to retrieve different outcomes from the overall dataset. The first two techniques rely on the concept of Approximate Temporal Functional Dependencies (ATFDs). ATFDs have been proposed, with their suitable treatment of temporal information, as a methodological tool for mining clinical data. An example of the knowledge derivable through dependencies may be "within 15 days, patients with the same diagnosis and the same therapy usually receive the same daily amount of drug". However, current ATFD models are not analyzing the temporal evolution of the data, such as "For most patients with the same diagnosis, the same drug is prescribed after the same symptom". To this extent, we propose a new kind of ATFD called Approximate Pure Temporally Evolving Functional Dependencies (APEFDs). Another limitation of such kind of dependencies is that they cannot deal with quantitative data when some tolerance can be allowed for numerical values. In particular, this limitation arises in clinical data warehouses, where analysis and mining have to consider one or more measures related to quantitative data (such as lab test results and vital signs), concerning multiple dimensional (alphanumeric) attributes (such as patient, hospital, physician, diagnosis) and some time dimensions (such as the day since hospitalization and the calendar date). According to this scenario, we introduce a new kind of ATFD, named Multi-Approximate Temporal Functional Dependency (MATFD), which considers dependencies between dimensions and quantitative measures from temporal clinical data. These new dependencies may provide new knowledge as "within 15 days, patients with the same diagnosis and the same therapy receive a daily amount of drug within a fixed range". The other techniques are based on pattern mining, which has also been proposed as a methodological tool for mining clinical data. However, many methods proposed so far focus on mining of temporal rules which describe relationships between data sequences or instantaneous events, without considering the presence of more complex temporal patterns into the dataset. These patterns, such as trends of a particular vital sign, are often very relevant for clinicians. Moreover, it is really interesting to discover if some sort of event, such as a drug administration, is capable of changing these trends and how. To this extent, we propose a new kind of temporal patterns, called Trend-Event Patterns (TEPs), that focuses on events and their influence on trends that can be retrieved from some measures, such as vital signs. With TEPs we can express concepts such as "The administration of paracetamol on a patient with an increasing temperature leads to a decreasing trend in temperature after such administration occurs". We also decided to analyze another interesting pattern mining technique that includes prediction. This technique discovers a compact set of patterns that aim to describe the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important to improve the overall class prediction performance. We show that our classification approach achieves a significant reduction in the number of extracted patterns, compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model. For each technique described above, we developed a tool to retrieve its kind of rule. All the results are obtained by pre-processing and mining clinical data and, as mentioned before, in particular ICU data from MIMIC III database

    Pseudo-contractions as Gentle Repairs

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    Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas
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