881 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Ecological and confined domain ontology construction scheme using concept clustering for knowledge management

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    Knowledge management in a structured system is a complicated task that requires common, standardized methods that are acceptable to all actors in a system. Ontology, in this regard, is a primary element and plays a central role in knowledge management, interoperability between various departments, and better decision making. The ontology construction for structured systems comprises logical and structural complications. Researchers have already proposed a variety of domain ontology construction schemes. However, these schemes do not involve some important phases of ontology construction that make ontologies more collaborative. Furthermore, these schemes do not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology. The major objectives of this research were to provide a comparison between some existing ontology construction schemes and to propose an enhanced ecological and confined domain ontology construction (EC-DOC) scheme for structured knowledge management. The proposed scheme introduces five important phases to construct an ontology, with a major focus on the conceptualizing and clustering of domain concepts. In the conceptualization phase, a glossary of domain-related concepts and their properties is maintained, and a Fuzzy C-Mean soft clustering mechanism is used to form the clusters of these concepts. In addition, the localization of concepts is instantly performed after the conceptualization phase, and a translation file of localized concepts is created. The EC-DOC scheme can provide accurate concepts regarding the terms for a specific domain, and these concepts can be made available in a preferred local language

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    GPT Semantic Networking: A Dream of the Semantic Web – The Time is Now

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    The book presents research and practical implementations related to natural language processing (NLP) technologies based on the concept of artificial intelligence, generative AI, and the concept of Complex Networks aimed at creating Semantic Networks. The main principles of NLP, training models on large volumes of text data, new universal and multi-purpose language processing systems are presented. It is shown how the combination of NLP and Semantic Networks technologies opens up new horizons for text analysis, context understanding, the formation of domain models, causal networks, etc. This book presents methods for creating Semantic Networks based on prompt engineering. Practices are presented that will help build semantic networks capable of solving complex problems and making revolutionary changes in the analytical activity. The publication is intended for those who are going to use large language models for the construction and analysis of semantic networks in order to solve applied problems, in particular, in the field of decision making.У книзі представлені дослідження та практичні реалізації технологій обробки природної мови (НЛП), заснованих на концепції штучного інтелект, генеративний ШІ та концепція складних мереж, спрямована на створення семантичних мереж. Представлено основні принципи НЛП, моделі навчання на великих обсягах текстових даних, нові універсальні та багатоцільові системи обробки мови. Показано, як поєднання технологій NLP і семантичних мереж відкриває нові горизонти для аналізу тексту, розуміння контексту, формування моделей домену, причинно-наслідкових мереж тощо. У цій книзі представлені методи створення семантичних мереж на основі оперативного проектування. Представлені практики, які допоможуть побудувати семантичні мережі, здатні вирішувати складні проблеми та вносити революційні зміни в аналітичну діяльність. Видання розраховане на тих, хто збирається використовувати велику мову моделі побудови та аналізу семантичних мереж з метою вирішення прикладних задач, зокрема, у сфері прийняття рішень

    The Self The Soul and The World: Affect Reason and Complexity

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    This book looks at the affective-cognitive roots of how the human mind inquires into the workings of nature and, more generally, how the mind confronts reality. Reality is an infinitely complex system, in virtue of which the mind can comprehend it only in bits and pieces, by making up interpretations of the myriads of signals received from the world by way of integrating those with information stored from the past. This constitutes a piecemeal interpretation by which we assemble our phenomenal reality. In perceiving the complex world and responding to it, the mind invokes the logic of affect and the logic of reason, the former mostly innate and implicit, and the latter generated consciously in explicit terms with reference to mind-independent relations between entities in nature. It is a strange combination of affect and reason that enables us to make decisions and inferences, --- the latter mostly of the inductive type --- thereby making possible the development of theories. Theories are our tool-kits for explaining and predicting phenomena, guiding us along in our journey in life. Theories, however, are defeasible, and need to be constantly updated, at times even radically. In this, the self and the soul are of enormous relevance. The former is the affect-based psychological engine driving all our mental processes, while the latter is the capacity of the conscious mind to examine and reconstruct the self by modulating repressed conflicts. If the soul remains inoperative, all our theories become misdirected and a rot spreads inexorably all around us

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023

    Beyond Quantity: Research with Subsymbolic AI

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    How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Computational Stylistics in Poetry, Prose, and Drama

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    The contributions in this edited volume approach poetry, narrative, and drama from the perspective of Computational Stylistics. They exemplify methods of computational textual analysis and explore the possibility of computational generation of literary texts. The volume presents a range of computational and Natural Language Processing applications to literary studies, such as motif detection, network analysis, machine learning, and deep learning
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