287,346 research outputs found

    Digitális felsőoktatás, szerzői jog és COVID-19 pandémia : empirikus kutatás a Szegedi Tudományegyetemen

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    Article 5 of the European Union’s Directive 2019/790 on Copyright and Related Rights in the Digital Single Market (CDSM Directive) is of crucial importance to anyone interested in digital learning and education. The new rules have broadened the scope of educational limitations and exceptions, but their effectiveness will be tested only over time. Such testing was made even more relevant after the global outbreak of the SARS-CoV-2 (COVID-19) pandemic. It has led to the closure of the premises of educational institutions and libraries, and made online access, use and sharing educational materials more urgent than ever. Hungary was the first EU Member State to implement Article 5 in April 2020, but there is scarce empirical evidence on whether the new rules have reached their goals at all. This paper summarizes the empirical analysis of the awareness, perceptions and use practises of students, educators and librarians of the University of Szeged with respect to the copyright aspects of digital (distance and online) learning and teaching in the pandemic

    The threshold of rule productivity in infants

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    Most learning theories agree that the productivity of a rule or a pattern relies on regular exemplars being dominant over exceptions; the threshold for productivity is, however, unclear; moreover, gradient productivity levels are assumed for different rules/patterns, regular or irregular. One theory by Yang, the Tolerance Principle (TP), specified a productivity threshold applicable to all rules, calculated by the numbers of total exemplars and exceptions of a rule; furthermore, rules are viewed as quantal, either productive or unproductive, with no gradient levels. We evaluated the threshold and gradience-quantalness questions by investigating infants’ generalization. In an implicit learning task, 14-month-olds heard exemplars of an artificial word-order rule and exceptions; their distributions were set closed to the TP-threshold (5.77) on both sides: 11 regular exemplars vs. 5 exceptions in Condition 1 (productiveness predicted), and 10 regular exemplars vs. 6 exceptions in Condition 2 (unproductiveness predicted). These predictions were pitted against those of the statistical majority threshold (50%), a common assumption which would predict generalization in both conditions (68.75, 62.5%). Infants were tested on the trained rule with new exemplars. Results revealed generalization in Condition 1, but not in Condition 2, supporting the TP-threshold, not the statistical majority threshold. Gradience-quantalness was assessed by combined analyses of Conditions 1-2 and previous experiments by Koulaguina and Shi. The training across the conditions contained gradually decreasing regular exemplars (100, 80, 68.75, 62.5, 50%) relative to exceptions. Results of test trials showed evidence for quantalness in infants (productive: 100, 80, 68.75%; unproductive: 62.5, 50%), with no gradient levels of productivity

    A MDL-based Model of Gender Knowledge Acquisition

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    This paper presents an iterative model of\ud knowledge acquisition of gender information\ud associated with word endings in\ud French. Gender knowledge is represented\ud as a set of rules containing exceptions.\ud Our model takes noun-gender pairs as input\ud and constantly maintains a list of\ud rules and exceptions which is both coherent\ud with the input data and minimal with\ud respect to a minimum description length\ud criterion. This model was compared to\ud human data at various ages and showed a\ud good fit. We also compared the kind of\ud rules discovered by the model with rules\ud usually extracted by linguists and found\ud interesting discrepancies

    Experience-dependent brain development as a key to understanding the language system

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    An influential view of the nature of the language system is that of an evolved biological system in which a set of rules is combined with a lexicon that contains the words of the language together with a representation of their context. Alternative views, usually based on connectionist modeling, attempt to explain the structure of language on the basis of complex associative processes. Here I put forward a third view that stresses experience-dependent structural development of the brain circuits supporting language as a core principle of the organization of the language system. On this view, embodied in a recent neuroconstructivist neural network of past tense development and processing, initial domain-general predispositions enable the development of functionally specialized brain structures through interactions between experience-dependent brain development and statistical learning in a structured environment. Together, these processes shape a biological adult language system that appears to separate into distinct mechanism for processing rules and exceptions, whereas in reality those subsystems co-develop and interact closely. This view puts experience-dependent brain development in response to a specific language environment at the heart of understanding not only language development but adult language processing as well

    Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs

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    This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods (both connectionist and symbolic).Comment: See http://www.jair.org/ for any accompanying file

    Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

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    We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model's global behavior, we propose the LIME-FOLD algorithm ---a heuristic-based inductive logic programming (ILP) algorithm capable of learning non-monotonic logic programs---that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system

    Otherwise Open: Managing Incompatible Content with Open Educational Resources

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    This paper seeks to provide an overview of the problem posed by the incorporation of materials protected by all-rights-reserved copyright, or that are not legally compatible with the copyright terms of materials offered to users, into otherwise open educational resources. This paper also describes a number of approaches to resolving this issue, including the reliance on jurisdictional copyright exceptions and limitations, and explores the trade-offs involved in adopting any one of these approaches. This paper also suggests areas for further empirical research into these issues

    Measurement of body temperature and heart rate for the development of healthcare system using IOT platform

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    Health can be define as a state of complete mental, physical and social well-being and not merely the absence of disease or infirmity according to the World Health Organization (WHO) [1]. Having a healthy body is the greatest blessing of life, hence healthcare is required to maintain or improve the health since the healthcare is the maintenance or improvement of health through the diagnosis, prevention, and treatment of injury, disease, illness, and other mental and physical impairments in human beings. The novel paradigm of Internet of Things (IoT) has the potential to transform modern healthcare and improve the well-being of entire society [2]. IoT is a concept aims to connec
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