17 research outputs found

    Method versatility in analysing human attitudes towards technology

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    Various research domains are facing new challenges brought about by growing volumes of data. To make optimal use of them, and to increase the reproducibility of research findings, method versatility is required. Method versatility is the ability to flexibly apply widely varying data analytic methods depending on the study goal and the dataset characteristics. Method versatility is an essential characteristic of data science, but in other areas of research, such as educational science or psychology, its importance is yet to be fully accepted. Versatile methods can enrich the repertoire of specialists who validate psychometric instruments, conduct data analysis of large-scale educational surveys, and communicate their findings to the academic community, which corresponds to three stages of the research cycle: measurement, research per se, and communication. In this thesis, studies related to these stages have a common theme of human attitudes towards technology, as this topic becomes vitally important in our age of ever-increasing digitization. The thesis is based on four studies, in which method versatility is introduced in four different ways: the consecutive use of methods, the toolbox choice, the simultaneous use, and the range extension. In the first study, different methods of psychometric analysis are used consecutively to reassess psychometric properties of a recently developed scale measuring affinity for technology interaction. In the second, the random forest algorithm and hierarchical linear modeling, as tools from machine learning and statistical toolboxes, are applied to data analysis of a large-scale educational survey related to students’ attitudes to information and communication technology. In the third, the challenge of selecting the number of clusters in model-based clustering is addressed by the simultaneous use of model fit, cluster separation, and the stability of partition criteria, so that generalizable separable clusters can be selected in the data related to teachers’ attitudes towards technology. The fourth reports the development and evaluation of a scholarly knowledge graph-powered dashboard aimed at extending the range of scholarly communication means. The findings of the thesis can be helpful for increasing method versatility in various research areas. They can also facilitate methodological advancement of academic training in data analysis and aid further development of scholarly communication in accordance with open science principles.Verschiedene Forschungsbereiche müssen sich durch steigende Datenmengen neuen Herausforderungen stellen. Der Umgang damit erfordert – auch in Hinblick auf die Reproduzierbarkeit von Forschungsergebnissen – Methodenvielfalt. Methodenvielfalt ist die Fähigkeit umfangreiche Analysemethoden unter Berücksichtigung von angestrebten Studienzielen und gegebenen Eigenschaften der Datensätze flexible anzuwenden. Methodenvielfalt ist ein essentieller Bestandteil der Datenwissenschaft, der aber in seinem Umfang in verschiedenen Forschungsbereichen wie z. B. den Bildungswissenschaften oder der Psychologie noch nicht erfasst wird. Methodenvielfalt erweitert die Fachkenntnisse von Wissenschaftlern, die psychometrische Instrumente validieren, Datenanalysen von groß angelegten Umfragen im Bildungsbereich durchführen und ihre Ergebnisse im akademischen Kontext präsentieren. Das entspricht den drei Phasen eines Forschungszyklus: Messung, Forschung per se und Kommunikation. In dieser Doktorarbeit werden Studien, die sich auf diese Phasen konzentrieren, durch das gemeinsame Thema der Einstellung zu Technologien verbunden. Dieses Thema ist im Zeitalter zunehmender Digitalisierung von entscheidender Bedeutung. Die Doktorarbeit basiert auf vier Studien, die Methodenvielfalt auf vier verschiedenen Arten vorstellt: die konsekutive Anwendung von Methoden, die Toolbox-Auswahl, die simultane Anwendung von Methoden sowie die Erweiterung der Bandbreite. In der ersten Studie werden verschiedene psychometrische Analysemethoden konsekutiv angewandt, um die psychometrischen Eigenschaften einer entwickelten Skala zur Messung der Affinität von Interaktion mit Technologien zu überprüfen. In der zweiten Studie werden der Random-Forest-Algorithmus und die hierarchische lineare Modellierung als Methoden des Machine Learnings und der Statistik zur Datenanalyse einer groß angelegten Umfrage über die Einstellung von Schülern zur Informations- und Kommunikationstechnologie herangezogen. In der dritten Studie wird die Auswahl der Anzahl von Clustern im modellbasierten Clustering bei gleichzeitiger Verwendung von Kriterien für die Modellanpassung, der Clustertrennung und der Stabilität beleuchtet, so dass generalisierbare trennbare Cluster in den Daten zu den Einstellungen von Lehrern zu Technologien ausgewählt werden können. Die vierte Studie berichtet über die Entwicklung und Evaluierung eines wissenschaftlichen wissensgraphbasierten Dashboards, das die Bandbreite wissenschaftlicher Kommunikationsmittel erweitert. Die Ergebnisse der Doktorarbeit tragen dazu bei, die Anwendung von vielfältigen Methoden in verschiedenen Forschungsbereichen zu erhöhen. Außerdem fördern sie die methodische Ausbildung in der Datenanalyse und unterstützen die Weiterentwicklung der wissenschaftlichen Kommunikation im Rahmen von Open Science

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

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    5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

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    Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

    How Stable is Knowledge Base Knowledge?

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    Knowledge Bases (KBs) provide structured representation of the real-world in the form of extensive collections of facts about real-world entities, their properties and relationships. They are ubiquitous in large-scale intelligent systems that exploit structured information such as in tasks like structured search, question answering and reasoning, and hence their data quality becomes paramount. The inevitability of change in the real-world, brings us to a central property of KBs -- they are highly dynamic in that the information they contain are constantly subject to change. In other words, KBs are unstable. In this paper, we investigate the notion of KB stability, specifically, the problem of KBs changing due to real-world change. Some entity-property-pairs do not undergo change in reality anymore (e.g., Einstein-children or Tesla-founders), while others might well change in the future (e.g., Tesla-board member or Ronaldo-occupation as of 2022). This notion of real-world grounded change is different from other changes that affect the data only, notably correction and delayed insertion, which have received attention in data cleaning, vandalism detection, and completeness estimation already. To analyze KB stability, we proceed in three steps. (1) We present heuristics to delineate changes due to world evolution from delayed completions and corrections, and use these to study the real-world evolution behaviour of diverse Wikidata domains, finding a high skew in terms of properties. (2) We evaluate heuristics to identify entities and properties likely to not change due to real-world change, and filter inherently stable entities and properties. (3) We evaluate the possibility of predicting stability post-hoc, specifically predicting change in a property of an entity, finding that this is possible with up to 83% F1 score, on a balanced binary stability prediction task.Comment: Incomplete draft. 12 page

    Predicting Document Coverage for Relation Extraction

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    Predicting Document Coverage for Relation Extraction

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    This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation
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