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Abstract: Generating realistic and contextually appropriate gestures is crucial for creating engaging embodied conversational agents. Although speech is the primary input for gesture generation, adding controls like gesture velocity, hand height, and emotion is essential for generating more natural, human-like gestures. However, current approaches to controllable gesture generation often utilize a limited number of control parameters and lack the ability to activate/deactivate them selectively. Therefore, in this work, we propose the Cont-Gest model, a Transformer-based gesture generation model that enables selective control activation through masked training and a control fusion strategy. Furthermore, to better support the development of such models, we propose a novel evaluation-driven development (EDD) workflow, which combines several iterative tasks: automatic control signal extraction, control specification, visual (subjective) feedback, and objective evaluation. This workflow enables continuous monitoring of model performance and facilitates iterative refinement through feedback-driven development cycles. For objective evaluation, we are using the validated Kinetic–Hellinger distance, an objective metric that correlates strongly with the human perception of gesture quality. We evaluated multiple model configurations and control dynamics strategies within the proposed workflow. Experimental results show that Feature-wise Linear Modulation (FiLM) conditioning, combined with single-mask training and voice activity scaling, achieves the best balance between gesture quality and adherence to control inputs.
Keywords: gesture generation, objective evaluation, selective control activation, transformers, weakly supervised learning
Published in DKUM: 09.09.2025; Views: 0; Downloads: 3
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Abstract: This study investigates how effectively European football clubs communicate and implement accessibility features for disabled fans, aiming to develop a comprehensive framework for evaluating these practices. Using a multi-phase research design, the study analyzes disability support systems through semi-structured interviews with representatives from eleven European football clubs and a systematic analysis of club documentation. The methodology combined traditional qualitative analysis with large language model (LLM)-assisted content analysis, enabling robust identification of thematic patterns and performance indicators. Our findings reveal significant disparities in disability support practices, with larger clubs demonstrating structured approaches through dedicated Disability Access Officers (DAOs) and comprehensive communication strategies. Analysis identified three distinct performance tiers: Elite Performers, primarily well-resourced clubs with advanced systems; Solid Performers, mid-tier clubs with established frameworks; and Developing Systems, smaller organizations with emerging support structures. We present a validated Fan Communication Model incorporating key weighted criteria, including infrastructure, dedicated personnel, engagement, specific adaptations, ticketing, challenge management, and feedback systems. This model provides a standardized framework for evaluating disability support communication in football organizations. The research demonstrates the importance of integrating technological solutions with human-centered approaches while maintaining universal design principles. Our findings contribute to the sports accessibility literature and provide evidence-based recommendations for football organizations seeking to enhance their communication with disabled fans.
Keywords: disability access, football organizations, fan communication, communication strategies, sport accessibility, inclusive design, performance evaluation
Published in DKUM: 26.08.2025; Views: 0; Downloads: 2
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Abstract: In educational theory and research, metacognition is increasingly seen as an important predictor of successful learning – it is the key to learning and academic achievement. The study investigates "off-line" metacognition (i.e. the level of prediction and the level of self-evaluation) in relation to the solving of mathematical problems by primary school pupils. The research was carried out on a group of 311 pupils of 16 classes of primary schools. We used the test consisting of five tasks, which also included questions aimed at finding out the level of pupils' prediction and their level of self-evaluation. We processed the obtained data with the intentions of a quantitative methodological approach. It follows from the research findings that students who were successful in solving the tasks achieved a higher level of prediction and self-assessment than students who were not successful.
Keywords: metacognition, prediction, self-evaluation, solving of problems
Published in DKUM: 29.07.2025; Views: 0; Downloads: 4
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Abstract: Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms.
Keywords: algorithmic performance, experimental evaluation, metaheuristics, quantile, confidence interval, stochastic algorithms, evolutionary computation, swarm intelligence, experimental methodology
Published in DKUM: 28.03.2025; Views: 0; Downloads: 9
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Abstract: The intelligent management of built cultural heritage, including heritage buildings, requires common semantics in the form of standardized ontologies to achieve semantic interoperability. Foundational ontologies should be reused when building new ontologies, as they provide high-level terms; however, candidate foundational ontologies should be evaluated for quality and pitfalls. Simple metrics (e.g., number of concepts) are easy to obtain with existing tools. Complex metrics such as quality of ontology structure, functional adequacy, transferability, reliability, compatibility, maintainability, and operability, are defined in recent ontology evaluation frameworks; however, these do not evaluate interoperability features. The paper proposes an improved framework for an automated ontology evaluation based on the OQuaRE framework. Our approach improved some of the metrics of the OQuaRE framework and introduced three metrics for assessing the interoperability of the ontology in question (Externes, Composability, and Aggregability). In the experimental section, the framework is validated in an evaluation of cultural heritage information ontology (CIDOC CRM—ISO 12217:2014) with the use of new software for ontology evaluation. The detailed results reveal that the ontology is minimally acceptable and that the improved evaluation framework efficiently integrated interoperability metrics. Recommendations for the improvement of the cultural heritage information ontology are described in the Discussion and Conclusions section.
Keywords: ontology evaluation, interoperability metrics, cultural heritage, heritage buildings, cultural heritage information ontology, systematic evaluation, cultural heritage domain ontologies, BIM
Published in DKUM: 11.03.2025; Views: 0; Downloads: 8
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