Big Data and Cognitive Computing

Active athletes represent a specific target for learning and development. Their schedules, including training sessions and competitions, leave little time for education. However, athletes still need skills beyond sports to ensure they are prepared for future employment. Our study approaches this issue by identifying appropriate settings for athletes’ learning and development. (1) Based on the background of current athletes’ education, it addresses the gap of not enough attention being paid to transferable practices from corporate attitudes to learning and development. (2) The study’s methodology primarily uses the case study concept because this conveys the video content we created for the athletes’ learning and development. This is combined with the method of content analysis of selected examples from corporate learning and development and the design thinking workshop, with the engagement of important stakeholder groups: athletes (2 participants), lecturers (2 participants), and representatives of sports organizations (1 participant). The other 9 workshop participants were master’s students in a managerial study programme because of their age similarities with the current athletes and the applicability of the courses they were studying to athletes’ education. (3) The designed process was created as a digital twin using haptic artefacts and the S2M technology (version 1.0) within the OMiLAB platform (version 1.6). Our results show that video content tailored to the athletes’ constraints is a viable solution that improves their career prospects. (4) The study’s practical implications are supported by the expert validation of the model provided by the inside of the large sports organizations’ management.

Although multi-agent reinforcement learning (MARL) has achieved significant success in various domains, its deployment in real-world scenarios remains challenging, particularly in communication-constrained environments involving multi-task coupling. Existing methods suffer from two limitations: (1) the inability to effectively integrate and process incomplete state from disparate agents, and (2) a lack of robust mechanisms for handling complex multi-task coupling. To address these challenges, we propose the Coupled Communication-Task Decoupling (CCTD) framework. CCTD introduces two critical innovations: first, a distributed state compensation mechanism to process historical data, thereby reconstructing accurate global states from partial observations; second, a hierarchical architecture that systematically decomposes complex tasks into manageable subtasks while preserving their interdependencies. Thanks to its modular design, CCTD can integrate with existing MARL algorithms and allow for flexible combination of various subtasks. Extensive experiments demonstrate that CCTD outperforms baseline methods, achieving a 10% improvement in communication reception rate and superior performance across all subtasks in multi-task environments.

This paper proposes a novel method for transferable adversarial attacks from Image Quality Assessment (IQA) to Video Quality Assessment (VQA) models. Attacking modern VQA models is challenging due to their high complexity and the temporal nature of video content. Since IQA and VQA models share similar low- and mid-level feature representations, and IQA models are substantially cheaper and faster to run, we leverage them as surrogates to generate transferable adversarial perturbations. Our method, MaxT-I2VQA jointly Maximizes IQA scores and Targets IQA feature activations to improve transferability from IQA to VQA models. We first analyze the correlation between IQA and VQA internal features and use these insights to design a feature-targeting loss. We evaluate MaxT-I2VQA by transferring attacks from four state-of-the-art IQA models to four recent VQA models and compare against three competitive baselines. Compared to prior methods, MaxT-I2VQA increases the transferability of an attack success rate by 7.9% and reduces per-example attack runtime by 8 times. Our experiments confirm that IQA and VQA feature spaces are sufficiently aligned to enable effective cross-task transfer.