New Perspectives in the Development of the Artificial Sport Trainer

ABSTRACT: The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines.Akemi Galvez and Andres Iglesias have received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement no. 778035, and from the project TIN2017-89275-R funded by MCIN/AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa”

Towards a virtual coach for boccia: developing a virtual augmented interaction based on a boccia simulator

Science and Technology Publications, Lda. All rights reserved. Disability can be a factor that leads to social exclusion. Considering that involvement in society is paramount for a person with disability, participation in sports can be a powerful tool for inclusion. Based on this premise, the authors propose an intelligent virtual coach for Boccia to encourage the practice of this sport on persons with disabilities, while promoting social inclusion and shortening the learning curve for individuals new to the sport by learning about game strategy. The envisioned virtual coach will rely on Artificial Intelligence models, thus requiring the creation of large datasets, namely for ball placement and throwing movement recommendations. To answer these problems, this work is focused on the development of a Boccia simulator. With this simulator, it is possible to generate artificial gameplay images and allow the user to control an avatar with body tracking. Gesture recognition was implemented with a state-machine, thus enabling the player to throw the ball, with customizable physics, by performing one of two different throwing movements. This functionality can allow the recording of data describing the body movement associated with the placement of the ball in a certain position within the virtual court, which is essential for the proposed recommendation system.FCT - Fundação para a Ciência e a Tecnologia(690874).This article is supported by the project Deus ex Machina: NORTE – 01 – 0145 – FEDER - 000026, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Vinicius Silva also thanks FCT for the PhD scholarship SFRH/BD/133314/2017

The AI Family: The Information Security Managers Best Frenemy?

In this exploratory study, we deliberately pull apart the Artificial from the Intelligence, the material from the human. We first assessed the existing technological controls available to Information Security Managers (ISMs) to ensure their in-depth defense strategies. Based on the AI watch taxonomy, we then discuss each of the 15 technologies and their potential impact on the transformation of jobs in the field of security (i.e., AI trainers, AI explainers and AI sustainers). Additionally, in a pilot study we collect the evaluation and the narratives of the employees (n=6) of a small financial institution in a focus group session. We particularly focus on their perception of the role of AI systems in the future of cyber security

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Noise-Aware Quantum Software Testing

Quantum Computing (QC) promises computational speedup over classic computing for solving some complex problems. However, noise exists in current and near-term quantum computers. Quantum software testing (for gaining confidence in quantum software's correctness) is inevitably impacted by noise, to the extent that it is impossible to know if a test case failed due to noise or real faults. Existing testing techniques test quantum programs without considering noise, i.e., by executing tests on ideal quantum computer simulators. Consequently, they are not directly applicable to testing quantum software on real QC hardware or noisy simulators. To this end, we propose a noise-aware approach (named QOIN) to alleviate the noise effect on test results of quantum programs. QOIN employs machine learning techniques (e.g., transfer learning) to learn the noise effect of a quantum computer and filter it from a quantum program's outputs. Such filtered outputs are then used as the input to perform test case assessments (determining the passing or failing of a test case execution against a test oracle). We evaluated QOIN on IBM's 23 noise models with nine real-world quantum programs and 1000 artificial quantum programs. We also generated faulty versions of these programs to check if a failing test case execution can be determined under noise. Results show that QOIN can reduce the noise effect by more than $80\%$. To check QOIN's effectiveness for quantum software testing, we used an existing test oracle for quantum software testing. The results showed that the F1-score of the test oracle was improved on average by $82\%$ for six real-world programs and by $75\%$ for 800 artificial programs, demonstrating that QOIN can effectively learn noise patterns and enable noise-aware quantum software testing

Behavioral Profiling of SCADA Network Traffic using Machine Learning Algorithms

Mixed traffic networks containing both traditional ICT network traffic and SCADA network traffic are more commonplace now due to the desire for remote control and monitoring of industrial processes. The ability to identify SCADA devices on a mixed traffic network with zero prior knowledge, such as port, protocol or IP address, is desirable since SCADA devices are communicating over corporate networks but typically use non-standard ports and proprietary protocols. Four supervised ML algorithms are tested on a mixed traffic dataset containing 116,527 dataflows from both SCADA and traditional ICT networks: Naive Bayes, NBTree, BayesNet, and J4.8. Using packet timing, packet size and data throughput as traffic behavior categories, this research calculates 24 attributes from each device dataflow. All four algorithms are tested with three attribute subsets: a full set and two reduced attribute subsets. The attributes and ML algorithms chosen for experimentation successfully demonstrate that a TPR of .9935 for SCADA network traffic is feasible on a given network. It also successfully identifies an optimal attribute subset, while maintaining at least a .99 TPR. The optimal attribute subset provides the SCADA network traffic behaviors that most effectively differentiating them from traditional ICT network traffic

spinfortec2022 : Tagungsband zum 14. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs), Chemnitz 29. - 30. September 2022

Dieser Tagungsband enthält die Beiträge aller Vorträge und Posterpräsentationen des 14. Symposiums der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs) an der Technischen Universität Chemnitz (29.-30. September 2022). Mit dem Ziel, das Forschungsfeld der Sportinformatik und Sporttechnologie voranzubringen, wurden knapp 20 vierseitige Beiträge eingereicht und in den Sessions Informations- und Feedbacksysteme im Sport, Digitale Bewegung: Datenerfassung, Analyse und Algorithmen sowie Sportgeräteentwicklung: Materialien, Konstruktion, Tests vorgestellt.This conference volume contains the contributions of all oral and poster presentations of the 14th Symposium of the Section Sport Informatics and Engineering of the German Association for Sport Science (dvs) at Chemnitz University of Technology (September 29-30, 2022). With the goal of advancing the research field of sports informatics and sports technology, nearly 20 four-page papers were submitted and presented in the sessions Information and Feedback Systems in Sport, Digital Movement: Data Acquisition, Analysis and Algorithms, and Sports Equipment Development: Materials, Construction, Testing