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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tsai, Ho-Jung Wu, Hsiao-Kuang Chen, Chun-Chuan Yeh, Chen-Hsin Chu, Tzu-Yueh Yeh, Shih-Ching National Central University Department of Computer Science and Information Engineering Taiwan National Central University Department of Biomedical Sciences and Engineering Taiwan Hunan University College of Computer Science and Electrical Engineering China

ISBN: (纸本)9798350363586

Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the development of explainable artificial intelligence (XAI) offers a solution to this problem. In this study, we employed a VR-based GO/NOGO task with distractions, capturing participants' eye movement, head movement, and electroencephalography (EEG) data. We used the collected data to train an explainable multimodal fusion model. Besides classifying between ADHD and normal children, the proposed model also generates explanation heatmaps. The heatmaps provide the importance of specific variables and timestamps in the EEG data to help us analyze the patterns captured by the model. According to our observations, the model identified specific time intervals that related to specific event-related potentials (ERPs) components. The heatmaps also demonstrate that the impacts of distractions vary between not only the GO and NOGO events but also ADHD and normal children. © 2024 IEEE.

关键词： Eye movements

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CEGSet: Collection of standardized cause-effect graph specifications 12

CEGSet: Collection of standardized cause-effect graph specif...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs 22

Optimization of the test case minimization algorithm based o...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Krupalija, Ehlimana Cogo, Emir Bećirović, Šeila Prazina, Irfan Pozderac, Damir Bešić, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781665475464

Many different methods are used for generating blackbox test case suites. Test case minimization is used for reducing the feasible test case suite size in order to minimize the cost of testing while ensuring maximum fault detection. This paper presents an optimization of the existing test case minimization algorithm based on forward-propagation of the cause-effect graphing method. The algorithm performs test case prioritization based on test case strength, a newly introduced test case selection metric. The optimized version of the minimization algorithm was evaluated by using thirteen different examples from the available literature. In cases where the existing algorithm did not generate the minimum test case subsets, significant improvements of test effect coverage metric values were achieved. Test effect coverage metric values were not improved only in cases where maximum optimization was already achieved by using the existing algorithm. © 2023 IEEE.

关键词： Black-box testing

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Fiber Bragg Gratings Sensor System for Kinematic and Dynamic Assessment in Lower Limb Robotic Rehabilitation

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IEEE Sensors Journal 2025年第11期25卷 19275-19281页

作者： Lorenzutti, Pedro Coimbra, Joao Alexandre Menegardo, Rafael Mayag, Luis Souza, Edson Morais, Eliton Frizera, Anselmo Diaz, Camilo Leal-Junior, Arnaldo Informatics Department Federal University of Espirito Santo Vitoria29075-910 Brazil Federal University of Espirito Santo Electrical Engineering Department Vitoria29075-910 Brazil Federal University of Espirito Santo Mechanical Engineering Department Vitoria29075-910 Brazil VSB - Technical University of Ostrava Department of Cybernetics and Biomedical Engineering Ostrava70833 Czech Republic

This paper presents the development and application of a fiber Bragg grating (FBG) based sensor system for dynamic and kinematics assessment of an exoskeleton for lower limb rehabilitation. The exoskeleton has 2 degree-of-freedom, one at the knee joint and the other for the ankle joint, which is positioned on a chair for robotic-assisted rehabilitation of both joints. The proposed FBG-based sensor system include an FBG array for the strain distribution assessment along the exoskeleton structure, which is used for the human-robot interaction assessment. In addition, FBG accelerometers and inclinometers are positioned on the knee and ankle joints (respectively) of the exoskeleton for the kinematic analysis of such device. The angle characterization tests indicated a root mean squared error (RMSE) for angle assessment of 3.17° and 1.64° for knee and ankle joint, respectively, which results in a relative errors of 3.96% and 6.07% considering the range of movement of both joints. Moreover, the strain sensors indicated a determination coefficient of 0.999 for FBGs 1 and 2. Thereafter, the sensor system is applied on the knee and ankle joint angle measurements as well as the human-robot interaction via strain distribution along the structure. In this case, the tests with an user indicate the variations of knee and ankle joints within the range of movement applied by the exoskeleton's controller. Furthermore, the interaction forces between the user and the exoskeleton are evaluated using the strain distribution along the exoskeleton structure. © 2025 IEEE.

关键词： Exoskeleton (Robotics)

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Assessing the Viability of AC to DC Microgrid Conversion: A Comprehensive Analysis of Technical and Economic Factors 59

Assessing the Viability of AC to DC Microgrid Conversion: A ...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus Pv Technology Laboratory Department Of Electrical and Computer Engineering Cyprus Phaethon Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350379730

In the face of global warming and the acceleration of climate changes, urgent and decisive measures must be taken to mitigate these phenomena. This study addresses this urgent need by exploring the transition from traditional AC distribution systems to more efficient DC and hybrid AC/DC systems, recognizing the utmost importance of both technical developments and economic considerations. Through a thorough overview of existing literature and empirical knowledge gathered from the nanogrid of FOSS Research Centre for Sustainable Energy of University of Cyprus, this research clarifies the inherent advantages of DC and AC/DC hybrid systems. In addition, it presents a detailed case study for the comprehensive assessment of the feasibility and effectiveness of these systems. The preliminary economic analysis carried out in this study demonstrates the cost-effectiveness and performance improvements offered by hybrid AC/DC microgrids compared to conventional AC configurations. According to the findings of this work it is obvious that the ROI increases with the increase of DC loads over AC loads until the case where DC loads reach the 75% of the total load (ROI=13.9%). However, even in the case of Full DC microgrid, the ROI is higher by 1% compared to the case of Full AC microgrid thus significantly reducing the payback period. These findings not only underline the urgency of the transition to more efficient and sustainable energy distribution systems, but also provide valuable information for policymakers, industry stakeholders and researchers. By embracing these innovative technologies, a more resilient and environmentally responsive energy infrastructure can be promoted that is in line with the demands of a rapidly changing global climate. © 2024 IEEE.

关键词： Clean energy

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Relevance Vector Machine for Code Smell Detection 22

Relevance Vector Machine for Code Smell Detection

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798331527471

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy. © 2024 IEEE.

关键词： Software design

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A Comparison of Selected Agent-Based Modelling Frameworks 15th

A Comparison of Selected Agent-Based Modelling Frameworks

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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A Low-Cost Real-Time Cyber Physical System for Overcoming Excess Braking Issues In Race Cars 12

A Low-Cost Real-Time Cyber Physical System for Overcoming Ex...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Samaras, V. Tatas, K. Lontos, A. Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus Frederick University Department of Mechanical Engineering Nicosia Cyprus

ISBN: (纸本)9798350321074

This paper presents the development of proof of concept for a low-cost, real-time Cyber Physical System (CPS)/IoT that deals with observed excess braking issues in Formula Student (FS) race cars. The goal is to utilize telemetry aspects through a smart monitoring system under the FS low budget competition restrictions, with low-cost materials, open-source software, Long Range wireless communication (LoRa) and cloud services. The paper also introduces an algorithm for implementing an adaptive sampling rate based on the proximity of monitored parameters to warning levels to minimize unnecessary data transmission. The system prototype was tested under realistic driving conditions, obtaining useful preliminary data that can be used to guide both driving behavior and future vehicle design. The results demonstrate that the proposed approach can be useful for the race team in FS conditions and may be possible to expand it to other similar driving scenarios outside FS. © 2023 IEEE.

关键词： Cyber Physical System

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Improved Quasi-Random Designs with Constant Pool Sizes for Group Testing 18

Improved Quasi-Random Designs with Constant Pool Sizes for G...

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Hamada, Ryouji Lu, Xiao-Nan Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering Chofu-shi Tokyo Japan Gifu University Faculty of Engineering Department of Electrical Electronics and Computer Engineering Gifu-shi Gifu Japan

ISBN: (纸本)9784885523519

Group testing is a well-studied approach for identifying defective items among a large amount of items by conducting a relatively small number of tests on pools of items. In this paper, we propose a novel method for generating quasi-random designs with constant pool sizes for noiseless non-adaptive group testing. Our approach incorporates insights from combinatorial properties to optimize the distribution of Hamming distances in pooling designs. Simulations with established practical decoding algorithms show that our quasi-random designs outperform existing random and quasi-random methods in both identification accuracy and stability. These improved designs have the potential to provide significant benefits for real-world applications of group testing, such as PCR testing in large populations and network security. © 2024 The Institute of Electronics, Information and Communication Engineers.

关键词： Hamming distance

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