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International computer Science and engineering Conference (ICSEC)

作者： Kristiawan Dwi Usmanto Nova Eka Budiyanta Eko Mulyanto Yuniarno Cyprianus Kuntoro Adi Dept. of Informatics Universitas Sanata Dharma Yogyakarta Indonesia Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

This research develops a real-time hand gesture detection system using the You Only Look Once (YOLO) algorithm for simple math learning applications aimed at children with special needs. This application is a simple drag and drop game using hand gesture indicators. The system utilizes computer vision and deep learning technology to recognize hand gestures in videos which are then converted into interactive inputs in learning media. System testing involves hyperparameter tuning methods, including learning rate, image size, and momentum adjustments. The test results show that the YOLO algorithm with hyperparameter tuning method with hyperparameter values of learning rate 0.0001, image size 320, and momentum 0.937 is effective in detecting hand gestures with an mAP@0.5: 0. 95 score of 93.27%. The test results provide the potential for using a real-time hand gesture detection system as an interesting and interactive learning method for children with disabilities, especially for those who have limitations in communication.

关键词： YOLO Training Learning systems System testing Games Detectors Media Real-time systems Tuning Videos

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Deep Predictive User Path-Inclusive Service Mobility in B5G/6G Edge Clouds

Deep Predictive User Path-Inclusive Service Mobility in B5G/...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Lusungu J. Mwasinga Syed M. Raza Duc-Tai Le Moonseong Kim Min Young Chung Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea Dept. of IT Convergence Seoul Theological University Bucheon Korea

Multi-access Edge Computing (MEC)-enabled Beyond 5G (B5G) or 6G networks require service migration to maintain service continuity and acceptable quality of experience for high-mobility User Equipment (UE). This involves migration of containerized applications to between Edge Hosts via the backhaul network as the associated UE moves away from its current gNodeB. Although crucial for both UEs and mobile network operators (MNOs), frequent migration not only prolongs service disruption time (downtime) for UEs but also increases operational expenses for the MNO. To address this problem, our study proposes a Deep Predictive User Path-Inclusive Service Mobility (DUPSM) approach for efficient management of the end-to-end migration process including deciding when and where to migrate the service. It features a deep Actor-Critic agent that learns suitable decision policy to balance cost-benefit trade-off interms of migration cost and quality of experience, respectively. As user mobility largely influences migration decisions, the proposed method incorporates user mobility path forecasting through a Generative Adversarial Network (GAN) based prediction technique to enhance ability for selecting suitable Edge Host while considering resource variations. The effectiveness of the proposed method is assessed through simulation experiments, showcasing an average reduction in service downtime of 22% when compared to a baseline scheme.

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Exploiting Information Theory for Intuitive Robot Programming of Manual Activities

arXiv

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arXiv 2024年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

Observational learning is a promising approach to enable people without expertise in programming to transfer skills to robots in a user-friendly manner, since it mirrors how humans learn new behaviors by observing others. Many existing methods focus on instructing robots to mimic human trajectories, but motion-level strategies often pose challenges in skills generalization across diverse environments. This paper proposes a novel framework that allows robots to achieve a higher-level understanding of human-demonstrated manual tasks recorded in RGB videos. By recognizing the task structure and goals, robots generalize what observed to unseen scenarios. We found our task representation on Shannon’s Information Theory (IT), which is applied for the first time to manual tasks. IT helps extract the active scene elements and quantify the information shared between hands and objects. We exploit scene graph properties to encode the extracted interaction features in a compact structure and segment the demonstration into blocks, streamlining the generation of Behavior Trees for robot replicas. Experiments validated the effectiveness of IT to automatically generate robot execution plans from a single human demonstration. Additionally, we provide HANDSOME, an open-source dataset of HAND Skills demOnstrated by Multi-subjEcts, to promote further research and evaluation in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention

Big Data Analytics for Proactive Financial Fraud Detection a...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Yehezkeil Hasudungan Fitra Putri Oganda Ora Plane Maria Daeli Raihan Nesti Anggraini Santoso Dept. of Informatics Engineering University of Raharja Tangerang Indonesia Dept. of Information System University of Raharja Tangerang Indonesia Dept. of Digital Business University of Raharja Tangerang Indonesia Dept. of Computer System University of Raharja Tangerang Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics.

关键词： Economics Technological innovation Prevention and mitigation Finance Big Data Stability analysis Real-time systems Fraud Security Financial industry

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Real-Time Person Re-Identification to Improve Human-Robot Interaction

Real-Time Person Re-Identification to Improve Human-Robot In...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Carlsen, Per Antoine Taylor, Angelique M. Chan, Darren M. Uddin, Md Zia Riek, Laurel Torresen, Jim Logistics Oda Oslo Norway Cornell University College of Computing and Information Science Cornell Tech New York United States HRL Laboratories LLC MalibuCA United States Sustainable Communication Technologies SINTEF Digital Oslo Norway University of California Dept. of Computer Science and Engineering San Diego United States University of Oslo Dept. of Informatics and RITMO Oslo Norway

ISBN: (纸本)9798350372601

Mobile robots deployed in the homes of disabled people and older adults can play a vital role as companions and aid in activities of daily living, enhancing people's quality of life. For these robots to effectively assist, it can be helpful if they possess social and interactive skills to facilitate natural and personalized interactions. Personalization, including using proper names and remembering preferences, is crucial for building strong social connections and fostering trust in human-robot interaction. However, achieving personalized interactions necessitates the robot's ability to autonomously recognize and re-identify individuals without relying on specific constraints or prior knowledge. This work introduces two lightweight Dual convolutional neural networks, LuNet Light and LuNet Lightest, designed for person re-identification in a robotic context without the limitations of existing systems. We introduce a lightweight architecture, LuNet Lightest which achieves near state-of-the-art performance on the MARS dataset. It is well-suited for real-time applications on low-cost, hardware-constrained robots. This person-identification system enables assistive mobile robots to accurately identify users, fostering trust and facilitating natural, personalized interactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Precision Localization: An Experimental Study on BLE Fingerprinting and Trilateration with ESP32

Precision Localization: An Experimental Study on BLE Fingerp...

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Ahmed H. Elhusseiny Marwa Zamzam Yasmine Zaghloul Dept. of Electrical Engineering The German International University Cairo Egypt Dept. of Informatics and Computer Science The German International University Cairo Egypt

This paper presents a method for indoor localization using two complementary techniques: Fingerprinting (FP) and Trilateration. The system is based on Bluetooth Low Energy (BLE) technology and a network of wireless anchors to determine the location of a mobile device. The FP approach collects Received Signal Strength Indicator (RSSI) data from BLE anchors in various indoor locations and creates a map of the environment in an offline stage. During the online stage, the device’s location is determined by comparing its current RSSI measurements to the map. The FP technique showed an accuracy of 79.31% without positions on the other side of a separating concrete wall and 71.88% including those positions. In contrast, trilateration calculates the position of the device using distance measurements from multiple anchors, achieving an accuracy of 44.44%. Both techniques were tested in various environments and layouts, and the results indicate that FP is more suitable for indoor localization, particularly in large buildings with many rooms. This paper highlights the potential of these two techniques for practical indoor localization applications. Moreover, one of the experimental setups for the fingerprinting technique was tested in multiple rooms separated by a concrete wall to provide information about the potential scalability of the technique.

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Machine Learning Prediction Based UI for Aircraft Cockpit

Machine Learning Prediction Based UI for Aircraft Cockpit

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International Conference on Electrical and Electronics engineering, ELECO

作者： Bilge Topal Levent Çarkacıoğlu Behçet Uğur Töreyin Dept. of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey Informatics Institute Istanbul Technical University Istanbul Turkey

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial.

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Minimizing Area Footprint of UAV Communication Security Using FPGAs

Minimizing Area Footprint of UAV Communication Security Usin...

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Evangelia Konstantopoulou George S. Athanasiou Nicolas Sklavos Computer Engineering & Informatics Dept. SCYTALE Group University of Patras Patras Hellas U-blox Athens S.A. Athens Hellas

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

Nowadays, unmanned aerial vehicles (UAVs) are used in numerous applications, facilitating human activities. However, UAVs are vulnerable to cyberattacks, particularly concerning communication. To protect UAV communication, lightweight cryptographic algorithms have been enrolled, ensuring security in such resource-constrained environments. The National Institute of Standards and Technology (NIST) promoted sponge constructions and nonlinear feedback shift registers (NFSR) as the bases for lightweight cryptography. As hardware offers significant advantages regarding performance and area-footprint over software, research moved on employing reconfigurable computing like Field-Programmable Gate Arrays (FPGAs) in this field. This paper proposes a novel security realization in UAVs based on hardware implementation of lightweight NFSR-based authenticated encryption, targeting reduced-area and uncompromised security. Xilinx Artix-7 FPGA is considered and NIST lightweight finalist TinyJAMBU mechanism is employed. The area consumption of the proposed solution is minimal, compared to existing solutions in the literature, making it ideal for UAV devices with limited bandwidth and computational resources.

关键词： Software algorithms NIST Autonomous aerial vehicles Shift registers Hardware Software Encryption

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