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16th International Conference on Computer Supported Education, CSEDU 2024

作者： Francišković, Teodor Sakal Anđelić, Ana Slivka, Jelena Luburić, Nikola Kovačević, Aleksandar Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897586972

Flipped Classroom (FC) is an active learning design requiring the students to engage in pre-class learning activities to prepare for face-to-face sessions. Identifying FC learning behaviors that lead to academic success remains a challenge. This paper addresses this challenge by conducting an empirical study in an undergraduate software engineering course employing the FC model. The empirical study draws on the data from an intelligent tutoring system that captured the learning traces of students performing pre-class activities. These traces provided indicators of students’ regularity of engagement, which were then matched to their final exam scores. Regression models were trained to predict final exam scores based on the regularity of engagement indicators. Students were clustered based on their regularity of engagement indicators to uncover actionable feedback for future course iterations. The case study confirms the generalizability of earlier findings that the regularity of engagement is vital for student performance and that course-specific predictors significantly impact the models’ prediction performance. Copyright © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)

关键词： Students

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Detection of Hyperbolic Reflections on Real GPR Data Using YOLO Model Trained with Synthetic Data 47

Detection of Hyperbolic Reflections on Real GPR Data Using Y...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Bugarinović, Željko Gavrilović, Milan Ristić, Aleksandar Bugarinović, Milka Šarkanović Radulović, Aleksandra Govedarica, Miro University of Novi Sad Faculty of Technical Sciences Department for Computing and Control Engineering Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9798350365597

This paper analyzes the application of the YOLO algorithm to automatically recognize hyperbolic reflections on radargrams. A specific shape of hyperbolic reflection on radargrams occurs with objects of circular cross-section that are scanned perpendicular to their axis, such as underground pipes. Requirements for processing a large number of radargrams are increasingly being imposed in practice. Therefore, the development of algorithms for the automated finding of hyperbolic reflections on radargrams has been intensified in recent years. Conventional methods take a lot of time and are more susceptible to noise than algorithms based on deep learning. One of such algorithms is also applied in this paper. The model was trained with 2000 synthetic radargrams generated using gprMax software. The basic idea is reflected in the analysis of the successful detection of hyperbolic reflections on real radargrams using a model trained only with synthetic radargrams. Experimental results show a high level of success. © 2024 IEEE.

关键词： Ground penetrating radar systems

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Smooth Motion Planning of an Omnidirectional Mobile Robot in Dynamic Environments

Smooth Motion Planning of an Omnidirectional Mobile Robot in...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Šelek, Ana Seder, Marija Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a real-time smooth motion planning method for a four mecanum wheeled omnidirectional mobile robot in dynamic environments that generates a smooth collision-free trajectory is proposed. The method employs a graph-search-based path planning algorithm and a smoothing technique based on clothoids. It is suitable for real-time operation due to its computational simplicity and allows fast path replanning in case the robot encounters unknown and dynamic obstacles. Such a path is feasible for holonomic mobile robots since it does not contain sharp turns so the energy consumption is reduced. Our method is compared to the reactive model predictive control scheme in simulations. The comparison showed advantages of the proposed method in the reduced task execution time, path length, cumulative replanning time, and smooth velocity profile. © 2022 IEEE.

关键词： Motion planning

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4K Video Coding Efficiency in UAV Systems 45

4K Video Coding Efficiency in UAV Systems

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331035

With increasing requirements for high quality, low bandwidth video transmission systems, comes a demand for more ad hoc video encoders. Unmanned Aerial Vehicles (UAVs), or just drones, have recently grown in popularity and use. Therefore, in this paper, we provide an efficiency comparison of the two most popular video encoders in a First-person view (FPV) drone piloting system. We film all the test sequences ourselves, using a drone with an attached 4K camera. Each sequence captures a scene with different spatiotemporal characteristics. Because video encoders are sensitive to video content or scene complexity, instead of doing a comprehensive comparison of all sequences at once, we first classify all the sequences into four groups. Finally, we examine the compression efficiency by varying the parameters with the highest impact on encoder operation. The quality of compressed footage is determined using four objective video quality measures. Due to high computational complexity, tests were performed on several nodes of a supercomputer and the relative encoding times were also considered. © 2022 Croatian Society MIPRO.

关键词： Antennas

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BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization 26

BiERL: A Meta Evolutionary Reinforcement Learning Framework ...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wang, Junyi Zhu, Yuanyang Wang, Zhi Zheng, Yan Hao, Jianye Chen, Chunlin Department of Control Science and Intelligent Engineering Nanjing University China College of Intelligence and Computing Tianjin University China

ISBN: (纸本)9781643684369

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters). In the paper, we propose a general meta ERL framework via bilevel optimization (BiERL) to jointly update hyperparameters in parallel to training the ERL model within a single agent, which relieves the need for prior domain knowledge or costly optimization procedure before model deployment. We design an elegant meta-level architecture that embeds the inner-level's evolving experience into an informative population representation and introduce a simple and feasible evaluation of the meta-level fitness function to facilitate learning efficiency. We perform extensive experiments in MuJoCo and Box2D tasks to verify that as a general framework, BiERL outperforms various baselines and consistently improves the learning performance for a diversity of ERL algorithms. © 2023 The Authors.

关键词： Reinforcement learning

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Accelerating Heterogeneous Federated Learning with Closed-form Classifiers 41

Accelerating Heterogeneous Federated Learning with Closed-fo...

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41st International Conference on Machine Learning, ICML 2024

作者： Fanì, Eros Camoriano, Raffaello Caputo, Barbara Ciccone, Marco Department of Computing and Control Engineering Polytechnic University of Turin Italy Istituto Italiano di Tecnologia Genoa Italy CINI Consortium Rome Italy

Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively impacting convergence speed and accuracy. To address this issue, we introduce Federated Recursive Ridge Regression (FED3R). Our method fits a Ridge Regression classifier computed in closed form leveraging pre-trained features. FED3R is immune to statistical heterogeneity and is invariant to the sampling order of the clients. Therefore, it proves particularly effective in cross-device scenarios. Furthermore, it is fast and efficient in terms of communication and computation costs, requiring up to two orders of magnitude fewer resources than the competitors. Finally, we propose to leverage the FED3R parameters as an initialization for a softmax classifier and subsequently fine-tune the model using any FL algorithm (FED3R with Fine-Tuning, FED3R+FT). Our findings also indicate that maintaining a fixed classifier aids in stabilizing the training and learning more discriminative features in cross-device settings. Official website: https://***/. Copyright 2024 by the author(s)

关键词： Federated learning

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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IEEE Transactions on Services computing 2025年第3期18卷 1753-1768页

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

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Position Paper: Enhancing the Learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding 17

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Kinect-Based Evaluation of Severity of Facial Paresis: Pilot Study 1

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Kovarik, Jan Schätz, Martin Ciler, Jakub Kohout, Jan Mares, Jan Department of Computing and Control Engineering UCT Prague Technicka 5 Dejvice Prague 616628 Czech Republic

ISBN: (数字)9783031214356

ISBN: (纸本)9783031214349

Facial paresis, temporary or permanent loss of facial muscle mobility, seriously affects the patient’s physical and mental health. Patients suffer from difficulties with eating, salivating or speaking. Facial rehabilitation is usually the solution to facial paresis. However, during the rehabilitation facial muscle mobility must be monitored. Unfortunately, the assessment of facial mobility usually depends on the subjective opinion of the doctor. The aim of this pilot study was to design a method of objective identification of a healthy patient and a patient with facial paralysis. Using a depth sensor Kinect a few records of facial movements of health patient were taken for both symmetric and asymmetric movement. Using statistical methods the most suitable of 1347 facial landmarks were chosen for the classification model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biomedical data Facial paresis Kinect Statistical methods

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