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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo Kim, Jong Yeong Mun, Seo Park, BoJeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electronic Engineering Busan Korea Republic of Dong-A University Dept.of Mechanical Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This study proposed a body movement-based system for efficiently controlling robots in large indoor environments. Traditional hand gesture recognition systems experience significant accuracy loss in large indoor spaces, particularly as the distance between the user and the camera grows. To address this issue, clear and large body movements were used as control signals instead of hand movements. For example, participants could give commands to the robot by raising both hands or lifting one hand in a specific direction. The system utilized MediaPipe Pose to track the user's posture in real time and communicated commands from the server to the robot via TCP/IP socket communication. This structure ensured stable data transmission, enabling smooth robot control even in expansive spaces. Consequently, this research demonstrated the feasibility of effective robot control in large indoor areas, such as screen golf courses, and suggested the potential for system expansion in various indoor environments. © 2025 IEEE.

关键词： Human robot interaction

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Deeper Anomaly Detection in Industrial Automation

Deeper Anomaly Detection in Industrial Automation

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Ho-Seok Jung Sung Won Jang Youngmi Baek Dept. of Computer Software Engineering Changshin University Korea Dept. of Computer Engineering Changshin University Korea

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

The security of industrial networks, particularly in industrial automation systems, is critical for ensuring system reliability and protecting sensitive data. This paper proposes a deeper anomaly detection system using the ResNet34 (Residual Network) model to identify and detect cyber-attacks in industrial networks, specifically focusing on Controller Area Network (CAN) systems. The study highlights the vulnerabilities in industrial communication protocols, such as CAN, Modbus, and Ethernet/IP, which are susceptible to cyber-attacks including replay, modification, and fuzzing attacks. These attacks can disrupt the functioning of industrial systems and cause significant damage. Experimental results show that the proposed model achieves a 100 % detection rate for all types of cyber-attacks, demonstrating its effectiveness in recognizing abnormal patterns and responding to changes in network behavior. The results confirm that the ResNet34-based deep anomaly detection model can be a valuable tool for strengthening the security of industrial networks by providing real-time detection of cyber-attacks, thereby ensuring the stability and safety of industrial automation systems.

关键词： Automation Stability analysis Real-time systems Safety Controller area networks Security Reliability Cyberattack Anomaly detection Residual neural networks

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Enhanced Reversible Data Hiding in Encrypted Images by Pixel Shifting 16

Enhanced Reversible Data Hiding in Encrypted Images by Pixel...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Yang, Ching-Nung Lu, Yu-Quan Liu, Shu-Yu Xiong, Lizhi Dept. of Computer Sci. and Inf. Engineering National Dong Hwa University Hualien Taiwan School of Computer and Software Nanjing University of Inf. Sci. & Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) is accordingly proposed. With the reversibility and hiding data in encrypted image, RDHEI has potential utility in fields such as military and medical image transmission, and forensic analysis. Recently, Panchikkil et al. propose a RDHEI by using pixel swapping (RDHE-PSW) to swap some pixels in a block. The pixel swapping does not change pixel values, and thus Panchikkil et al.’s RDHEI-PSW can retain entropy and histogram. The smoothness of a block is calculated to extract the hidden bits and meantime recover the cover image. Panchikkil et al.’s RDHEI-PSW only hides two secret bits in every block. Recently, Kim et al. use pixel shifting (PSH), a circular right shift on a block, instead of pixel swapping to propose a RDHEI-PSH. In this paper, we still adopt PSH approach but use it in a different way (a modified PSH) to propose an enhanced RDHEI-PSH with a higher embedding capacity than Kim et al.’s RDHEI-PSH. Our enhanced RDHEI-PSH double the patterns on a block, on which we can embed more secret bits. Experimental results demonstrate that our enhanced RDHEI-PSH surpasses Panchikkil et al.’s RDHEI-PSW and Kim et al.’s RDHEI-PSH. © 2025 SPIE.

关键词： Military photography

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Open RAN Slicing with Quantum Optimization

arXiv

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arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025, CC BY.

关键词： Constrained optimization

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Open RAN Slicing with Quantum Optimization

Open RAN Slicing with Quantum Optimization

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International Global Information Infrastructure Symposium (GIIS)

作者： Patatchona Keyela Soumaya Cherkaoui Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

关键词： Annealing Training data Open RAN Quality of service Reinforcement learning Ultra reliable low latency communication Quantum annealing Resource management Optimization Faces

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A Comparative Analysis of Data Resampling Techniques for Addressing Class Imbalance in Mosquito Classification

A Comparative Analysis of Data Resampling Techniques for Add...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training Visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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3D Face Reconstruction with Graph Attention Based on Action Unit Features

3D Face Reconstruction with Graph Attention Based on Action ...

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International Conference on Information Networking

作者： Hyeonjin Kim Hyukjoon Lee Jinheung Kong Dept. of Computer Engineering Kwangwoon University Seoul Korea Dept. of Embedded Software Enngeeing Kwangwoon University Seoul Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The reconstruction of 3D face shapes and expressions from 2D images remains unconquered due to the lack of detailed modeling of human facial movement based on the correlation between the different parts of faces. We propose to use facial action units (AUs), which are detailed taxonomy of the human facial movements based on observation of activation of muscles or muscle groups, in the 3D facial expression reconstruction to allow for detailed modeling of various facial expression types. We present a novel 3D face reconstruction framework called Action Unit feature-based Graph Attention Network Encoder (AUGANE) that can generate a 3D face model that is responsive to AU activation given a single monocular 2D image to capture expressions. AUGANE leverages AU-specific features as well as facial global features to enable accurate 3D facial expression reconstruction with Graph Attention Network Encoder (GANE). We also introduce a novel loss function which is to force learning toward the minimal discrepancy in AU activation between the input and rendered reconstruction. The experimental results demonstrate the superior performance of the proposed framework over state-of-the-art methods.

关键词： Solid modeling Gold Three-dimensional displays Shape Taxonomy Force Muscles Transformers Image reconstruction Faces

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Explainable Multiclass Blood Cell Classification: Combining Custom CNNs with SHAP

Explainable Multiclass Blood Cell Classification: Combining ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Shantanu Dey Anik Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Birulia Savar Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Blood cell classification is a crucial task in medical diagnostics. This work can identify different blood cell types that play essential roles in the immune system and overall body function. This research introduces a custom Convolutional Neural Network (CNN) model with SHAP for explainable multiclass classification of blood cell images. We classify eight types of blood cells—basophil, eosinophil, erythroblast, immunoglobulin (Ig), lymphocyte, monocyte, neutrophil, and platelet. An ablation study was conducted to optimize the CNN architecture. We compared its performance with eleven transfer learning models, including VGG16, ResNet101, and EfficientNet variants. Our model achieved 92.60% accuracy while maintaining lower complexity. The use of SHAP provides transparency by highlighting crucial features in the model’s decision-making process.

关键词： Training Accuracy Microprocessors Transfer learning Lymphocytes computer architecture Convolutional neural networks Medical diagnosis Blood Testing

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering Systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - computer and Information Sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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