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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

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

ISBN: (纸本)9798331520113

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 IEEE.

关键词： Constrained optimization

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Deeper Anomaly Detection in Industrial Automation 15

Deeper Anomaly Detection in Industrial Automation

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

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

ISBN: (纸本)9798331507695

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. © 2025 IEEE.

关键词： Cyber attacks

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Masked Face Detection and Recognition Using a Unified Feature Extractor 5

Masked Face Detection and Recognition Using a Unified Featur...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Iftikhar, Aroobah Shaukat, Arslan Tariq, Rimsha College of Eme Dept of Computer and Software Engineering Islamabad Pakistan Information Technology University Dept of Computer Engineering Lahore Pakistan

ISBN: (纸本)9798350394764

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area. © 2024 IEEE.

关键词： Face recognition

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Role-Specific Physiological Responses and Quality of Experience in Collaborative Virtual Reality Tasks: A Comparative Study of Leaders and Followers 10

Role-Specific Physiological Responses and Quality of Experie...

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10th International Conference on Virtual Reality, ICVR 2024

作者： Moharana, Bhagyabati Keighrey, Conor Murray, Niall Technological University of the Shannon Dept of Computer and Software Engineering Midlands Athlone Ireland

ISBN: (纸本)9798350364231

The emergence of Collaborative Virtual Reality (CVR) technology has transformed team-based interactions across diverse fields, offering immersive and interactive environments that enhance collaborative efforts. However, a critical gap remains in understanding the Quality of Experience (QoE) from a task role-specific perspective within these environments. This study addresses this gap by evaluating the QoE differences between leaders and followers in CVR settings, using both subjective assessments and objective physiological measures. The research employs advanced biosensors, including eye-tracking and physiological responses, to examine how distinct roles influence cognitive load, emotional states, and overall user satisfaction. Participants engaged in a collaborative 'pick and place' task within a VR environment, with data collected on electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), inter-beat interval (IBI), skin temperature, and pupil dilation (PD). The NASA Task Load Index (NASA-TLX) was also used to measure perceived workload. Findings reveal significant differences in physiological responses between leaders and followers, with leaders experiencing higher cognitive and temporal demands. The results suggest that role-specific QoE evaluations can inform the design of more equitable and effective CVR systems, enhancing user satisfaction and collaboration outcomes. This study advances the field by integrating multimodal data acquisition and providing insights into CVR environments for diverse collaborative roles. © 2024 IEEE.

关键词： Helmet mounted displays

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks 11

Line Graph is the Key: An Exploration of Line Graph on Link ...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Xing, Chen Makrehchi, Masoud Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331531904

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods. © 2024 IEEE.

关键词： Graph embeddings

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal 8

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Ince, Cemile Ince, Kenan Hanbay, Davut Inönü University Computer Engineering Dept Malatya Turkey Inönü University Software Engineering Dept. Malatya Turkey

ISBN: (纸本)9798331531492

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version. © 2024 IEEE.

关键词： Pixels

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms 4

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Bertrand, Simon Germain, Pascal Tawbi, Nadia Laval University Dept. of Computer Science & Software Engineering Québec Canada

ISBN: (纸本)9798350354775

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%. © 2024 IEEE.

关键词： Deep neural networks

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Using ChatGPT in Undergraduate computer Science and software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer Science and software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer Science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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