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2023 International Conference on Consumer elec.ronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Jong-Shin Yang, Cheng.Ying Yao, Jenq-Foung J.F. Yen, Chin-En Hwang, Min-Shiang Chaoyang Uni. of Tech Dept. of Info. and Comm. Eng. Taichung Taiwan University of Taipei Department of Computer Science Taipei Taiwan Georgia College and State University Dept of Computer Science Milledgeville United States Chaoyang Uni. of Tech. Dept. of Early Childhood Development and Education Taiwan Asia University Dept. of C.S. and Info. Eng. Taichung Taiwan

ISBN: (纸本)9798350324174

For a secure communication, it enables the destination could successfully release the authentic information from the sender. At the meantime, a secure system protects the transmitted information from the eavesdroppers. Traditionally, the encryption, authorization and authentication schemes could be employed for protecting the eavesdroppers. However, the unexpected attacks have been continuously developed. It could not be avoided for the undesired attacks. According to Shannon's perfect communication, there exists an error-free cryptogram that could protect for the attacker. Hence, based on the secrecy rate, it is expected that the secure communication could be practical with physical-layer coding scheme. Besides, the coding scheme is not only to obtain a critical level of security, but also to achieve a lower decoding error. For a security concern, increasing the secrecy rate leads a higher level of security ability. However, it might suffer the system performance without bandwidth efficiency. Hence, the tradeoff between the security level and coding rate is important to lead a secure communication. © 2023 IEEE.

关键词： Secure communication

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Multi-Robot Energy Persistence using Load Sharing for Battery Driven Robots

Multi-Robot Energy Persistence using Load Sharing for Batter...

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IEEE Conference on Vehicular Technology (VTC)

作者： Sanghamitra Mishra Arijit Mondal Samrat Mondal Dept. of Computer Science & Eng. Indian Institute of Technology Patna Bihar India

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

As mobile robots navigate through a warehouse collecting items from storage locations and transporting them to designated drop-off points, they consume energy. In this paper, we introduce an intelligent Robot Load Sharing (RLS) algorithm designed to minimize energy usage during item transportation within a warehouse by a fleet of mobile robots, ensuring sustained energy levels along their routes. The energy consumption at each time slot is parameterized as a quadratic function of the load and the traveling distance for a team of robots. A load-sharing framework is presented where pairs of off-loading and on-loading robots are selec.ed that decide on their meeting points and the load amount to be shared. Our algorithm is compared with different no-load-sharing scenarios and we observe a significant reduction in the total energy consumption of the warehouse.

关键词： Energy consumption Navigation Transportation Energy states Batteries Mobile robots Robots Optimization Intelligent robots Load modeling

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Improved Residual Vision Transformer for CT to MRI Translation 5

Improved Residual Vision Transformer for CT to MRI Translati...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Kundu, Souraja Iwahori, Yuji Bhuyan, Manas Kamal Bhatt, Manish Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350358018

Medical image translation across different modalities plays a crucial role in clinical diagnosis, but it often encounters challeng.s due to the variations in imaging characteristics. In this paper, we present an 'Improved Residual Vision Transformer (ResViT)' for CT to MRI image translation. Our contributions encompass three vital aspects. Firstly, we introduce a novel CNN-based encoder-decoder architecture within ResViT, explicitly designed to extract Lymph Node level features, ensuring their preservation during the translation process. Secondly, we propose the innovative 'Lymph Node Aware Loss' function, which leverages attention-based multi-scale feature extraction to faithfully transfer Lymph Node-specific features from real MRIs to synthetic MRIs. Finally, we conduct an extensive literature review and in-dept. discussions about the optimal loss terms in the generator of ResViT, with a specific focus on Lymph Node preservation. Through extensive experiments on a dataset comprising abdominal Contrast-Enhanced CT and T2-Weighted Axial MRI scans of 47 patients, we validate the effectiveness of our proposed ResViT model. The mean PSNR, SSIM, and MSE metrics exhibit substantial improvements over the existing ResViT model, showcasing the superiority of the modified ResViT in both the pre-training and fine-tuning stages. Our synthetic MRIs offer valuable contributions to data augmentation, MRI registration with respect to CT, and Lymph Node annotation tasks, underscoring their biomedical significance. © 2023 IEEE.

关键词： Magnetic resonance imaging

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QR-MO: Q-Routing for Multi-Objective Shortest-Path Computation in 5G-MEC Systems

arXiv

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arXiv 2025年

作者： Sarah, Annisa Garroppo, Rosario G. Nencioni, Gianfranco Dept. of Electrical Eng. and Computer Science University of Stavanger Stavanger Norway Dept. of Information Eng University of Pisa Pisa Italy

Multi-access edge computing (MEC) is a promising technology that provides low-latency processing capabilities. To optimize the network performance in a MEC system, an efficient routing path between a user and a MEC host is essential. The network performance is characterized by multiple attributes, including packet-loss probability, latency, and jitter. A user service may require a particular combination of such attributes, complicating the shortest-path computation. This paper introduces Q-Routing for Multi-Objective shortest-path computation (QR-MO), which simultaneously optimizes multiple attributes. We compare the QR-MO’s solutions with the optimal solutions provided by the Multi-objective Dijkstra Algorithm (MDA). The result shows the favorable potential of QR-MO. After 100 episodes, QR-MO achieves 100% accuracy in networks with low to moderate average node degrees, regardless of the size, and over 85% accuracy in networks with high average node degrees. © 2025, CC BY.

关键词： 5G mobile communication systems

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DNS Tunnel Problem In Cybersecurity

DNS Tunnel Problem In Cybersecurity

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Eng. and Natural Sciences Maltepe University İstanbul Turkey Dept. of Eng. Management and Technology College of Eng. and Computer Science The Uni. of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Name System (DNS) is the most important building block of the Internet. Websites, file transfer applications, and e-mail services use the DNS service. Therefore, these services can rarely be blocked by firewalls to prevent their access from being affected. Since applications such as firewalls and Intrusion Detection Systems (IDS) do not check the allowed protocols, attackers can open a secret path called DNS Tunnel through the DNS protocol to access sensitive data and cause many attacks. In this study, DNS Tunnel, DNS Tunnel detection methods and detection methods, and DNS attack types are included.

关键词： Deep learning Protocols Machine learning algorithms Firewalls (computing) Tunneling Malware Behavioral sciences

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Advancing AI-based Assistive Systems for Visually Impaired People: Multi-Class Object Detection and Currency Classification

Advancing AI-based Assistive Systems for Visually Impaired P...

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Information and Communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Nasir Uddin Foysal Thamid Junaeid Thamid Md Shihab Uddin Md Fakrul Islam Muhammad Nazrul Islam Faiz Al Faisal Dept. of Computer Science & Eng. Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science & Eng. Military Institute of Science and Technology Dhaka Bangladesh

Blind and Visually Impaired (BVI) persons depend on the assistive systems. An intelligent assistive system may provide situational awareness and support in the age of machine learning and computer vision with the help of deep learning and computer vision. Among many, multi-classed object detection and currency classification are the two key means for providing situational awareness and environmental information to the BVI people. Thus, the objective of this research is to propose a modified YOLOv7 and a YOLOv8 algorithm for multi-classed object detection and currency classification, respectively. To attain this objective, a modified YOLOv7 and a YOLOv8 was proposed and evaluated considering the datasets for object identification and currency classification, respectively. The dataset for object detection was created from scratch that includes 3046 photos and spans 20 distinct classes, while the dataset used for currency classification used 295 photos divided into eight distinct groups. The evaluation results showed that in comparison to the basic YOLOv7 (89.3% mAP) the modified YOLOv7 has obtained 91.4% mAP for multi-classed object detection, while the YOLOv8 has reached 94.6% mAP for currency classification comparing to the Radial Basis Function Network (91.51% mAP).

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AI-Powered Resilience: Addressing the Mental Health Impact of Mass Layoffs in the Digital Age 2

AI-Powered Resilience: Addressing the Mental Health Impact o...

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2nd International Conference on Emerging Trends in Information Technology and eng.neering, ic-ETITE 2024

作者： Avik, Suranjeet Chowdhury Chowdhury, Abdullahi Moriwam Maruf, Md Abdullah Al Naha, Ranesh Ahammad, Imtiaz Bangladesh University of Textiles Dept. of Textile Engineering Dhaka Bangladesh School of Comp. and Mathematical Sciences The University of Adelaide Adelaide Australia University of Bangladesh Faculty of Computer Science & Eng. World Dhaka Bangladesh East West University Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh Centre for Smart Analytics Federation University Australia Churchill Australia

ISBN: (纸本)9798350328202

In recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern regarding the psycho-logical impact of these layoffs on individual employees. This research delves deep into understanding the repercussions of these widespread job losses on mental health, emphasizing the increased intensity of symptoms like depression and anxiety. Utilizing the Beck Depression Inventory (BDI) for its methodology, the study scrupulously evaluates the dept. of these mental health symptoms before and subsequent to the implementation of AI-driven mental health solutions. The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities. As the world grapples with the swift changes in employment dynamics in this digital age, the study accentuates the significance of AI. Tapping into its vast potential to enhance mental resilience can be seen as an avant-garde strategy, presenting a pragmatic way to address the looming mental health challeng.s arising from unpredictable economic transitions. © 2024 IEEE.

关键词： Employment

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Generalizability of NLP-based Models for Modern Software Development Cross-Domain Environments

Generalizability of NLP-based Models for Modern Software Dev...

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Natural Language-Based Software eng.neering (NLBSE), IEEE/ACM International Workshop on

作者： Rrezarta Krasniqi Hyunsook Do Dept. of Computer Science and Eng. University of North Texas

Natural Language Processing (NLP) has shown to be effective for solving complex problems in the Software eng.neering (SE) domain, such as building chatbots and its ability to translate multi-languages. Despite the advances allowed by NLP, there are technical loopholes that hinder its fullest potential within the SE domain. The open problem remains in their generalizability for modern software development tasks that typically operate in a dynamic environment, such as AWS and SaaS platforms. The problem with these setups is that they may not contain labeled data. This poses a challeng. when applying most prominent data-centric NLP models such as BERT transformer models. This position paper highlights some of the most pressing challeng.s drawn between the intersection of NLP and SE domains. Our vision revolves around improving the NLP model generalizability for dynamic cross-domain environments that contain little or no labeled target-domain data. We discuss these challeng.s and propose a research roadmap to tackle this problem as a research community emanating from SE lenses.

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Investigating the Performance of Different Machine Learning Models for Forecasting Li-ion Battery Core Temperature Under Dynamic Loading Conditions

Investigating the Performance of Different Machine Learning ...

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IEEE International Conference on elec.ro-Information Technology

作者： Kazi Abdullah Al Arafat Sharif Md Yousuf Bhuiyan Rajib Mahamud Imtiaz Parvez Dept. of Computer Science and Engineering Atish Dipankar Uni. of Sci. and Tech. Dhaka Bangladesh Dept. of Mechanical Eng. Idaho State University Pocatello ID Dept. of Computer Science and Eng. Dept. of Computer Sci. Utah Valley University Orem UT

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This study delves into the difficulties involved in managing heat, within lithium ion (Li ion) batteries for vehicles emphasizing the impact of temperature on battery performance and safety. It stresses the importance of maintaining temperatures to extend battery lifespan and boost efficiency. By employing machine learning (ML) methods to predict battery temperatures and optimize thermal management systems this research aims to enhance battery safety, performance and longevity. The study compares models like Recurrent Neural Networks (RNNs) Multi Layer Perceptrons (MLPs) and Linear Regression to evaluate their effectiveness in forecasting battery temperature under conditions. The results suggest that incorporating ML into battery management systems can result in efficient and safer operations for automotive applications. A detailed analysis of the three ML models-MLP, RNN and Linear Regression-is carried out for predicting battery temperatures. The main objective is to assess and contrast their accuracy in forecasting based on data. Simulation outcomes such as Mean Absolute Error (MAE) Coefficient of Determination (R2) and Root Mean Squared Error (RMSE) offer insights into each models performance across criteria. This study contributes towards advancing methodologies for predicting battery temperatures thereby improving decision-making processes, for energy management systems and related uses.

关键词： Temperature sensors Temperature distribution Recurrent neural networks Linear regression Machine learning Predictive models Mathematical models Batteries Forecasting Vehicle dynamics

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Assessment of IT Governance Maturity Level for Yemeni Telec.m Sector Using COBIT 2019 Framework 1

Assessment of IT Governance Maturity Level for Yemeni Teleco...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Ghallab, Abdullatif Alswari, Tahani Alhazbi, Saleh Al-Fuhaidi, Belal Hadwan, Mohammed Al-Fahaidy, Farouk Al-Dowail, Mohammed Maqtary, Naseebah Department of Computer Science University of Science and Technology Sana’a Yemen Department of IT Open University Malaysia Sana’a Yemen Dept. of Computer Science & Eng. Qatar University Doha Qatar Department of IT Qassim University Buraydah Saudi Arabia Department of Electrical Engineering Ibb University Ibb Yemen Department of Information Systems University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Information technology (IT) plays a crucial role in business operations most organizations rely on IT resources for gaining competitive advantages and creating innovative and continuous values. This increased the importance of enterprise IT governance (EGIT) and developed the recent version of the control objectives for information and related technology (COBIT), COBIT 2019 framework. It identifies the seven components of an effective EGIT system: processes, organizational structures, information flows and items, people, skills and competencies, policies and procedures, culture, ethics and behavior, services, infrastructure, and applications. The EGIT maturity level (ITGML) assessment helps to align the EGIT with strategic objectives. This study aims to assess the ITGML of the Yemeni telec.m sector using COBIT 2019 framework. A quantitative method was applied, with a 73.6% response rate (148 out of 201 questionnaires collected). The study revealed that the current ITGML is 3.48, reflecting a defined maturity level. The findings suggested that IT governance practices based on COBIT 2019 are well documented and standardized within the Yemeni telec.m sector. © 2024 IEEE.

关键词： Telec.mmunication industry

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