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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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Innovative metallization pattern and technique for industrial rear emitter p-type tunnel oxide passivated contact solar cells

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Journal of Industrial and Engineering Chemistry 2025年 147卷 628-635页

作者： Khokhar, Muhammad Quddamah Yousuf, Hasnain Rahman, Rafi Ur Aida, Maha Nur Madara, Polgampola Chamani Jony, Jaljalalul Abedin Dao, Vinh-Ai Pham, Duy Phong Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Cuba Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Cuba Department of Future Energy Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics Faculty of Applied Sciences HCMC University of Technology and Education Ho Chi Minh City700000 Viet Nam College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Cuba

In this study, we aim to enhance the efficiency of p-type tunnel oxide passivated contact (p-TOPCon) solar cells by improving charge carrier extraction while achieving higher fill factor (FF) and open circuit voltage (Voc). We explore various potential methods for the photovoltaic industrial procedure, including optimizing metallization patterns and techniques, such as the utilization of a high efficiency grid pattern (HEGP) in place of the conventional straight finger-line pattern (SFLP). Co-firing conditions, peak firing temperature, and belt speed are investigated for their impact on FF enhancement, along with evaluating the effects of single and double print processes. Furthermore, we examine the efficacy of Nafion treatment as a surface modification technique to enhance FF. Experimental results indicate that optimizing co-firing conditions and employing Nafion treatment significantly increased FF to approximately 83.26% in p-TOPCon solar cells. Adjusting peak firing temperature and belt speed, as well as implementing the double print process, demonstrate superior FF enhancement compared to the single print process. Additionally, Nafion treatment contributes to FF improvement by passivating surface defects and enhancing charge carrier collection. Overall, this study provides valuable insights into optimizing co-firing conditions and surface treatment techniques to enhance p-TOPCon solar cell efficiency (Eff) up to 23.50%, thus advancing high-efficiency photovoltaic technology. © 2024 The Korean Society of Industrial and Engineering Chemistry

关键词： Metallizing

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks

Drone-Assisted Load Distribution Framework for Traffic Optim...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Priyanka Soni Omkar Manohar Mense Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms.

关键词： Base stations Smart cities Scalability Smart homes Internet of Things Servers Relays Optimization Drones Strain

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HYBRID FIREFLY ALGORITHM AND SPERM SWARM OPTIMIZATION ALGORITHM USING NEWTON-RAPHSON METHOD (HFASSON) AND ITS APPLICATION IN CR-VANET

arXiv

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arXiv 2025年

作者： Rehannara Beegum, T. Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Bin Shehadeh, Hisham A. Ali, Usman Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan Department of Computer and Software Technology University of Swat KPK Pakistan

This paper proposes a new hybrid algorithm, combining FA, SSO, and the N-R method to accelerate convergence towards global optima, named the Hybrid Firefly Algorithm and Sperm Swarm Optimization with Newton-Raphson (HFASSON). The performance of HFASSON is evaluated using 23 benchmark functions from the CEC 2017 suite, tested in 30, 50, and 100 dimensions. A statistical comparison is performed to assess the effectiveness of HFASSON against FA, SSO, HFASSO, and five hybrid algorithms: Water Cycle Moth Flame Optimization (WCMFO), Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA), Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA), Grey Wolf and Cuckoo Search Algorithm (GWOCS), and Hybrid Firefly Genetic Algorithm (FAGA). Results from the Friedman rank test show the superior performance of HFASSON. Additionally, HFASSON is applied to Cognitive Radio Vehicular Ad-hoc Networks (CR-VANET), outperforming basic CR-VANET in spectrum utilization. These findings demonstrate HFASSON’s efficiency in wireless network applications. © 2025, CC BY.

关键词： Vehicular ad hoc networks

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Revolutionizing Digital Media Content using a Smart Drone and an Innovative ML Technique 22

Revolutionizing Digital Media Content using a Smart Drone an...

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22nd International Learning and technology Conference, L and T 2025

作者： Almalki, Faris A. Almarzouki, Rahaf A Technology Taif University College of Computers and Information Department of Computer Engineering Taif Saudi Arabia King Abdulaziz University Collage of Economics and Admissions Department of Management Information System Jeddah21589 Saudi Arabia

ISBN: (纸本)9798331532215

In the contemporary digital age, the fields of journalism and social media assume a pivotal role in disseminating information about current events and issues to the public. However, conventional techniques for gathering news and capturing images frequently encounter constraints and limitations. In recent years, technological advancements have transformed the creation and consumption of digital media content. This study presents an innovative solution that combines a smart drone with a reinforcement learning (RL) machine learning technique to enhance digital media content in a timely manner. The proposed framework has been subjected to validation and testing in a real-world scenario in Taif City through the transfer of rich digital media content. The results demonstrate promising outcomes, which support Saudi Arabia's efforts to enrich digital media content for tourism. © 2025 IEEE.

关键词： Adversarial machine learning

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Takei, Gaku Obara, Hidemine Haneda, Ryo Nozaki, Takahiro Graduate School of Science and Technology Keio University Yokohama Japan Yokohama National University Department of Physics Electrical and Computer Engineering Yokohama Japan Keio University Department of System Design Engineering Yokohama Japan

ISBN: (纸本)9798331533892

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths. © 2025 IEEE.

关键词： motion control Multi-level inverter voice coil motor

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BPRM: Blockchain-Based Privacy Preserving and Robust Data Aggregation Supporting Multifunctionality for Fog-Assisted Smart Grid

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IEEE Internet of Things Journal 2025年第9期12卷 11664-11675页

作者： Zhao, Chuankun Wang, Liangliang Liu, Zhiquan Zhang, Kai Wang, Libo Li, Weiwei Chen, Kefei Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Jinan University College of Cyber Security Guangzhou510632 China Hainan University School of Cryptology Haikou570228 China Hangzhou Normal University Department of Mathematics Hangzhou310036 China

While the collection of users’ live or periodic electricity consumption data brings significant advantages for the operation of smart grids, it also heightens the risk of user privacy leakage. Numerous data aggregation schemes have been proposed to address this issue. However, most of these schemes either fail to accommodate the need for multifunctional data analysis or rely on a trusted third party (TTP). Given the efficient data processing capabilities offered by fog computing, we propose a blockchain-based privacy-preserving data aggregation (BPRM) scheme supporting multifunctionality for fog-assisted smart grid without TTP. This scheme ensures data confidentiality and data integrity while providing various statistical functions. In addition, we implement a consensus mechanism between smart meters, further enhancing the security and robustness of the smart grid system. Moreover, not only does the proposed the batch verification reduce the authentication costs but also support error detection in signatures. With BPRM, data center can calculate multiple statistical functions, achieving a win-win strategy. Extensive security and performance analyses demonstrate that BPRM can withstand various security threats and effectively protect user privacy while maintaining efficiency in both computational and communication overhead. © 2014 IEEE.

关键词： Data aggregation

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Prasanna Kumar Sushma S A K Chandrasekaran Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Cloud and Smart System Services Lab Siddaganga Institute of Technology Tumakuru

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms.

关键词： Costs Heuristic algorithms Dynamics Collaboration Quality of service Multitasking Minimization Delays Servers Vehicle dynamics

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Exploring Immersive Virtual Reality in Higher Education: Research Gap and Future Direction -A Scoping Review

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IEEE Access 2025年 13卷 76308-76321页

作者： Sunardi Meyliana Hendric, Spits Warnars Leslie Harco Rosmansyah, Yusep Bina Nusantara University Computer Science Department Jakarta 11480 Indonesia Bina Nusantara University Information System Department School of Information System Jakarta 11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung 40132 Indonesia

The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (10 papers), and immersion elements such as avatars and haptic feedback (six papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load - all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning desig

关键词： Higher Education Immersive Learning Learning Design Scoping review Virtual reality

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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