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IEEE International Conference on Communications (ICC)

作者： Yuan Chen Xiuhua Li Guozeng Xu Ling Liu Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

关键词： Analytical models Sensitivity Costs Computational modeling Simulation Heuristic algorithms Collaboration

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FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting

arXiv

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arXiv 2024年

作者： Seo, Jungwon Kim, Minhoe Rong, Chunming Department of Electrical Engineering and Computer Science University of Stavanger Stavanger4021 Norway Department of Computer Convergence Software Korea University Sejong-si30019 Korea Republic of

Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication overhead, impacting overall training efficiency. To mitigate this, prior work has explored compression techniques such as quantization. However, in heterogeneous FL settings, clients may employ different quantization levels based on their hardware or network constraints, necessitating a mixed-precision aggregation process at the server. This introduces additional challenges, exacerbating client drift and leading to performance degradation. In this work, we propose FedShift, a novel aggregation methodology designed to mitigate performance degradation in FL scenarios with mixed quantization levels. FedShift employs a statistical matching mechanism based on weight shifting to align mixed-precision models, thereby reducing model divergence and addressing quantization-induced bias. Our approach functions as an add-on to existing FL optimization algorithms, enhancing their robustness and improving convergence. Empirical results demonstrate that FedShift effectively mitigates the negative impact of mixed-precision aggregation, yielding superior performance across various FL benchmarks. © 2024, CC BY.

关键词： Federated learning

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AI-powered Career Guidance System

AI-powered Career Guidance System

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

作者： Nuha El-Khalili Kinda Suliman Dardese Nagham Thamer Alotaibi Yara Mubaydeen Iman Tahboub Software Engineering Department University of Petra Amman Jordan Data Science and Artificial Intelligence University of Petra Amman Jordan Computer Science Department University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Choosing the right major at an early age can be overwhelming. In addition, career advising is limited in many schools. Studies show that among the factors that affects career selection are personal attributes, passion, and career exploration activities. In this study, we present an AI powered application utilizing Flutter and Virtual Reality (VR) technology to assist high school and university students in making informed career choices. The application provides personalized career recommendations based on personality tests. The study analyzed a publicly available dataset combining the results of Big Five personality test with personal information including the study majors. Synthetic Minority Oversampling Technique (SMOTE) was employed to handle class imbalance. Multiple classification models (Random Forest, Gradient Boosting, Decision Tree, Naive Bayes, and KNN) were tested. Our findings demonstrate that the Random Forest classifier, achieved better performance with an accuracy of 87.3% and an AUC-ROC of 0.99.

关键词： Solid modeling Accuracy Machine learning algorithms engineering profession Virtual reality Nearest neighbor methods Prediction algorithms User experience Random forests Tuning

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Cost-Effective PUF and ECC Based Authentication Protocol for Secure Internet of Drones Communication

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IEEE Internet of Things Journal 2025年

作者： Nyangaresi, Vincent Omollo Ahmad, Musheer Maghrabi, Louai A. Althaqafi, Turki Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bondo Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai India Jamia Millia Islamia Department of Computer Engineering New Delhi 110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia Dar Al-Hekma University School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

Internet of Drones (IoD) plays a critical role in remote monitoring operations. It has been heavily deployed in applications such as traffic management, air traffic control, disaster and agricultural monitoring. In such operations, high volumes of sensitive and private data is collected and transmitted to the ground stations. The usage of unreliable and open wireless channels during the message exchange process exposes the IoD to a myriad of security threats. Therefore, numerous authentication protocols have been developed in the recent past to mitigate these threats. However, most of these protocols are still inadequate in the provision of robust security against typical IoD attacks. Because, they involve resource-intensive operations and make them unsuitable for an IoD environment. Therefore, we put forward a new authentication protocol which exhibits costeffective performance. To develop the new protocol for secure IoD communication, we leverage on biometrics, physically unclonable function and elliptic curve cryptography for the robust performance. Extensive formal security analysis using the Random Oracle Model (ROM) is showing that proposed protocol is provably secure. The informal security analysis is also performed which demonstrates its robustness against all the threat assumptions in the Canetti-Krawczyk (C-K) model. Specifically, it is found to offer conditional privacy, unlinkability, anonymity, mutual authentication, session key establishment and perfect key secrecy. Moreover, it is capable enough to mitigate numerous threats such as forgery, impersonation, stolen verifier, replay, physical capture, de-synchronization, known secret key and smart card loss attacks. The performance is cost-effective as evident from the comparative analysis showing that the proposed protocol incurs the lowest energy consumptions and computation overheads at relatively low communication costs compared to many other authentication schemes. © 2014 IEEE.

关键词： Authentication elliptic curve cryptography Internet of Drones physically unclonable function security

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Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition

arXiv

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arXiv 2025年

作者： Wu, Tian-Shuang Lyu, Shen-Huan Chen, Ning Qu, Zhihao Ye, Baoliu Key Laboratory of Water Big Data Technology Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security concerns. However, when the number of available samples is extremely limited, class imbalance becomes a common and tricky problem. Achieving an equal number of samples across all classes is often costly and impractical in real-world applications, and previous studies on few-sample model compression have mostly ignored this significant issue. Our experiments comprehensively demonstrate that class imbalance negatively affects the overall performance of few-sample model compression methods. To address this problem, we propose a novel and adaptive framework named OOD-Enhanced Few-Sample Model Compression (OE-FSMC). This framework integrates easily accessible out-of-distribution (OOD) data into both the compression and fine-tuning processes, effectively rebalancing the training distribution. We also incorporate a joint distillation loss and a regularization term to reduce the risk of the model overfitting to the OOD data. Extensive experiments on multiple benchmark datasets show that our framework can be seamlessly incorporated into existing few-sample model compression methods, effectively mitigating the accuracy degradation caused by class imbalance. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

关键词：

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Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

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Digital Communications and Networks 2021年第4期7卷 492-504页

作者： Nyothiri Aung Weidong Zhang Kashif Sultan Sahraoui Dhelim Yibo Ai National Center for Materials Service Safely University of Science and Technology BeijingBeijing100083China Department of Software Engineering Bahria University Islamabad CampusIslamabadPakistan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charging policies have been introduced in recent ***,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management *** this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city *** proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging *** propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging *** proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection *** large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

关键词： IoV EV VANET Smart city Congestion pricing Congestion avoidance EV charging Traffic optimization

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

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Light(science & Applications) 2025年第3期14卷 746-756页

作者： Na Qian Defu Zhou Haowen Shu Ming Zhang Xingjun Wang Daoxin Dai Xiao Deng Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBejingChina State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouChina

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.

关键词： analog parallel processor sharing hardware platform multifunctional integrated systems establish full cooperation different functionalities analog parallel processor app based silicon photonic platform electronic circuitryherewe broadband signal

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Real-Time Simulation of a Fast Charger Using a Low-Cost FPGA Platform 48

Real-Time Simulation of a Fast Charger Using a Low-Cost FPGA...

引用

48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Meddah, Karim Chalangar, Hossein Ould-Bachir, Tarek Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontréalQCH3T 1J4 Canada

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Advances in semiconductor technologies bring to the market power electronic converters (PECs) switched at high frequencies, and make their real-time simulation and testing using a hardware-in-loop configuration difficult. Various approaches have been reported in the literature that demonstrate the feasibility of simulating high switching frequency (HSF) PECs in real-time, but require high-end devices. This paper demonstrates that low-cost FPGA platforms can be leveraged for the accurate real-time simulation of HSF of PEC. The proposed platforms make use of a solver that uses state variables from the previous time-point to determine the status of uncontrolled switches. The performance of the proposed approach is assessed using a battery charger test case. The paper demonstrates that the proposed method is latency-and resource-efficient, and achieves a time-step of 32 ns. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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