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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Pairing-Free Blockchain-Assisted Certificateless Aggregation Signcryption Scheme for VANETs

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IEEE Internet of Things Journal 2025年第11期12卷 15545-15557页

作者： Wang, Yahui Peng, Cong Jia, Xiaoying Wen, Jiaming Zhang, Yuanyuan Xinyang Normal University School of Computer and Information Technology School of Software Xinyang464000 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China South-Central Minzu University School of Mathematics and Statistics Wuhan430074 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2522 Australia Hubei University of Technology School of computer science Wuhan430072 China

Smart vehicle applications play a crucial role in intelligent transportation systems, enabling sensor-equipped vehicles to establish dynamic networks for efficient collection, sharing, and aggregation. This significantly enhances road security and efficiency by transmitting crucial information to traffic authorities. However, partial research on certificateless aggregation signcryption (CLAS) scheme reveals an intriguing phenomenon where each proposed scheme consistently exhibits numerous security vulnerabilities, particularly susceptible to public key replacement attacks. To address these challenges, this paper proposes a blockchain-assisted certificateless aggregation sign-cryption scheme (BACLAS), leveraging blockchain technology to securely store users' public key on a distributed ledger and prevent public key replacement attacks effectively. Furthermore, it is a provably secure communication scheme for real-world Vehicle-to-Infrastructure (V2I) communication while addressing practical security concerns. The BACLAS scheme ensures security in terms of existential unforgeability against adaptive chosen message attacks (EUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) based on the hardness assumption of the elliptic curve discrete logarithm problem and computational Diffie-Hellman problem in the random oracle model. The proposed scheme effectively reduces computational costs and time consumption, resulting in a significant reduction of the computation burden ranging from 50.02% to 88.28% compared to other competitive schemes. Moreover, it successfully addresses the key-escrow problem, thereby achieving enhanced security properties. © 2014 IEEE.

关键词： Vehicular ad hoc networks

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Optimal Illumination Distance Metrics for Person Re-identification 21st

Optimal Illumination Distance Metrics for Person Re-identif...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Chao Wang, Zhongyuan Hu, Ruimin Wang, Xiaochen Zhou, Wen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China College of Information Engineering Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9789819601240

Person re-identification (Re-ID) has been widely used in public security and surveillance. Due to the influence of different shooting times and locations, can lead to lighting variations in the images captured by the camera, and the lighting variations seriously affect the Re-ID performance. Existing methods utilize images under different lighting conditions to transform to uniform lighting conditions to eliminate the effect of lighting on Re-ID. However, these methods ignore the fact that different illumination metric distances have different recognition performances. Therefore, we propose the optimal illumination distance metric, by analyzing different light distances for pedestrians in different light conditions, we find that there exists an optimal light condition in which the distance between image pairs is smaller than the distance in other light conditions. The distance under the optimal lighting condition is used in the retrieval process as the distance between its final sorted image pairs. To verify the effectiveness of our method, Market-1501-IA and Duke-MTMC-IA datasets are constructed, and a large number of experiments are conducted under different lighting conditions to verify the existence of the optimal lighting conditions and the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models 2

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Nia, Nooshin Nooraei Shirzad, Nasratullah Osemeha, Nwabueze Kenneth Near East University Department of Computer Information Systems Nicosia99138 Cyprus Artificial Intelligence Near East University Nicosia Cyprus Near East University Software Engineering Department Nicosia Cyprus

ISBN: (纸本)9798331505745

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM. © 2025 IEEE.

关键词： Recurrent neural networks

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Autonomous sortie scheduling for carrier aircraft fleet under towing mode

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Defence Technology(防务技术) 2025年第1期43卷 1-12页

作者： Zhilong Deng Xuanbo Liu Yuqi Dou Xichao Su Haixu Li Lei Wang Xinwei Wang Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China School of Mathematical Sciences Dalian University of TechnologyDalian 116024China Naval Aviation University Yantai 264001China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094China Department of Automation School of Information Science and TechnologyTsinghua UniversityBeijing 100084China

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft *** primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch *** this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource ***,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as *** analyzing the constraints in sortie scheduling,a mixed-integer planning model is *** particular,the constraint on preparing spot occupancy is improved to further enhance the sortie *** basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance *** efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is *** solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing *** animation of the simulation results is available at ***/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

关键词： Carrier aircraft Autonomous sortie scheduling Resource allocation Collision-avoidance Hybrid flow-shop scheduling problem

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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

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IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Economic Operation Strategy for Fast Charging Station Assisted by HESS Consisting of Retired Batteries Considering Concurrent Decommissioning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7897-7909页

作者： Zhang, Ronghui Zhang, Kai Manandhar, Ujjal Wang, Benfei Qu, Xiaobo Chen, Hong Sun Yat-sen University Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Guangdong 510275 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Tsinghua University School of Vehicle and Mobility Beijing100084 China Tongji University College of Electronics and Information Engineering Shanghai200092 China

The wide adoption of electric vehicles (EVs) leads to the need for fast charging stations (FCSs) with battery energy storage systems (BESSs) due to charging anxieties and various charging behaviors, and results in numerous retired batteries (RBs) necessitating the recycling solutions. To address these issues simultaneously, this study proposes an economic operation strategy for FCSs assisted by the RB-based hybrid energy storage system (HESS). In the proposed strategy, the FCS economic operational model is established, where a rolling mixed-integer linear programming (RMILP) mechanism is adopted considering uncertain charging behaviors, and the fuzzy C-means (FCM) algorithm is adopted to cluster RBs to form the RB-based HESS. Moreover, a concurrent decommissioning method is introduced for the RB-based HESS lifespan extension and cost reduction, with the assistance of a state-of-health-based droop control for power allocation optimization among RBs. Finally, various simulation and experimental studies have been conducted to verify the effectiveness of the proposed economic operation strategy. The results demonstrate that FCSs with the RB-based HESSs achieve 67.32% annual net revenue (ANR) superiority over FCSs without BESSs, and 12.69% ANR increment over those with BESSs. Moreover, the lifespan of the RB-based HESS can be extended by 11.23% with the concurrent decommissioning method. © 2015 IEEE.

关键词： Charging stations

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