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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Zhao, Yue Wang, Jizhi Kong, Lingrui Sui, Tongtong Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Industrial Network and Information System Security Qilu University of Technology Shandong Academy of Sciences Jinan Shandong China Quancheng Laboratory Jinan Key Laboratory of Digital Security Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400710247

The advancement of 5G and mobile internet technologies has propelled the development of emerging businesses and applications, demanding higher requirements for network bandwidth and computational resources. To address this challenge, the computing power network (CPN) has emerged. The CPN integrates computing and networking in depth, achieving collaborative allocation of distributed computing resources, enhancing the efficient utilization of computing resources, and improving user experiences in network services. However, in the development of fields such as Extended Reality (XR), Smart Healthcare, Connected Vehicles, and Artificial Intelligence (AI), there is a multidimensional, complex, and rapidly growing demand for computing resources. Therefore, it is crucial to establish a trading mechanism that integrates multiple computing resources in CPN. In this paper, a computing power trading scheme based on blockchain and combinatorial double auction is proposed. Firstly, a computing power trading system based on blockchain was designed, leveraging the decentralized and highly secure characteristics of blockchain technology to enhance the security of computing power transactions. Furthermore, a multi-type computing power trading mechanism (MCPT) based on blockchain and combinatorial double auction was introduced. The use of combinatorial double auctions as an incentive mechanism enables the trading of multiple computing power resources and results in high social welfare. Moreover, a discrete rime optimization algorithm (DRIME) is designed in the proposed mechanism (MCPT) to better solve the computing trading problem. Through simulation experiments, the effectiveness of the approach is validated. © 2024 Copyright held by the owner/author(s).

关键词： Computing power

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Science China(information Sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN Advanced Institute of Photonics Technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial Key Laboratory of Photonics Information Technology Guangdong University of Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection （COD）,which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network（DBFN）with a parallel attention selection mechanism （PASM）.In detail,a schismatic receptive field block（SRF）combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism （HAM）,and a depth feature polymerization module （DFPM）is employed for high-level information to extract detection features in the other ***,both texture features and detection features are input into the PASM to acquire selective expression ***,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling （ASPP）and a residual channel attention block （RCAB）being applied *** results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error （MAE）,weighted F βmeasure （Fβω）,structural measure （Sα）,and E-measure （Eφ）

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary

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Pilot Backdoor Attack against Deep Reinforcement Learning Empowered Intelligent Reflection Surface for Smart Radio

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4891-4903页

作者： Huang, Yunsong Wang, Hui-Ming Wang, Zhaowei Liu, Weicheng Xi’an Jiaotong University School of Information and Communications Engineering Xi’an710049 China Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China

Intelligent reflection surface (IRS) has been used to assist communication by reflection and beamforming where a direct path is not available. Thus, IRS adjusts the wireless channel to enhance the data transmission efficiency with low power consumption. Recently, deep reinforcement learning (DRL) has been exploited in IRS coefficients optimization. IRS can be controlled by DRL to adapt their phase shift to the propagation environment and an expected reflection pattern can be obtained. However, due to the openness of wireless channel and the unexplainability of DRL, it is vulnerable to adversary attacks lauched through wireless channels. In this paper, we investigate a pilot contamination based backdoor attack against DRL based IRS beamforming, where an IRS controlled by an adversary attacker is used to contaminate the channel state information (CSI) during the training phase. The backdoor attack is covert because the adversary need not to know the legitimate pilot sequence and DRL performs well when the adversary IRS keeps inactive. We show that the backdoor attack can reduce the data rate significantly with the adversary IRS. At last, we propose a retraining method agaist the attack to recover the data rate. © 2025 IEEE. All rights reserved.

关键词： Deep reinforcement learning

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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

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Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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Frontiers of computer Science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

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Secure and Efficient Blockchain-Based Access Control Scheme with Attribute Update

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1539-1550页

作者： Zuo, Yuting Xu, Li Li, Jiguo Li, Jie Wang, Xiaoding Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Lab Shanghai200240 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

Secure data access control is widely concerned in Cyber Physical Systems (CPSs) to protect consumer rights. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an ideal solution that supports fine-grained access control. It is a pity that they fail to consider the attribute control and the verifiable legitimacy when consumer's attributes are updated. Additionally, the existing revocable CP-ABE schemes are unsuitable for resource-constrained consumer electronic devices due to their high computational and storage overhead. This paper proposes a blockchain-based access control scheme with attribute update. A new revocation mechanism is designed to guarantee the forward/backward security of the attribute update. The lightweight ciphertext update protocol is proposed to optimize the computation cost. To realize secure and efficient attribute management, this paper designs the on-chain/off-chain separation storage mode, and proposes the traceability-based attribute set generation and validation algorithm. This paper performs the formal security proof. The experimental results demonstrate that the scheme is efficient in terms of encryption and decryption. © 1975-2011 IEEE.

关键词： Ciphertext

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Density Peak Clustering Algorithm Based on Data Field Theory and Grid Similarity

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Journal of computer Science & Technology 2025年第2期40卷 301-321页

作者： Qing-Ying Yu Ge-Ge Shi Dong-Sheng Xu Wen-Kai Wang Chuan-Ming Chen Yong-Long Luo School of Computer and Information Anhui Normal UniversityWuhu 241002China Anhui Provincial Key Laboratory of Network and Information Security Wuhu 241002China

The density peak clustering algorithm can rapidly identify cluster centers by drawing decision graphs without any prior knowledge;however,when multiple density peaks are present in one cluster of the dataset,the cluster centers cannot be accurately obtained,leading to incorrect ***,the single-step allocation strategy is poorly fault-tolerant and can lead to successive allocation *** address these problems,this study proposes a density peak clustering method based on data field theory and grid similarity,which divides the original data into grid spaces to obtain subspace codes and grid ***,the potential grid density is introduced to measure the density of each grid based on the data field,and the density peaks are identified to obtain the cluster centers from the high-density ***,self-organized clustering is realized based on neighborhood extension centers and grid *** thus reduces the possibility of successive assignment errors and the negative impact of multiple density peaks on the clustering *** addition,the algorithm automatically determines the cluster centers and can correctly assign the non-density-peak grid to the corresponding *** results with synthetic and real-world datasets demonstrate that the proposed algorithm outperforms similar algorithms in terms of accuracy and efficiency when dealing with complex datasets with large density differences and cross-tangling.

关键词： clustering data field grid similarity grid space neighborhood extension center

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Measurement Manifolds Shape Clustering algorithms Synthetic data

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ADACQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment 31

ADACQR: Enhancing Query Reformulation for Conversational Sea...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lai, Yilong Wu, Jialong Zhang, Congzhi Sun, Haowen Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9798891761964

Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context. Recent works aimed to boost the performance of CQR through alignment. However, they are designed for one specific retrieval system, which potentially results in sub-optimal generalization. To overcome this limitation, we present a novel framework ADACQR. By aligning reformulation models with both term-based and semantic-based retrieval systems, ADACQR enhances the generalizability of information-seeking queries among diverse retrieval environments through a two-stage training strategy. Moreover, two effective approaches are proposed to obtain superior labels and diverse input candidates, boosting the efficiency and robustness of the framework. Experimental results on the TopiOCQA, QReCC and TREC CAsT datasets demonstrate that ADACQR outperforms the existing methods in a more efficient framework, offering both quantitative and qualitative improvements in conversational query reformulation. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding 31

SEED: Accelerating Reasoning Tree Construction via Scheduled...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Zhenglin Wu, Jialong Lai, Yilong Zhang, Congzhi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by encouraging the exploration of intermediate steps, surpassing the capabilities of chain-of-thought prompting. However, significant inference latency is introduced due to the systematic exploration and evaluation of multiple thought paths. This paper introduces SEED, a novel and efficient inference framework to improve both runtime speed and GPU memory management concurrently. Based on a scheduled speculative execution, SEED efficiently handles multiple iterations for thought generation and state evaluation, leveraging a rounds-scheduled strategy to manage draft model dispatching. Extensive experimental evaluations on three reasoning datasets demonstrate the superior speedup performance of SEED. © 2025 Association for Computational Linguistics.

关键词： Memory management

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