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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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A Secure Federated Learning Framework for Residential Short-Term Load Forecasting

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IEEE Transactions on Smart Grid 2024年第2期15卷 2044-2055页

作者： Husnoo, Muhammad Akbar Anwar, Adnan Hosseinzadeh, Nasser Islam, Shama Naz Mahmood, Abdun Naser Doss, Robin Deakin University Centre for Cyber Security Research and Innovation GeelongVIC3216 Australia Deakin University Centre for Smart Power and Energy Research GeelongVIC3216 Australia La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the 'sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional FedSGD models. © 2010-2012 IEEE.

关键词： Stochastic systems

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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The Study of Multi-type Computing Power Trading Mechanism in Computing Power network Based on Blockchain and Combinatorial Double Auction 24

The Study of Multi-type Computing Power Trading Mechanism in...

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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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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Decoupled knowledge distillation method based on meta-learning

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High-Confidence Computing 2024年第1期4卷 1-6页

作者： Wenqing Du Liting Geng Jianxiong Liu Zhigang Zhao Chunxiao Wang Jidong Huo Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Aerospace Science&Industry Network Information Development Co. LTDBeijing 100854China

With the advancement of deep learning techniques,the number of model parameters has been increasing,leading to significant memory consumption and limits in the deployment of such models in real-time *** reduce the number of model parameters and enhance the generalization capability of neural networks,we propose a method called Decoupled MetaDistil,which involves decoupled *** method utilizes meta-learning to guide the teacher model and dynamically adjusts the knowledge transfer strategy based on feedback from the student model,thereby improving the generalization ***,we introduce a decoupled loss method to explicitly transfer positive sample knowledge and explore the potential of negative samples *** experiments demonstrate the effectiveness of our method.

关键词： Model compression Knowledge distillation Meta-learning Decoupled loss

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A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3311-3327页

作者： Shujiang Xu Ziye Wang Lianhai Wang Miodrag J.Mihaljevi′c Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan250014China Mathematical Institute The Serbian Academy of Sciences and ArtsBelgrade11000Serbia

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

关键词： Blockchain sharding graph partitioning algorithm

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Neural network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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