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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

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Influence of dextran solution and corneal collagen crosslinking on Corneal Biomechanical Parameters Evaluated by Corvis ST

Research Square

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Research Square 2021年

作者： Qin, Xiao Tian, Lei Zhang, Hui Zhang, Di Guo, Li-Li Zhang, Hai-Xia Jie, Ying Li, Lin Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing100069 China School of Biomedical Engineering Capital Medical University Beijing100069 China Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Capital Medical University Beijing Tongren Hospital Beijing100730 China

Purpose: To analyze the influence of dextran solution and corneal collagen crosslinking (CXL) on corneal biomechanical parameters evaluated by Corneal visualization Scheimpflug Technology (Corvis ST). Materials and Methods: Forty porcine eyes were included in this study. Twenty porcine eyes were soaked in dextran solution for 30 minutes (10 eyes in 2% dextran solution and 10 eyes in 20% dextran solution). CXL treatment was performed in 10 porcine eyes, the other 10 porcine eyes were regarded as a control group. Each eye was fixed on an experimental inflation platform to carry out Corvis measurements at different IOPs. Corneal biomechanical parameters were calculated based on Corvis measurement. Statistical analysis was used to analyze the influence of dextran solution and CXL on corneal biomechanical parameters based on Corvis parameters. Results: Corneal energy absorbed area (Aabsorbed) decreased after being soaked in dextran solution under IOP of 15 mmHg;Corneal elastic modulus (E) decreased after being soaked in 2% dextran solution and increased after being soaked in 20% dextran solution;SP-A1 increased after CXL. Conclusion: Both dextran solution and CXL can change corneal biomechanical properties;SP-A1 may be used as an effective parameter for the evaluation of CXL. © 2021, CC BY.

关键词： visualization

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Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives

arXiv

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arXiv 2024年

作者： Wu, Xuan Wang, Di Wen, Lijie Xiao, Yubin Wu, Chunguo Wu, Yuesong Yu, Chaoyu Maskell, Douglas L. Zhou, You The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin university Changchun130012 China The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore The School of Software Tsinghua University Beijing100084 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing surveys did not cover the state-of-the-art (SOTA) NCO solvers emerged recently. More importantly, to provide a comprehensive taxonomy of NCO solvers with up-to-date coverage, based on our thorough review of relevant publications and preprints, we divide all NCO solvers into four distinct categories, namely Learning to Construct, Learning to Improve, Learning to Predict-Once, and Learning to Predict-Multiplicity solvers. Subsequently, we present the inadequacies of the SOTA solvers, including poor generalization, incapability to solve large-scale VRPs, inability to address most types of VRP variants simultaneously, and difficulty in comparing these NCO solvers with the conventional Operations Research algorithms. Simultaneously, we propose promising and viable directions to overcome these inadequacies. In addition, we compare the performance of representative NCO solvers from the Reinforcement, Supervised, and Unsupervised Learning paradigms across both small- and large-scale VRPs. Finally, following the proposed taxonomy, we provide an accompanying web page as a live repository for NCO solvers. Through this survey and the live repository, we hope to make the research community of NCO solvers for VRPs more thriving. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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Poisoning attack against estimating from pairwise comparisons

arXiv

引用

arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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A Text Multi-label Classification Scheme Based on Resampling and Ensemble Learning 8th

A Text Multi-label Classification Scheme Based on Resampling...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Tianhao Weng, Tianrang Ji, Jiacheng Zhong, Mingjun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China Department of Computing Science University of Aberdeen AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9783031067600

The medical dispute cases are professional and closely related to medicine. Therefore, the mediation of cases in practice depends heavily on similar historical cases. Multi-label classification of legal documents can efficiently filter irrelevant historical cases, which helps to recommend the similar historical cases faster and better. However, the imbalance and label symbiosis of the data set directly affect multi-label classification of legal documents. Therefore, a multi-label classification scheme based on resampling and ensemble learning is presented in this paper The scheme includes two parts: in the first part, in order to reduce the impact of label symbiosis on resampling, a resampling algorithm based on the average sparsity of the label set is proposed improve the imbalance of the data set;in the second one, a multi-label classification algorithm based on ensemble learning is proposed to train multiple base classifiers and combine each base classifier with a voting strategy of one vote. It can effectively improve the effect of multi-label classification. The experimental results show that the scheme proposed in this paper can improve the effect of multi-label classification and is not only suitable for legal documents but also applicable for other text data sets with imbalanced classes and label symbiosis problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Joint Design for Cramér-Rao Bound and Secure Transmission in Semi-IRS Aided ISAC Systems

Joint Design for Cramér-Rao Bound and Secure Transmission i...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Wenjing Wei Xiaowei Pang Xiaoqi Qin Shiqi Gong Chengwen Xing Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China College of Computing & Data Science (CCDS) Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

We study a semi-passive intelligent reflecting surface (IRS) enabled ISAC, where IRS is employed to assist the secure communication and perform target sensing. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. We derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve the performance tradeoff, we design a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes.

关键词： Measurement Vehicular and wireless technologies Direction-of-arrival estimation Array signal processing Simulation Estimation Sensors

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A New Method for Quantifying Workload of Drivers and Application in Analyzing Stabilization Time after Critical Events in Varying Automation Levels

SSRN

引用

SSRN 2022年

作者： Zhai, Junda Lu, Guangquan Chen, Facheng Liu, Miaomiao School of Transportation Science and Engineering Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Traffic Management School People’s Public Security University of China Beijing100038 China Beijing100088 China

The workload of drivers increases rapidly after the critical events and the period of time for workload to stabilize is worthy to investigate due to the close relationship between the workload and driving performance of drivers. It is yet unclear if the stabilization time of drivers varies across the different automation levels. In previous studies, a number of measurements have been utilized in evaluating the workload of drivers while most of the existing methods have limitations in studying the stabilization time. Therefore, this study introduces a new method for quantifying the cognitive workload of drivers with a measurement frequency of 2 seconds based on the performance of drivers to a newly designed calculating NDRT. A driving simulator experiment containing three automated driving levels and two critical events was conducted for the verification and application of the quantification method. data were collected from 52 participants. The workload before and after critical events was analyzed and the results of quantified workload, the measurement acquired through the quantification method, showed a high similarity with heart rate and pupil diameter, which supported that the measurement could well reflect the workload of drivers with and without critical events. Moreover, the quantification method was applied to analyzing stabilization time after the critical situations and it was observed that quantified workload was a more appropriate measurement in determining the stabilization time of workload compared to heart rate and pupil diameter. In addition, the analysis results based on the workload quantification method showed that the effect of automation levels on the stabilization time of drivers after the critical events occurred was not significant. © 2022, The Authors. All rights reserved.

关键词： Automobile drivers

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu 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) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory data mining Cybernetics Convergence

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基于污点和概率的逃逸恶意软件多路径探索

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Security and Safety 2023年第3期2卷 83-106页

作者：徐钫洲张网羌卫中金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution ***,malware can employ evasive techniques to detect the analysis environment and alter its behavior *** known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown *** this paper,we present Antitoxin,a prototype for automatically exploring evasive *** utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive *** probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain ***,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path *** is achieved through forced execution,which forcefully sets the outcomes of branches on selected ***,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration ***,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual *** experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic *** probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and signific

关键词： Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection

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