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SSRN 2023年

作者： Lu, Ziyi Yu, Na Wang, Xuehe School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the UAV’s limited energy storage makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework based on crowdsourcing, in which the UAVs can call on ground vehicles to provide hitch services for energy saving and extending their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for crowdsourced ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over the traffic network is studied on the basis of the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only. © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection 34

Ethereum Smart Contract Representation Learning for Robust B...

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34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Research on Object-oriented Exploration Algorithm

Research on Object-oriented Exploration Algorithm

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Shen, Xiajiong Zhang, Peng Yang, Jiyong Chen, Wan Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781450387828

In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaningful while it is similar to attribute exploration. It can explore the implication relationship between objects. But for the existing work, almost all are attribute exploration, object-oriented exploration is rare. Research on object-oriented exploration has great practical value, such as object classification, retrieval and excavation. In order to address this problem, this article takes the object as the main research point in formal concept analysis based on related knowledge of traditional attribute exploration, and proposes theories of object-oriented exploration. According to these related theories and the framework of attribute exploration algorithm, an object-oriented exploration algorithm is proposed in this paper. The algorithm can be used to explore and understand the relationship between objects, which facilitates to understand better the relationship between objects and attributes. © 2020 ACM.

关键词： Formal concept analysis

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Location Rating Based on Graph Neural Network

Location Rating Based on Graph Neural Network

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Zheng, Fengbin Ding, Li Dang, Lanxue Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781450387828

With the rapid development of social networks, people are increasingly interested in sharing their location. Location recommendation has become an important personalization service for location-based social networks (LBSN). Location rating is one of the important tools. However, location-based social networks contain multidimensional network structures and node information. Existing approaches mostly focus on network structures that utilize one of these dimensions, which makes it difficult to efficiently aggregate information from multiple dimensions simultaneously. To overcome these difficulties, one of the recent approaches is the social recommendation based on graph neural networks (GNNs). Based on this, this paper proposes a graph neural network framework for location rating. Graph neural networks are highly inductive and can efficiently aggregate network structure and node information. In particular, the method not only aggregates homogeneous social networks composed of user and heterogeneous bipartite graphs composed of user-location, but also constructs location networks using location sequences to propose an aggregation model for location networks. Experiments are conducted on two datasets, and the results show that the method improves on the root mean square error (RMSE) and mean absolute error (MAE) metrics. © 2020 ACM.

关键词： Location

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Optimal control and cost-effective analysis of an agestructured emerging infectious disease model

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Infectious Disease Modelling 2022年第1期7卷 149-169页

作者： Peiqi Jia Junyuan Yang Xuezhi Li Complex Systems Research Center Shanxi UniversityTaiyuan030006ShanxiPR China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityTaiyuan030006PR China School of Mathematics and Information Science Henan Normal UniversityXinxiang453007HenanPR China

Emerging infectious diseases are one of the global public health problems which may lead to widespread epidemics and potentially life-threatening *** vaccination and physical distancing interventions are two elementary methods for preventing infectious diseases *** this paper,we construct a continuous age-structured model for investigating the transmission dynamics of an emerging infection disease during a short *** derive the basic regeneration number R 0,the spectral radius of the next generation operator K,which determines the disease outbreak or ***,we propose an optimal control problem to take account for the cost-effectiveness of social distancing intervention and *** rigorously obtain sufficient conditions for a L1 control *** simulations show that coupling integrated vaccination and physical distancing intervention could effectively eliminate the infection,and such control strategy is more sensitive for people aged 10e39 and over 60.

关键词： Age-structured model Admissible control Ekerland variational principle The basic reproduction number

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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data Preprocessing Technology in Network Traffic Anomaly Detection

Data Preprocessing Technology in Network Traffic Anomaly Det...

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作者： Duan, Xueyuan Fu, Yu Wang, Kun Department of Information Security Naval University of Engineering Wuhan430033 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang464000 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China

Computer network plays a very important role in today’s world. With the development of network technology and the increasing popularity of network topology, network management is facing huge challenges. Among them, finding network traffic anomalies is one of the most important tasks in network monitoring, and is an important technology in network intrusion, security monitoring, and performance maintenance. This paper aims to study data preprocessing techniques in network traffic anomaly detection. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized. Experiments show that the data preprocessing technology constructed in this paper has about 20% more data filtering than traditional data filtering, which shortens the amount of detection data. The threshold is around 0.6, and data preprocessing can maximize the reduction of data without destroying the integrity of the attacked data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice

Research on Visualization Algorithm of Association Rules Bas...

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Shen, Xiajiong Bao, Lijie Zhang, Lei Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display. © 2020 ACM.

关键词： Visualization

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