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Artificial Intelligence and Blockchain Technology (AIBT), International Conference on

作者： Hankang Sheng Zhangbing Li Jianxun Liu Xiao Zhang School of Computer Science and Engineering Hunan University of Science & Technology Xiang Tan China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology Xiang Tan China

ISBN: (纸本)9781665432689

In recent years, the rapid growth of the number of web services on the Internet has made it difficult to discover services from a large number of web services and classify them efficiently, which poses a huge challenge for users to provide appropriate web services. The existing web service classification technology has problems such as sparse description text, insufficient consideration of attribute information and structural relationships, so the accuracy of web service classification can be further improved. In response to the above problems, this paper first proposes an information distillation model based on reinforcement learning. Reinforcement learning is introduced to delete redundant information and retain valid information. Then, the classification network classifies the distilled information and determines the reward based on the prediction result. Then, the reward is fed to reinforcement learning for gradient update and the classification network is updated inversely according to the classification result. It can effectively alleviate the inherent noise caused by the introduction of the attention mechanism. It can also learn classification tasks by identifying important words or task-related structures without displaying structural annotations (ie, artificially building models). Through comparative experiments on the real data set of Programmable Web, the results show that the proposed model achieves better results on the benchmark data set than other methods.

关键词： Representation learning Web services Buildings Reinforcement learning Benchmark testing Data models Blockchains

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Web service discovery based on information gain theory and BiLSTM with attention mechanism 14th

Web service discovery based on information gain theory and B...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Zhang, Xiangping Liu, Jianxun Cao, Buqing Xiao, Qiaoxiang Wen, Yiping Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology XiangtanHunan China

ISBN: (纸本)9783030129804

Web service discovery is an important problem in service-oriented computing with the increasing number of Web services. Clustering or classifying Web services according to their functionalities has been proved to be an effective way to Web service discovery. Recently, semantic-based Web services clustering exploits topic model to extract latent topic features of Web services description document to improve the accuracy of service clustering and discovery. However, most of them don’t consider deep and fine-grained level information of description document, such as the weight (importance) for each word or the word order. While the deep and fine-grained level information can be fully used to argument service clustering and discovery. To address this problem, we proposed a Web service discovery approach based on information gain theory and BiLSTM with attention mechanism. This method firstly obtains the effective words through information gain theory and then adds them to an attention-based BiLSTM neural network for Web service clustering. The comparative experiments are performed on ProgrammableWeb dataset, and the results show that a significant improvement is achieved for our proposed method, compared with baseline methods. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Web services

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MDT: A multi-description topic based clustering approach for composite-service discovery 21

MDT: A multi-description topic based clustering approach for...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Hu, Rong Chen, Junjie Liu, Jianxun Nian, Qifeng Key Laboratory of Knowledge Processing AND Networked Manufacturing Hunan University of Sci AND Tec Xiangtan Hunan China

ISBN: (纸本)9781728120584

With the rapid development of Service-Oriented Architecture that enables rapid composition and integration of large-scale distributed systems, the number of web accessible services and their compositions is increasing rapidly, which makes it difficult to efficiently discover desired composite-services from a sea of candidates. Recently, topic-based clustering techniques have emerged as a promising way to improve the efficiency and accuracy of service discovery. However, it is a challenge to extract topics from short description texts of composite-services, for short texts do not provide sufficient word occurrences. In view of this problem, a multi-description topic based clustering approach for composite-service discovery is proposed. In this approach, descriptions of the component services of each composite-service are combined for topic extraction and modeling. Then, composite-services are clustered based on their topic similarities. Finally, composite-services are discovered from the cluster that is most topic-similar to the service queries. The effectiveness of the proposed approach was verified by some experiments on the real-world data at the end of this paper. © 2019 IEEE.

关键词： Information services

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Web Services Classification with Topical Attention Based Bi-LSTM 15th

Web Services Classification with Topical Attention Based Bi-...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Cao, Yingcheng Liu, Jianxun Cao, Buqing Shi, Min Wen, Yiping Peng, Zhenlian Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing HNUST Xiangtan China

ISBN: (纸本)9783030301453

With the rapid growth of the number of Web services on the Internet, how to classify web services correctly and efficiently become more and more important in the development and application of Web services. Existing function-based service clustering techniques have some problems, such as the sparse document semantics, unconsidered word order and the context information, so the accuracy of service classification needs to be further improved. To address this problem, this paper exploits the attention mechanism to combine the local implicit state vector of Bi-LSTM and the global LDA topic vector, and proposes a method of Web services classification with topical attention based Bi-LSTM. Specifically, it uses Bi-LSTM to automatically learn the feature representation of Web service. Then, it utilizes the offline training to obtain the topic vector of Web service document and performs the topic attention strengthening processing for Web service feature representation, and obtains the importance or weight of the different words in Web service document. Finally, the enhanced Web service feature representation is used as the input of the softmax neural network layer to perform the classification prediction of Web service. The experimental results validate the efficiency and effectiveness of the proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Web services

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An Entropy-Based Model for Recommendation of Taxis’Cruising Route

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Journal on Artificial Intelligence 2020年第3期2卷 137-148页

作者： Yizhi Liu Xuesong Wang Jianxun Liu Zhuhua Liao Yijiang Zhao Jianjun Wang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201China

Cruising route recommendation based on trajectory mining can improve taxi-drivers'income and reduce energy ***,existing methods mostly recommend pick-up points for taxis ***,their performance is not good enough since there lacks a good evaluation model for the pick-up ***,we propose an entropy-based model for recommendation of taxis'cruising ***,we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal ***,the information entropy of spatial-temporal features is integrated in the evaluation *** it is applied for getting the next pick-up points and further recommending a series of successive *** points are constructed a cruising route for *** results show that our method is able to obviously improve the recommendation accuracy of pick-up points,and help taxi-drivers make profitable benefits more than before.

关键词： Trajectory data mining location-based services(LBS) optimal route recommendation pick-up point recommendation information entropy

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A Hybrid Data-Driven Approach for Predicting Remaining Useful Life of Industrial Equipment

A Hybrid Data-Driven Approach for Predicting Remaining Usefu...

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2020国际计算机前沿大会

作者： Zheng Tan Yiping Wen TianCai Li School of Computer Science and Engineering Hunan University of Science and Technology Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology

Guaranteeing the safety of equipment is extremely important in industry. To improve reliability and availability of equipment, various methods for prognostics and health management(PHM) have been proposed. Predicting remaining useful life(RUL) of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues. With the rapid development of industrial equipment and sensing technology, an increasing amount of data on the health level of equipment can be obtained for RUL prediction. This paper proposes a hybrid data-driven approach based on stacked denoising autoencode(SDAE) and similarity theory for estimating remaining useful life of industrial equipment, which is named RULESS. Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction. The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation(C-MAPSS).

关键词： Remainingusefullife Prediction Industrialequipment StackedDenoisingAutoEncoder Similaritytheory RULESS

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Edge Intelligence Empowered Distribution Path Planning with Internet of Vehicles

Edge Intelligence Empowered Distribution Path Planning with ...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tiancai Li Yiping Wen Zheng Tan Hong Chen School of Computer Science and Engineering Hunan University of Science and Technology China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology

ISBN: (纸本)9781728176505

With rapid urbanization and increasing traffic, distribution path planning has become an important challenging issue. Traditional vehicle distribution route planning mostly relies on static road information or historical traffic data for route planning. However, route planning based on such information may be unreasonable, because many uncertainty factors involve in the vehicle distribution process. Edge intelligence can help to handle such uncertainty factors, as it can offer smart services in close proximity to the Internet of Vehicles (IoV) environment with low latency and less cost. Therefore, in this paper, a model for dynamic distribution path planning with IoV and edge intelligence is constructed, and a corresponding planning strategy is proposed. The result of simulation experiments shows its effectiveness.

关键词： Uncertainty Intelligent vehicles Computational modeling Roads High performance computing Path planning Planning

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An OSM Contributors Classification Method Based on WPCA and GMM

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Journal of Physics: Conference Series 2021年第1期2025卷

作者： Yijiang Zhao Xingcai Wei Yizhi Liu Zhuhua Liao Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Hunan University of Science and Technology Xiangtan 411201 China

Contributors have a significant impact on data quality of OpenStreetMap (OSM) because most of them are the non-professional, so clustering analysis of contributors based on different experiences has practical significance. Firstly, this paper obtained 31 behavioural characteristics of contributors from OSM historical data. Then, a weighted principal component analysis (WPCA) method was used to reduce the dimensions of the contributors' behaviour in the selected region. By using an unsupervised prototype-based Gaussian mixture model (GMM) clustering algorithm, contributors with similar contribution attributes in the London area were clustered into four groups. Finally, the characteristics of four different types of contributors are analysed, and two types of experienced and professional contributors are found, who contribute a large amount of high-quality data.

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A Campus Carpooling System Based on GPS Trajectories 12th

A Campus Carpooling System Based on GPS Trajectories

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12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Wang, Xuesong Liu, Yizhi Jiang, Zhengtao Peng, You Yin, Tianhao Liao, Zhuhua Zhao, Jingqiang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201 China Network Information Center Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030248994

College students are special because they relatively have tighter in economy but have greater consistency in leisure time. They prefer to go out together with schoolfellows due to higher trusts and closeness. Moreover, the electronic map is difficult to be updated. Campus-roads recently are updated rapidly. And many alleys in campuses are not shown in the electronic map. Therefore, we devise and implement a campus carpooling system based on GPS trajectories. It includes three parts. Firstly, the campus road network is extracted based on GPS trajectories. Next, the shortest sharing path in the campus is computed in terms of the campus road network. Then, passengers are matched automatically by the carpooling matching algorithm (CMA) in our system. Experiments show that our system is able to provide a safer and more comfortable carpooling experience for college students. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

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A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Scheduling algorithms

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