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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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Optimizing biomedical ontology alignment in lexical vector space

Optimizing biomedical ontology alignment in lexical vector s...

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作者： Xue, Xingsi Wu, Xiaojing Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian Minhou China College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian Minhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian Minhou China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian Minhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian Minhou China

Biomedical ontology matching dedicates to find two heterogeneous ontologies' alignment and address their heterogeneity problem. Typically, a biomedical ontology has various biomedical concepts that are described with various labels and datatype property names, which forms a lexical space where each label or datatype property represents one dimension. Therefore, it is an effective way to present two biomedical concepts in a vector space, and use the cosine distance to measure their similarity. In this work, we present two biomedical concepts in a lexical vector space which is constructed with their inner and context concepts' lexical information, and then utilize two vector's cosine distance to measure similarity value. Then, we propose a compact Evolutionary Algorithm (cEA) to find the concept correspondences. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s testing cases, and the expeirmental results with Vector space Based Ontology Matcher (VBOM), Genetic Algorithm based Ontology Matcher (GAOM) and OAEI's participants show the effectiveness of our proposal. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Vector spaces

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Comparing Recommendation Algorithms in Session-based E-commerce Sites

Comparing Recommendation Algorithms in Session-based E-comme...

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International Conference on intelligent Computing, Automation and Systems (ICICAS)

作者： Mingtian Peng Jiahe Zhang Shilin Wen Chi Harold Liu TravelSky Technology Limited Beijing Engineering Research Center of Civil Aviation Big Data and Key Laboratory of Intelligent Passenger Service of Civil Aviation-CAAC Beijing China College of Computer Science Beijing Institute of Technology Beijing China

Recommender systems have become widely used in various website applications. With the integration of deep learning and recommender systems, the classic Session-based Recommender System (SRS) appears, which can obtain implicit feedback from explicit interactions. Some scholars have pro-posed many effective recommendation algorithms to provide better recommendation service in SRS. In order to compare the performance of these session-based recommendation algorithms, we consider a simple E-commerce SRS scenario and choose four representative session-based recommendation algorithms in this paper. Then we do some evaluation experiments. The experimental results show that the combination of local preferences and global preferences will improve the recommendation performance significantly, and for GNNs and RNN s in session recommendation based on deep learning, we also conclude that the prediction effect of GNN s is slightly superior to RNN s on long sessions.

关键词： Deep learning Automation Prediction algorithms Recommender systems

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Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

arXiv

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

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An extragradient algorithm for fixed point problems and pseudomonotone equilibrium problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 89-96页

作者： Yao, Zhangsong Liou, Yeong-Cheng Zhu, Li-Jun School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China Department of Healthcare Administration and Medical Informatics Research Center of Nonlinear Analysis and Optimization Kaohsiung Medical University Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute Advanced Intelligent Perception and Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

Iterative methods for solving fixed point problems and pseudomonotone equilibrium problems have been considered. An extragradient algorithm has been presnted for finding a common element of the fixed points of pseudocontractive operators and the solutions of pseudomonotone equilibrium problems in Hilbert spaces. Weak convergence result of the suggested algorithm is proved. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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Experimental Research on Deep Reinforcement Learning in Autonomous navigation of Mobile robot 14

Experimental Research on Deep Reinforcement Learning in Auto...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yue, Pengyu Xin, Jing Zhao, Huan Liu, Ding Shan, Mao Zhang, Jian Xian Univ Technol Xian Peoples R China CETC 20 Shaanxi Key Lab Integrated & Intelligent Nav Xian Peoples R China Univ Sydney Australian Ctr Field Robot Sydney NSW Australia Univ Technol Sydney Global Big Data Technol Ctr Sydney NSW Australia

ISBN: (纸本)9781538694909

The paper is concerned with the autonomous navigation of mobile robot from the current position to the desired position only using the current visual observation, without the environment map built beforehand. Under the framework of deep reinforcement learning, the Deep Q Network (DON) is used to achieve the mapping from the original image to the optimal action of the mobile robot. Reinforcement learning requires a large number of training examples, which is difficult to directly be applied in a real robot navigation scenario. To solve the problem, the DQN is firstly trained in the Gazebo simulation environment, followed by the application of the well-trained DON in the real mobile robot navigation scenario. Both simulation and real-world experiments have been conducted to validate the proposed approach. The experimental results of mobile robot autonomous navigation in the Gazebo simulation environment show that the trained DQN can approximate the state action value function of the mobile robot and perform accurate mapping from the current original image to the optimal action of the mobile robot. The experimental results in real indoor scenes demonstrate that the DQN trained in the simulated environment can work in the real indoor environment, and the mobile robot can also avoid obstacles and reach the target location even with dynamics and the presence of interference in the environment. It is therefore an effective and environmentally adaptable autonomous navigation method for mobile robots in an unknown environment.

关键词： Deep reinforcement learning DQN mobile robot nature scene autonomous navigation

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A branch-and-price algorithm for the trucking problem using trailer-swapping operation mode

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Transportation Research Part C: Emerging Technologies 2025年 178卷

作者： Wenxiang Peng Xiangsheng Chen Zhaojie Xue Jintao You College of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Key Laboratory of Coastal Urban Resilient Infrastructures (Shenzhen University) Ministry of Education Shenzhen 518060 China State Key Laboratory of Subtropical Building and Urban Science Shenzhen 518060 China School of Advanced Engineering Great Bay University Dongguan 523000 China Great Bay Institute for Advanced Study Great Bay University Dongguan 523000 China Dongguan Key Laboratory of Intelligent Equipment and Smart Industry School of Advanced Engineering Great Bay University Dongguan 523000 China Shenzhen Research Institute of Big Data Shenzhen 518172 China

This study investigates the trucking problem using the trailer-swapping operation mode (TP-TSOM), where trucks swap trailers at network nodes during transit and then continue to pull the swapped trailer to its destination. Traditionally, trucking involves transport tasks distributed across the network, completed by trucks pulling trailers directly from origins to destinations. The trailer-swapping operation mode (TSOM), which involves collaboration among trucks, can enhance trucking efficiency. Accordingly, a mixed-integer linear programming model for TP-TSOM is developed to determine the optimal truck trailer-swapping plan, and an integer linear programming model for the two-truck swapping case is also constructed. To solve the TP-TSOM efficiently, a branch-and-price algorithm utilizing a trailer-swapping group-based set partitioning model is designed. The pricing sub-problem is formulated as multiple elementary shortest cycle problems with resource constraints (ESCPRC) based on different sub-graphs. By iteratively fixing virtual terminals, a new representation of the ESCPRC is constructed, enabling the ESCPRC to be solved using the labeling algorithm. To accelerate the solution process, two tailored refinement strategies are proposed, and a heuristic pricing algorithm based on shortest path faster algorithm is applied. Extensive numerical experiments are conducted to validate performance. The computational results show that the proposed exact algorithm can solve instances with up to 200 tasks and that TSOM significantly reduces transportation costs, travel time, and empty load rates.

关键词： Travel time

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HRDS: A High-Dimensional Lightweight keypoint Detection Network Enhancing HRNet with Dim-Channel and Space Gate Attention Using Kolmogorov-Arnold Networks

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Electronics 2025年第10期14卷 2038-2038页

作者： Xinran Wang Guoliang Li Feng Liu Key Laboratory of Intelligent Huazhong Agricultural University Wuhan 430070 China Technology in Animal Husbandry Ministry of Agriculture and Rural Affairs Wuhan 430070 China Engineering Research Center of Smart Agricultural Technology Ministry of Education Wuhan 430070 China College of Informatics Huazhong Agricultural University Wuhan 430070 China Hubei Province Research Center of Engineering Technology of Agricultural Big Data Wuhan 430070 China

Animal keypoint detection holds significant applications in fields such as biological behavior research and animal health monitoring. Although related research has reached a relatively mature stage of human keypoint detection, it still faces numerous challenges in the realm of animal keypoint detection. Firstly, there is a scarcity of keypoint detection datasets related to animals in public datasets. Secondly, existing solutions have adopted large-scale deep learning models to achieve higher accuracy, but these models are costly and difficult to widely promote within the industry. On the other hand, small-scale, low-cost detection models that are used to reduce costs suffer from insufficient accuracy and cannot meet the needs of industrial production. Therefore, designing and implementing a lightweight and high-accuracy animal keypoint detection model to meet industry needs has significant theoretical and practical importance. Addressing the aforementioned issues, this thesis proposes a lightweight animal keypoint detection method, HRDS, which maintains high accuracy while significantly reducing model complexity. Firstly, by removing the fourth stage from the HRNet architecture, the number of parameters and the computational complexity of the model are successfully reduced. Secondly, to enhance the model’s performance and robustness, a new attention mechanism module, DS, is designed. This module effectively counterbalances the loss of accuracy due to the significant reduction in the parameter count and helps to strengthen the model’s keypoint detection capability in complex scenarios. Experiments were performed on the AP-10K dataset, and the results indicated that the HRDS method achieves an accuracy rate of 70.34%, with a 73.05% reduction in the number of parameters compared to HRNet and only a 2.64% decrease in accuracy, maintaining high precision. The inference time of HRDS reaches 26.58 ms, with an inference speed of 37.62 FPS, and its inference time is only 87.

关键词： keypoint detection real-time detection lightweight model HRNet attention KAN

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A Social-Aware Mode Selection Algorithm for D2D Communications

A Social-Aware Mode Selection Algorithm for D2D Communicatio...

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International Conference on Communications, Signal Processing, and Systems, CSPS 2018

作者： Lu, Wei-feng Lin, Wei-jun Chen, Si-guang Yang, Li-jun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China College of IoT Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

ISBN: (纸本)9789811362637

Aiming at mode selection in D2D communication, a mode selection strategy based on social-aware was proposed. In addition to physical connection status, users’ social relationships also served as an observation element. Through relationship between users, the closeness of user relationship can be judged, and then through users’ reference, the similarity between users can be obtained. In this way, by combining physical connection with social relationships, users’ aggregate utility was acquired. According to the physical connection quality and the social relationship strength, taking maximizing users’ aggregate utility as the goal, a random algorithm was designed to match users. Simulation results show that mode selection based on social perception can effectively improve users’ aggregate utility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Aggregates

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Improving the Efficiency of Blockchain Applications with Smart Contract based Cyber-insurance

Improving the Efficiency of Blockchain Applications with Sma...

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IEEE International Conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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