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Utilizing Sub-Topic Units for Patent Prior-Art Search

Chinese Journal of Electronics 2025年第3期23卷 480-483页

作者： Dong Zhou Jianxun Liu Sanrong Zhang Key Laboratory of Knowledge Processing and Networked Manufacturing & School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China

One of the defining challenges in patent prior-art search is the problem of representing a long, technical document as a query. Previously work on this problem has concentrated on single query representations of the patent application. In the following paper, we describe an approach which uses multiple query representations generated from semantically coherent passages extracted from patent documents. We validate our technique in an experiment using the CLEF-IP 2011 patent search collection. Our system achieves statistically significant improvements over various state-of-art query generation techniques.

关键词： Patents Search problems Information filters

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Evaluating roving patrol effectiveness by GPS trajectory

Evaluating roving patrol effectiveness by GPS trajectory

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9th IEEE Int. Conf. on Dependable, Autonomic and Secure Comput., DASC 2011, incl. 9th Int. Conf. on Pervasive Intelligence and Computing, PICom 2011, 9th Int. Symp. on Embedded Computing, EmbeddedCom 2011, 1st Int. Conf. on Cloud and Green Comput.CGC

作者： Li, Huanhuan Liu, Jianxun Liu, Yizhi Jin, Long Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9780769546124

Roving patrol plays a very important role in strengthening safety within campuses or communities through their patrol around the target. In fact, there maybe exist some patrolmen who do not work efficiently. Therefore, it is useful to quantitatively evaluate their roving effectiveness. The widespread use of global positioning system (GPS) and GPS data logger have generated huge amount of trajectory data. It is a good way to evaluate the effectiveness. In this paper, we present the roving patrol effectiveness evaluation (RPEE) model by GPS Trajectory. Firstly, GPS data is pre-processed by redundancy reducing, trajectory segmentation and abnormality filtering. And then, evaluation index system is established based on the indicators extracted from trajectories by using statistical analysis. Finally, RPEE is conducted by using fuzzy comprehensive evaluation method. In this procedure, the weight distribution for indicators is determined by analytic hierarchy process. We evaluate the model by using the GPS data collected at 11 distinct patrol areas over a period of six months in the real world. The experimental results show that: indicators, such as the total effective patrol time, the phenomenon of staying, and the roving patrols at each checkpoint, etc., can be obtained objectively for RPEE. © 2011 IEEE.

关键词： Analytic hierarchy process

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Utilizing Sub-topic Units for Patent Prior-Art Search

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Chinese Journal of Electronics 2014年第3期23卷 480-483页

作者： ZHOU Dong LIU Jianxun ZHANG Sanrong Key Laboratory of Knowledge Processing and Networked Manufacturing & School of Computer Science and Engineering Hunan University of Science and Technology

关键词： Patent information retrieval Sub-topic units Multiple query representations Collaborative filtering Patent ranking

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AT-NCF: An Attention-based Time-aware Neural Collaborative Filtering Approach for Personalized Recommendation 35

AT-NCF: An Attention-based Time-aware Neural Collaborative F...

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35th International Conference on Software Engineering and knowledge Engineering, SEKE 2023

作者： Peng, Yimin Hu, Rong Wen, Yiping Key Laboratory of Knowledge Processing &Networked Manufacturing Hunan University of Sci & Tec Xiangtan China

With the explosive growth of data, how to apply these data with time characteristics to recommendation is particularly important. In order to do that, an attention-based time-aware neural collaborative filtering approach named ATNCF is proposed in this paper. Concretely, short-term preferences of users are learned by a time perception network, and long-term preferences part are modeled by a historical interaction matrix. Then, the short-term preferences are concatenated with long-term preferences as the dynamic user preference vector. After that, high-order user-item feature interactions are learned by a general neural collaborative filtering framework which includes two types of DL models, Deep Matrix Factorization (DMF) and Multiple-Layer Perception (MLP). Finally, the predicted scores are output from the final layer of AT-NCF. Experimental results on the real-world e-commerce dataset verify the effectiveness of the proposed method. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Collaborative filtering

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An efficient clustering approach using ant colony algorithm in mutidimensional search space

An efficient clustering approach using ant colony algorithm ...

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2011 8th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Jiang, Lei Ding, Lixin Peng, Yang Zhao, Chenhong State Key Lab of Software Engineering Wuhan University Wuhan 430072 China Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan 411201 China

ISBN: (纸本)9781612841816

Clustering is an important data analysis technique and it widely used in many field such as data mining, machine learning and pattern recognition. Ant colony optimization clustering is one of the popular partition algorithm. However, in mutidimensional search space, its results is usually ordinary as the disturbing of redundant information. To address the problem, this paper presents MD-ACO clustering algorithm which improves the ant structure to implement attribute reduction. Four real data sets from UCI machine learning repository are used to evaluate MD-ACO with ACO. The results show that MD-ACO is more competitive. © 2011 IEEE.

关键词： Ant colony optimization

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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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Homogeneous spiking neural P systems without delays

Homogeneous spiking neural P systems without delays

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2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013

作者： Peng, Xian Wu Liu, Jian Xun Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan 411201 China

ISBN: (纸本)9783037858202

Spiking neural P systems are a new class of bio-inspired computing devices incorporating the ideas of spiking neural networks into P systems. Homogeneous spiking neural P systems are a variant of spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking neural P systems without delays is investigated and proved. This paper provides an answer to an open problem formulated by Zeng Xiangxiang, etal. © (2013) Trans Tech Publications, Switzerland.

关键词： Neural networks

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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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Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

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Computers, Materials & Continua 2021年第11期69卷 2261-2276页

作者： Yuzhen Liu Xiaoliang Wang Xinghui She Ming Yi Yuelong Li Frank Jiang Hunan University of Science and Technology Xiangtan411201China School of Info Technology Deakin UniversityGeelong3215Australia Key Laboratory of Knowledge Processing and Networked Manufacturing College of Hunan Province411201China

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes,and help people live well in mosquito-infested *** this study,we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical *** our method,decision-making is controlled by a deep learning model,and the proposed method uses infrared sensors and an array of pressure sensors to collect *** the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model,determining automatically the intention of the user to open or close the mosquito *** used optical flow to extract pressure map features,and they were fed to a 3-dimensional convolutional neural network(3D-CNN)classification model *** achieved the expected results using a nested cross-validation method to evaluate our *** learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of *** research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

关键词： Internet of things smart home ZigBee protocol internet of medical things deep learning

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Dual-Level Attention Based on Heterogeneous Graph Convolution Network for Aspect-Based Sentiment Classification

Dual-Level Attention Based on Heterogeneous Graph Convolutio...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Peng Yuan Lei Jiang Jianxun Liu Dong Zhou Pei Li Yang Gao Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology China Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9781728165479

ISBN: (纸本)9781728165486

We introduce a flexible HIN (Heterogeneous Information Network) framework to model user-generated comments. It can integrate various types of additional information and capture the relationship between them to reduce the semantic sparsity of a small amount of labeled data. It can also take advantage of the hidden network structure information by spreading the information together with the graph. Then we propose to use a dual-level attention-based heterogeneous convolutional graph network to understand the importance of different adjacent nodes and of different types of nodes to the current node. By doing this, we can mitigate the shortcomings that most existing algorithms ignore, i.e. the network structure information between the words in the sentence and the sentence itself. The experimental results on the SemEval dataset prove the validity and reliability of our model.

关键词： Sentiment analysis Semantics Recurrent neural networks Training Support vector machines Analytical models Task analysis

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