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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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CUTE: A Collaborative Fusion Representation-Based Fine-Tuning and Retrieval Framework for Code Search 19th

CUTE: A Collaborative Fusion Representation-Based Fine-Tunin...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Song, Qihong Liu, Jianxun Hu, Haize 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 Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search aims at searching semantically related code snippets from the large-scale database based on a given natural descriptive query. Fine-tuning pre-trained models for code search tasks has recently emerged as a new trend. However, most studies fine-tune models merely using metric learning, overlooking the beneficial effect of the collaborative relationship between code and query. In this paper, we introduce an effective fine-tuning and retrieval framework called CUTE. In the fine-tuning component, we propose a Collaborative Fusion Representation (CFR) consisting of three stages: pre-representation, collaborative representation, and residual fusion. CFR enhances the representation of code and query, considering token-level collaborative features between code and query. Furthermore, we apply augmentation techniques to generate vector-level hard negative samples for training, which further improves the ability of the pre-trained model to distinguish and represent features during fine-tuning. In the retrieval component, we introduce a two-stage retrieval architecture that includes pre-retrieval and refined ranking, significantly reducing time and computational resource consumption. We evaluate CUTE with three advanced pre-trained models on CodeSearchNet consisting of six programming languages. Extensive experiments demonstrate the fine-tuning effectiveness and retrieval efficiency of CUTE. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Query processing

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Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

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Computer Systems Science & Engineering 2023年第6期45卷 2505-2519页

作者： Yizhi Liu Rutian Qing Yijiang Zhao Xuesong Wang Zhuhua Liao Qinghua Li Buqing Cao School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Northwestern Institute on Complex Systems Northwestern UniversityEvanstonIL60208USA Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201China

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the *** how to alleviate sparsity and further enhance accuracy is still *** at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal ***-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding ***,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from *** a novel algorithm named STC_DeepFM is presented for offline pick-up area ***,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and *** experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O *** results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

关键词： Location-based service(LBS) trajectory data mining offline-toonline(O2O)recommendation deep factorization machine(DeepFM) spatiotemporal context

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Utilizing Sub-Topic Units for Patent Prior-Art Search

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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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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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A Tag Recommendation Method for OpenStreetMap Based on FP-Growth and Improved Markov Process 7th

A Tag Recommendation Method for OpenStreetMap Based on FP-Gr...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zhao, Yijiang Guo, Xicheng Liu, Yizhi Liao, Zhuhua Liu, Min Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030786175

Volunteer geographic information (VGI), as a new geographic information source, has gradually become an important complement for authoritative data sources. Yet, due to the non-professionalism and spontaneity of most volunteers, and the lack of effective contribution constraints, the issues of attribute quality of OpenStreetMap (OSM) objects, such as semantic inconsistency and incompleteness, have received many attentions. However, there are few methods for effectively improving OSM’s semantic quality. In this paper, we proposed a tag recommendation method based on the combination of FP-Growth and the Markov process. The aim of our algorithm is to improve the quality of OSM objects by recommending some tags when volunteers contribute to the platform. The results show that the method based on FP-Growth and Improved Markov Process can effectively recommend tag-keys for different feature classes. © 2021, Springer Nature Switzerland AG.

关键词： Markov processes

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An OSM contributors classification method based on WPCA and GMM 3

An OSM contributors classification method based on WPCA and ...

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2021 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Zhao, Yijiang Wei, Xingcai Liu, Yizhi Liao, Zhuhua Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Hunan University of Science and Technology Xiangtan411201 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. © Journal of Physics: Conference Series 2021.

关键词： Clustering algorithms

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Combining Turning Point Detection with Grid Transformation for Road Intersection Detection 7th

Combining Turning Point Detection with Grid Transformation f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Qing, Rutian Liu, Yizhi Zhao, Yijiang Guo, Zhihou Liao, Zhuhua Liu, Min Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030786175

Road intersection is one of the crucial elements in a road network. How to accurately detect road intersection is a vital process in road network construction. The traditional approach finds the turning point in trajectory by computing the trajectory point’s turning angle and uses a clustering algorithm to cluster the turning point to yield the cluster point representing road intersection. However, the approach will detect some fake road intersection points. We propose an approach combining turning point detection with grid transformation for road intersection detection to address this issue. The process of grid transformation can filter the point in low-density area. So, the fake intersection point can be filtered by the process as far as possible. We use a real-world trajectory data set to conduct the experiment and receive a good result. The precision of our approach’s experimental result is 14% more than the traditional approach’s result, and the recall and the F-score are also higher than the traditional approach. © 2021, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Using Feature Interaction among GPS Data for Road Intersection Detection 2

Using Feature Interaction among GPS Data for Road Intersecti...

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2nd International Workshop on Human-Centric Multimedia Analysis, HUMA 2021, co-located with ACM Multimedia, MM 2021

作者： Qing, Rutian Liu, Yizhi Zhao, Yijiang Liao, Zhuhua Liu, YuXuan Key Laboratory of Knowledge Processing and Networked Manufacturing College of Hunan Province School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9781450386715

Road intersection plays a vital role in road network construction, automatic drive, and intelligent transportation systems. Most methods detect road intersections only using geometrical features without spatiooral features, leading to insufficient precision. In addition, the existing methods do not consider the impact of feature interaction. For the issue, this paper proposes a novel way to detect road intersections based on GPS trajectory by extracting spatiooral features and using the interaction of the features to enhance the precision of the detection. The proposed method is implemented on DIDI's GPS data in Chengdu. The performance shows that the proposed method can effectively improve the precision of road intersection detection compared with the existing method, which is beneficial for road network construction and makes up for the deficiency of existing methods. © 2021 ACM.

关键词： Clustering algorithms

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DeepFM-Based Taxi Pick-Up Area Recommendation 25th

DeepFM-Based Taxi Pick-Up Area Recommendation

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Wang, Xuesong Liu, Yizhi Liao, Zhuhua Zhao, Yijiang 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

ISBN: (纸本)9783030688202

Recommending accurately pick-up area with sparse GPS data is valuable and still challenging to increase taxi drivers’ profits and reduce fuel consumption. In recent years, the recommendation approach based on matrix factorization has been proposed to deal with sparsity. However, it is not accurate enough due to the regardless of the interaction effect between features. Therefore, this paper proposes DeepFM-based taxi pick-up area recommendation. Firstly, the research area is divided into grid area of equal size, the pick-up point information is extracted from the original GPS trajectory data, the pick-up point information and POI data are mapped to the grid area, the corresponding grid attributes are calculated and the grid feature matrix is constructed;Then, DeepFM is used to mine the combined relationship between the grid features, combining spatial information to recommend the most suitable grid area for drivers;Finally, the performance evaluation is carried out using DiDi's public data. The experimental results show that this method can significantly improve the quality of the recommended results and is superior to some existing recommended methods. © 2021, Springer Nature Switzerland AG.

关键词： Location based services

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