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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...

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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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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Dynamic Vehicle Distribution Path Optimization Based on Collaboration of Cloud, Edge and End Devices 15th

Dynamic Vehicle Distribution Path Optimization Based on Coll...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Li, Tiancai Wen, Yiping Tan, Zheng Chen, Hong 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

ISBN: (纸本)9789811625398

Aiming at the problems of unreasonable distribution routes in the current logistics distribution field, without considering the impact of real-time road conditions, and the inability to reduce the impact on the timeliness of distribution, this paper proposes a dynamic vehicle distribution path optimization method based on the collaboration of cloud, edge and end devices. This method considers the requirements of demand points for the delivery time and considers the changes in road traffic conditions caused by random road traffic incidents. Combining the characteristics of vehicle speed and time penalty cost in the vehicle delivery process establishes a logistics delivery vehicle path optimization model. Solve it and optimize it with the A* algorithm and dynamic schedule. This method collects road condition data in real-time through terminal equipment, evaluates and judges road conditions at the edge, and makes real-time adjustments to the distribution plan made in advance at the cloud data center. Through simulation experiments on application examples, the vehicle path optimization method proposed in this paper that considers real-time road conditions changes and the optimization method that does not consider road conditions are compared and analyzed, verifying the effectiveness of this method. Experimental results show that this method can reduce distribution costs, reduce distribution time, and reduce the impact of changes in road conditions on the distribution results. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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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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Research on Machine Learning Method for Equipment Health Management in Industrial Internet of Things 15th

Research on Machine Learning Method for Equipment Health Man...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Tan, Zheng Wen, Yiping 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

ISBN: (纸本)9789811625398

Much industrial equipment integrates multiple types of sensors for data collection and real-time connection with the Industrial Internet of Things (IIoT) at now. With the popularity and application of miniaturized and low-cost sensors, to manage the health of equipment in the IIoT, such as evaluating the current health indicator (HI) of the equipment, and predicting its remaining useful life (RUL), the machine learning-based method reflects a broader application prospect due to its good at data mining and analysis. A RUL prediction method of equipment in this paper is proposed. First, a specific data processing method is proposed according to the characteristics of the data, the outputs of the hidden layer of stacked denoising autoencoder (SDAE) as the features of the data set which are extracted to complete the HI construction. Then, the trajectory pointwise difference and similarity method are proposed to generate predicted equipment RUL. This paper uses the C-MAPSS engine data set for experimental verification. The experimental results show that the method proposed in this paper can effectively use the data of multi-dimensional sensor data to get the degradation progress of the equipment, thereby assessing the health of the equipment and more accurately estimating the RUL. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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An Online Machine Learning-Based Prediction Strategy for Dynamic Evolutionary Multi-objective Optimization 11th

An Online Machine Learning-Based Prediction Strategy for Dyn...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Min Chen, Diankun Zhang, Qiongbing Jiang, Lei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan4111201 China Key Laboratory of Knowledge Processing and Networked Manufacturing College of Hunan Province Xiangtan4111201 China

ISBN: (纸本)9783030720612

Due to the impact of environmental changes, dynamic evolutionary multi-objective optimization algorithms need to track the time-varying Pareto optimal solution set of dynamic multi-objective optimization problems (DMOPs) as soon as possible by effectively mining historical data. Since online machine learning can help algorithms dynamically adapt to new patterns in the data in machine learning community, this paper introduces Passive-Aggressive Regression (PAR, a common online learning technology) into dynamic evolutionary multi-objective optimization research area. Specifically, a PAR-based prediction strategy is proposed to predict the new Pareto optimal solution set of the next environment. Furthermore, we integrate the proposed prediction strategy into the multi-objective evolutionary algorithm based on decomposition with a differential evolution operator (MOEA/D-DE) to handle DMOPs. Finally, the proposed prediction strategy is compared with three state-of-the-art prediction strategies under the same dynamic MOEA/D-DE framework on CEC2018 dynamic optimization competition problems. The experimental results indicate that the PAR-based prediction strategy is promising for dealing with DMOPs. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

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Journal on Artificial Intelligence 2021年第1期3卷 33-43页

作者： Yizhi Liu Rutian Qing Liangran Wu Min Liu Zhuhua Liao Yijiang Zhao Key Laboratory of Knowledge Processing and Networked Manufacturing College of Hunan ProvinceXiangtan411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China

In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal *** at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG *** integrate greedy algorithm and hillclimbing algorithm into genetic *** algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and *** approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in *** results show that our method outperforms some other methods in the field.

关键词： Large-scale logistics distribution vehicle routing greedy algorithm hill-climbing algorithm hybrid genetic algorithm

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A Hybrid Data-Driven Approach for Predicting Remaining Useful Life of Industrial Equipment 6th

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

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6th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2020

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

ISBN: (纸本)9789811579837

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). © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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