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Joint user profiling with hierarchical attention networks

Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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Clustering moving objects in spatial networks

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12th International Conference on database Systems for Advanced Applications

作者： Chen, Jidong Lai, Caifeng Meng, Xiaofeng Xu, Jianliang Hu, Haibo School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9783540717027

Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.

关键词： spatial-temporal databases moving objects clustering spatial networks

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The Development Trends of Fuel Cell Technologies Based on Patent Analysis

The Development Trends of Fuel Cell Technologies Based on Pa...

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2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Tan Xinyu Zhang Yingxuan Deng Sanhong School of Information Management Nanjing university Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Province

Fuel cells are made from fuel and *** of its low pollution,high energy conversion efficiency and high reliability,fuel cell has become the future direction of new energy application,the technological development path in the field of fuel cell research has great significance to the development of technological and energy *** the patent analysis method,this paper analyses the patent data from Derwent Innovation Index quantitively to study the state of application for patents,core technologies,highly cited patents and the main *** shows that auxiliary device and related methods were a research hotspot in recent years;as the biggest patent holder of fuel cell technologies,Toyota,Honda motor *** Nissan motor *** an *** paper has discovered some potential problems behind the phenomena and some suggestions are put forward finally.

关键词： Fuel cells

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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

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Journal of Computer Science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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Distributed join algorithms on multi-CPU clusters with GPUDirect RDMA 19

Distributed join algorithms on multi-CPU clusters with GPUDi...

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48th International Conference on Parallel Processing, ICPP 2019

作者： Guo, Chengxin Chen, Hong Zhang, Feng Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE School of Information Renmin University of China China

ISBN: (纸本)9781450362955

In data management systems, query processing on GPUs or distributed clusters have proven to be an effective method for high efficiency. However, the high PCIe data transfer overhead between CPUs and GPUs, and the communication cost between nodes in distributed systems are usually bottleneck for improving system performance. Recently, GPUDirect RDMA has been developed and has received a lot of attention. It contains the features of the RDMA and GPUDirect technologies, which provides new opportunities for optimizing query processing. In this paper, we revisit the join algorithm, one of the most important operators in query processing, with GPUDirect RDMA. Specifically, we explore the performance of the hash join and sort merge join with GPUDirect RDMA. We present a new design using GPUDirect RDMA to improve the data communication in distributed join algorithms on multi-GPU clusters. We propose a series of techniques, including multi-layer data partitioning, and adaptive data communication path selection for various transmission channels. Experiments show that the proposed distributed join algorithms using GPUDirect RDMA achieve up to 1.83x performance speedup compared to the state-of-the-art distributed join algorithms. To the best of our knowledge, this is the first work for distributed GPU join algorithms. We believe that the insights and implications in this study shall shed lights on future researches using GPUDirect RDMA. © 2019 ACM.

关键词： Graphics processing unit

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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

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China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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Diversifying Question Generation over knowledge Base via External Natural Questions 30

Diversifying Question Generation over Knowledge Base via Ext...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guo, Shasha Zhang, Jing Ke, Xirui Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9782493814104

Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that diverse texts can express identical semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the aforementioned diversity. They calculate the ratio of unique n-grams in the generated question, which tends to measure duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG show that our approach can outperform pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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COP: Privacy-preserving multidimensional partition in DAS paradigm 09

COP: Privacy-preserving multidimensional partition in DAS pa...

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2009 International Conference on Extending database Technology/International Conference on database Theory Workshops, EDBT/ICDT '09

作者： Wang, Jieping Du, Xiaoyong Wang, Haocong Yang, Pingping Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781605586502

database-as-a-Service (DAS) is an emerging database management paradigm wherein partition based index is an effective way to querying encrypted data. However, previous research either focuses on one-dimensional partition or ignores multidimensional data distribution characteristic, especially sparsity and locality. In this paper, we propose Cluster based Onion Partition (COP), which is designed to decrease both false positive and dead space at the same time. Basically, COP is composed of two steps. First, it partition covered space level by level, which is like peeling of onion;second, at each level, a clustering algorithm based on local density is proposed to achieve local optimal secure partition. Extensive experiments on real dataset and synthetic dataset show that COP is a secure multidimensional partition with much less efficiency loss than previous top down or bottom up counterparts. Copyright 2009 ACM.

关键词： database systems

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DSM: Question Generation over knowledge Base via Modeling Diverse Subgraphs with Meta-learner

DSM: Question Generation over Knowledge Base via Modeling Di...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Guo, Shasha Zhang, Jing Wang, Yanling Zhang, Qianyi Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semantics-specific and semantics-agnostic knowledge on and across these tasks. Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation. Codes and datasets are available online. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Maintaining materialized relations incrementally to improve performance of ontology query

Maintaining materialized relations incrementally to improve ...

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7th International Conference on Web-Age Information Management Workshops, WAIM 2006

作者： Li, Man Du, Xiaoyong Wang, Shan School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE 100872 Beijing China

ISBN: (纸本)0769527051

For ontology-based applications, the efficiency of ontology query is vital. Different from existing approaches, the paper improves performance of ontology query by materializing some derived relations. Experimental results show that the integrated performance of ontology query can be improved greatly by maintaining materialized relations and the materialized relations technique has good scalability. Here the challenge is how to maintain the materialized relations incrementally with the update of ontologies. Because transitive relations are in common use in ontology, the paper proposes a novel algorithm for maintaining transitive materialized relations incrementally based on a special weighted materialized relation transitive graph, which can solve the coexistence problem of multiple derived paths better and proves the correctness of the algorithm. © 2006 IEEE.

关键词： Ontology

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