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检索条件"机构=Key Laboratory of Database and Parallel Computing"

共 134 条记录，以下是11-20 订阅

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User Local and Global Interest Depth Interaction for Recommendation Algorithm 7

User Local and Global Interest Depth Interaction for Recomme...

引用

7th International Conference on Information Science and Control Engineering, ICISCE 2020

作者： Zhang, Xufeng Yang, Yan Heilongiiang University School of Computer Science Technology Harbin China Key Laboratory of Database and Parallel Computing Harbin China

ISBN: (纸本)9781728164069

Using auxiliary information such as social networks or item attributes to improve the performance of recommender systems is a key issue in the research of recommender systems. It plays a very important role in alleviating the interactive sparsity of collaborative filtering methods. In response to this problem, a recommendation algorithm that user local and global interest depth interaction is proposed, which analyzes the user's implicit preferences from two perspectives: based on the user's historical behavior records, using a fully connected neural network to extract the user's local interests;based on the user's historical behavior items, through the association between entities in the knowledge graph, mines the entities associated with the user's historical behavior, forming a multi-layer global interest layer to generalize user interests. The user's local interest vector activates the item entity in the global interest layer to generate the user's global interest vector. Finally, the deep neural network is used to complete the feature interaction of users' local interests, global interests and recommended items. The user's local and global interests accurately describe user preferences and improve the accuracy of click-through rate prediction. Experiments show that the recommendation performance of the algorithm on the two public data sets is better than the comparison baseline and the classic recommendation algorithm. © 2020 IEEE.

关键词： Recommender systems

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Visible-Infrared Image Fusion Based on Double- Density Wavelet and Thermal Exchange Optimization 5

Visible-Infrared Image Fusion Based on Double- Density Wavel...

引用

5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Guo, Hao Chen, Jingyu Yang, Xuan Jiao, Qingliang Liu, Ming Beijing Institute of Environmental Features Beijing China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Infrared and visible image fusion has been a hot issue in image fusion. However, the parameters and result image have many proposed. In this paper, a novel visible infrared image fusion algorithm based on double-density wavelet and thermal exchange optimization is proposed. The pre-processed infrared and visible images are decomposed into detail layers and background layers by double-density wavelet transform. Thermal exchange optimization is used to estimate the number of layers and weight thresholds of decomposition. Experimental shows that both visual effects and quantitative analysis of proposed algorithm can get the best results. © 2021 IEEE.

关键词： Image fusion

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Literature recommendation based on reference graph

Literature recommendation based on reference graph

引用

International Conference on Advanced Computer Theory and Engineering

作者： Yan, Yang Long, Yun Key Laboratory of Database and Parallel Computing of Heilongjiang Province Department of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (纸本)9781424465408

Research papers are frequently requested in digital library. When users query for documents about a research problem, users usually want all the related documents and the citations with the relations among them, to enable the user know the development of the research area. Traditional recommendation techniques are mainly based on similarity among users or documents to do recommendation. They do not use the relationships among documents and, especially, cannot provide topology structure of related documents to the user. This paper proposes a new recommendation technique based on reference graph of documents. Combined with traditional recommendation techniques, it can recommend research paper more completely, accurately and clearly. Experimental results show that the reference graph based recommendation technique is more effective. © 2010 IEEE.

关键词： Digital libraries

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Influence Maximization in Microblog Social Network 7

Influence Maximization in Microblog Social Network

引用

7th International Conference on Information Science and Control Engineering, ICISCE 2020

作者： Ma, Zhengyi Yang, Yan Heilongjiang University School of Computer Science Technology Harbin China Key Laboratory of Database and Parallel Computing Harbin China

ISBN: (纸本)9781728164069

In recent years, the continuous rise of social networks has promoted the research of maximizing influence. Therefore, it is more and more important to mine a group of users with the largest influence range in social network, which is also of great significance for the promotion of some products. To solve the above problems, this paper studies the influence maximization in microblog social network, and proves that the problem is NP hard, and the objective function of the problem satisfies monotonicity and submodule. An effective algorithm is proposed to solve this problem. The algorithm fully considers the nature of microblog social network and microblog users, and measures the influence of users themselves with activity, communication power and coverage. Based on the independent cascade model, a communication model is designed according to the influence of users and the interaction times between users. Through the experiment on the real microblog data set, it is proved that the algorithm has good performance in the propagation range and operation efficiency, and the propagation effect is more in line with the reality. © 2020 IEEE.

关键词： Economic and social effects

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Sensitivity loss training based implicit feedback 27

Sensitivity loss training based implicit feedback

引用

27th IEEE International Conference on parallel and Distributed Systems, ICPADS 2021

作者： Li, Kun Wang, Nan Liu, Xinyu School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781665408783

In recommender systems, due to the lack of explicit feedback features, datasets with implicit feedback are always accustomed to train all samples without separating them during model training, without considering the non-consistency of samples. This leads to a significant decrease in sample utilization and creates challenges for model training. Also, little work has been done to explore the intrinsic laws implied in the implicit feedback dataset and how to effectively train the implicit feedback data. In this paper, we first summarize the variation pattern of loss with model training for different rating samples in the explicit feedback dataset, and find that model training is highly sensitive to the ratings. Second, we design an adaptive hierarchical training function with dynamic thresholds that can effectively distinguish different rating samples in the dataset, thus optimizing the implicit feedback dataset into an explicit feedback dataset to some extent. Finally, to better learn samples with different ratings, we also propose an adaptive hierarchical training strategy to obtain better training results in the implicit feedback dataset. Extensive experiments on three datasets show that our approach achieves excellent performance and greatly improves the performance of the model. © 2021 IEEE.

关键词： adaptive hierarchical training implicit feedback sample utilization sensitive

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An efficient Algorithm for Frequent Pattern Mining over Uncertain Data Stream 12

An efficient Algorithm for Frequent Pattern Mining over Unce...

引用

12th International Symposium on Computational Intelligence and Design, ISCID 2019

作者： Xie, Mingye Tan, Long Heilongjiang University Colleage of Computer Science and Technology China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781728146522

In recent years, due to the increasing demand for real-time data processing, data mining on stream gradually becomes a research hotspots, mining over uncertain data stream is a more practical compare to over precise data stream, which doesn't take uncertainty of data in real world into consideration. Most of the popular algorithms of uncertain data stream mining are based on tree structure, all branches in the tree need be retrieved to find all frequent patterns while is time-consuming. Considering this defect of tree structure, a new structure Uncertain Item-lists (UIT-lists) and a efficient Uncertain Frequent Stream mining algorithm(UFS-mine) is presented. Experiments show our approach has great performence on runtime and with same memory useage compare to an up-to-state uncertain stream data mining algorithm. © 2019 IEEE.

关键词： Data mining

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MND: An information of detected neighbors based multi-channel neighbor discovery protocol for wireless sensor networks

MND: An information of detected neighbors based multi-channe...

引用

6th China Conference of Wireless Sensor Networks, CWSN 2012

作者： Li, Jinbao Wang, Zhigang Yao, Liang Guo, Longjiang School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

ISBN: (纸本)9783642362514

Multi-channel neighbor discovery protocol based on information of detected neighbors for wireless sensor networks is proposed in this paper, called MND. To make sure that the discovery among nodes is as quick as possible, MND uses the schedule mechanism in which the nodes compute the potential neighbors set so as to calculate the sleep and wake schedule of the node. To minimize the influence of channel conflict to the performance, MND uses multi-channel mechanism and calculates the required total number of channels according to the duty cycle and network density. Through analysis, we can get how to choose the total number of channels for each node and the fact that the common neighbor node of the neighbors of a node is the neighbor of this node in a large probability. The simulation and test-bed experiment results show the discovery rate and discovery latency of MND is higher and lower, respectively. © 2013 Springer-Verlag.

关键词： Sensor nodes

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FasterBERT with Double Loss Function Based on Batch Data Preprocessing 2

FasterBERT with Double Loss Function Based on Batch Data Pre...

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2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Duan, Pengqi Zheng, Yudong Lu, Jun College of Computer Science and Technology Heilongjiang University HLJU Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781665437578

Even though pre-trained language models like BERT and XLNet have produced significant consequences on a variety of tasks of natural language processing, they are difficult to deploy in practical applications due to their large number of parameters, and huge calculation and memory costs. In order to improve the training speed and inference speed of the pre-trained model, the FasterBERT model is proposed in this paper. This model combines batch data preprocessing method with knowledge distillation. It increases the model to increase the model speed and maintains the original accuracy. Furthermore, double loss function is incorporated during the knowledge distillation process in this paper, so that the model can better learn the knowledge of the teacher model and the knowledge of the real labels. The accuracy of the model is improved. The effectiveness of the FasterBERT model is demonstrated on 12 datasets in this paper. Training speed of the FasterBERT is 1.1-2.2 times higher than FastBERT and 2-43 times higher than BERT. In addition, the accuracy is up to 0.6% higher than that of FastBERT, and the accuracy is better than that of the BERT model on three datasets. The experiments show that the FasterBERT model proposed in this paper performs well on six Chinese datasets and six English datasets. The speed is greatly improved while the accuracy of the task also has a significant effect. © 2021 IEEE.

关键词： Speed

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Influence Equilibrium Problem Research Based on Common Interests in Mobile Social Network 12

Influence Equilibrium Problem Research Based on Common Inter...

引用

12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Li, Jinbao Han, Yue Zhu, Jinghua School of Computer Science and Technology Heilongjiang University China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781509056965

Influence maximization refers to the number of influenced nodes reach to maximum by finding a set of seed nodes in social network. Due to peoples' excessive concentration of attention, sometimes, negative effects will appear during the influence propagation. However, the negative effects on influence propagation are not involved in the existing research work. In this paper, we consider the influence equilibrium problem in mobile social network. The goal of influence equilibrium is to balance the influential range of different kinds of seed sets through the way of node scheduling. Our goal is to reduce the negative effects by transforming the status of influence from excessive concentration to relatively balanced. There are two main challenges: (1) which nodes should be scheduled in mobile social network in order to achieve a balanced effect. (2) how to schedule the nodes. To address the two challenges, we estimate the randomness of nodes and then schedule the nodes which have high randomness. The entropy-based random walk select balance algorithm(RSB) is proposed in this paper. To achieve the balanced effect, random walk procedure is adopted to choose the sets that can receive the scheduled nodes during the scheduling phase. However, new imbalance may arise during this procedure. Thus, the entropy-based max-min balance algorithm(MMB) is proposed to deal with the new imbalance caused by RSB algorithm. Theoretical analysis and experimental results show that the RSB and MMB algorithm can balance the influence range and MMB performs better than RSB. © 2016 IEEE.

关键词： Entropy

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Regulatory Element parallel Mining Algorithm Based on Bit Combination 12

Regulatory Element Parallel Mining Algorithm Based on Bit Co...

引用

12th International Symposium on Computational Intelligence and Design, ISCID 2019

作者： Zhou, Kailong Lu, Jun Zhao, Renpeng Lv, Xingfeng Heilongjiang University College of Computer Science and Technology Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781728146522

Regulatory element parallel mining algorithm of frequent itemset based on bit combination is proposed in this paper. The algorithm is represented by converting promoter data filtered by frequent itemset into binary machine bits, and then mining the frequent itemset of the data set in parallel by means of regulatory element. In the whole mining process, if a combination of regulatory elements is frequent, data should be added 1, and if it is not frequent, the data should be pruned. Preprocessing, eliminating and other operations are used to optimize the whole algorithm. Converting the processed raw data and regulatory element into binary bits facilitates the parallel operation of data and saves memory space, breaking through the limitations of traditional methods that cannot be effectively parallelized due to recursive operations in the mining process. In this thesis, OpenMP technology is used to parallelize the algorithm based on regulatory element, which improves the efficiency of the algorithm. © 2019 IEEE.

关键词： Data mining

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