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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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A Sleep Stage Classification Method via Combination of Time and Frequency Domain Features based on Single-Channel EEG

A Sleep Stage Classification Method via Combination of Time ...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Caihong Zhao Wenpeng Neng College of Computer Science and Technology College of Electronic Engineering Heilongjiang University Heilongjiang Key Laboratory of Database and Parallel Computing Harbin China College of Computer Science and Technology Heilongjiang University Harbin China

Sleep staging is an important method to diagnose and treat insomnia, sleep apnea, and other sleep disorders. Compared with the multi-channel automatic sleep staging system, the single-channel EEG signal contains less information, and the traditional single analysis domain feature parameter extraction algorithm cannot meet the requirement of sleep stage classification accuracy. To solve this problem, we propose an automatic sleep staging method based on the combination of time-domain and frequency-domain features based on single-channel EEG signals. Empirical mode decomposition is used to decompose EEG signals in the time domain to obtain the decomposed signals at different time scales. Multiple local features are extracted from each decomposed signal. The frequency-domain features of EEG signals are obtained by using the frequency domain decomposition of EEG signals in various rhythms. The time-domain and frequency-domain decomposition features are combined into eigenvectors and selected for sleep staging. The experimental results show that the sleep staging method proposed in this paper with time-frequency domain features of single-channel EEG signals can approach the accuracy of sleep staging of multi-channel signals on the same data set, and superior to the sleep staging method with the same single-channel EEG signals.

关键词： Deep learning Time-frequency analysis Empirical mode decomposition Sleep Frequency-domain analysis Feature extraction Electroencephalography

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Optimization and parallelization of MRF community detection algorithm for a specific network

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International Journal of Performability Engineering 2019年第8期15卷 2153-2164页

作者： Lu, Jun Zhang, Yuanzhong College of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

Research on the optimization and parallelization of the MRF network community detection algorithm for a specific network is carried out in this paper. Firstly, the principle of the existing algorithm is expounded, the algorithm is analyzed, and some problems are pointed out. Some optimization strategies and rules are proposed, including the extraction of variables and operations from inner loops to outer loops, the merging of related operations in loops, the removal of redundant loops, and the split of loops. In order to achieve better parallelism, OpenMP parallel computing of this method is realized by reversing the order of inner and outer loops. The influence of the density of network edges on the algorithm efficiency is also analyzed in this paper. The optimization and parallel algorithm can be applied to the module partition of Alzheimer's disease gene data, and the efficiency of the algorithm is greatly improved. The optimization strategies and rules proposed in this paper can be further extended to general situations. It is significant in practical applications. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Efficiency

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FasterBERT with Double Loss Function Based on Batch Data Preprocessing

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

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International Conference on Electronics, Communications and Information Technology (CECIT)

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

ISBN: (纸本)9781665437585

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.

关键词： Training Costs Bit error rate Data preprocessing Data models Natural language processing Information and communication technology

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An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index

An Incremental Partitioning Graph Similarity Search Based on...

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2020国际计算机前沿大会

作者： Yuhang Li Yan Yang Yingli Zhong School of Computer Science Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Graph similarity search is a common operation of graph database,and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms.

关键词： Graphsimilaritysearch Similaritysearch Graphpartition

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Faster BERT with Double Loss Function Based on Batch Data Preprocessing

Faster BERT with Double Loss Function Based on Batch Data Pr...

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作者： Pengqi Duan Yudong Zheng Jun Lu College of Computer Science and Technology Heilongiiang University Heilongjiang UniversityHLJU College of Computer Science and Technology Heilongjiang University Heilongjiang UniversityHLJU College of Computer Science and Technology Heilongiiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Heilongjiang UniversityHLJU

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 *** order to improve the training speed and inference speed of the pretrained model,the FasterBERT model is proposed in this *** model combines batch data preprocessing method with knowledge *** increases the model to increase the model speed and maintains the original ***,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 *** accuracy of the model is *** effectiveness of the FasterBERT model is demonstrated on 12 datasets in this *** speed of the FasterBERT is 1.1-2.2 times higher than FastBERT and 2-43 times higher than *** 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 *** experiments show that the FasterBERT model proposed in this paper performs well on six Chinese datasets and six English *** speed is greatly improved while the accuracy of the task also has a significant effect.

关键词： pre-trained language model FasterBert model double loss function batch data processing knowledge distillation

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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...

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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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Local partition subdivision algorithm for approximate nearest neighbor query

Local partition subdivision algorithm for approximate neares...

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Penghui Dong Yan Yang School of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing Heilongjiang Province Harbin China

This paper solves the problem of approximate nearest neighbor queries on high-dimensional large data sets. The two most representative methods to solve the approximate nearest neighbor query problem on high-dimensional and large-scale data sets are based on inverted multi-index and based on inverted index. The query algorithm based on the inverted multi-index requires frequent access operations to the memory, which causes more waste of resources than the query operation based on the distance order. Traditional query algorithms based on inverted indexes need to divide the entire feature space into a large number of regions to ensure the accuracy of query results when solving the problem of high-dimensional large data sets, so a large amount of space resources are required. Based on the inverted index, a query algorithm is proposed. Through the subdivision process of Voronoi Cells that are initially divided into part of the original data set, the purpose is to significantly improve the high-dimensional data set without dividing a large number of regions. On the query performance, and avoid frequent access operations to the random access memory. Theoretical analysis and experimental results show that the proposed method not only effectively improves the query processing efficiency, but also ensures the accuracy of the query.

关键词： Information science Control engineering Query processing Memory management Random access memory Approximation algorithms Partitioning algorithms

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Regulatory Element parallel Mining Algorithm Based on Bit Combination 12

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

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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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Influence Maximization in Microblog Social Network

Influence Maximization in Microblog Social Network

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International Conference on Information Science and Control Engineering (ICISCE)

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

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.

关键词： Information science Power measurement Control engineering Social networking (online) Blogs Linear programming

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