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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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Local partition subdivision algorithm for approximate nearest neighbor query 7

Local partition subdivision algorithm for approximate neares...

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

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

ISBN: (纸本)9781728164069

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. © 2020 IEEE.

关键词： Nearest neighbor search

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An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index 6th

An Incremental Partitioning Graph Similarity Search Based on...

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

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

ISBN: (纸本)9789811579806

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. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Sensitivity loss training based implicit feedback

Sensitivity loss training based implicit feedback

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International Conference on parallel and Distributed Systems (ICPADS)

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

ISBN: (纸本)9781665408790

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.

关键词： Training Learning systems Adaptation models Sensitivity Conferences Recommender systems

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International Conference on Information Science and Technology (ICIST)

作者： Zhongqing Wang Yan Yang Yingli Zhong School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Graph similarity search is an important research problem in many applications, such as finding result graphs that have a similar structure to a given entity in biochemistry, data mining, and pattern recognition. Top-k graph similarity search is one of graph similarity search tasks, which aims to find the top-k graphs that are most similar to the query graph in a given graph database. In this paper, the top-k similarity search problem based on the graph edit distance is studied according to the corollary of the partitioned similarity theorem. Firstly, in order to speed up the online search process and avoid scanning each graph in the database one by one, an offline hierarchical inverted index is constructed to satisfy top-k search. Secondly, the offline hierarchical inverted index is used to filter the candidate graphs online and verify them, which reduces the time of searching the graphs. Finally, the good performance of the algorithm in running time and scalability is verified by running the similarity algorithm on real dataset and synthetic dataset.

关键词： Information science databases Scalability Filtering algorithms Search problems Partitioning algorithms Indexes

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

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

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

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.

关键词： Wavelet transforms Thermal decomposition Wavelet analysis Visual effects Thermal analysis Image fusion Optimization

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Node classification problem based on potential information and representation learning between nodes 7

Node classification problem based on potential information a...

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

作者： Li, Mingming Yang, Yan Heilongjiang University School of Computer Science Technology Hearbin Province China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin Province China

ISBN: (纸本)9781728164069

Representation learning of social network nodes refers to the feature expression of nodes in low-dimensional vector space. Most of the current methods are based on the network topology to achieve node feature learning, but the potential information of the network topology is not fully utilized. To solve this problem, we propose a graph embedding algorithm based on the similarity between nodes. This method obtains a new network topology through the second-order similarity between nodes. This network topology can enhance the correlation between nodes, thereby improving the effect of node classification in the network. We use Macro-F1 and Micro-F1 evaluation indicators to evaluate the benchmark algorithm and the SNE algorithm proposed in this paper on four real data sets. The experimental results show that the SNE algorithm can improve the effect of node classification. © 2020 IEEE.

关键词： Network topology

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NKMH: A Neural Efficient Recommendation Based on Neighborhood key Information Aggregation of Modified Hawkes

NKMH: A Neural Efficient Recommendation Based on Neighborhoo...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xin Xu Nan Wang Hu Jin Yang Liu Kun Li Key Laboratory of Database and Parallel Computing of Heilongjiang Province School of Computer Science and Technology Heilongjiang University Harbin China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd School of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The rapid development of neural networks has con-tributed to the increasing maturity of recommendation systems. However, deep neural networks have poor interpretability for models and do not show strong advantages for sparse data and noisy data. Recently, Hawkes process has become more and more focused for its good interpretability with probabilistic models. Based on this, we proposes A Neural Efficient Recommendation Model Based on Neighborhood key Information Aggregation of Modified Hawkes(NKMH). The model utilizes a neural network and designs three modules to jointly fit the modified Hawkes process. It not only inherits the high interpretability of Hawkes, but also effectively solves the problem of poor prediction ability of the Hawkes process. Besides, we present a novel key information search strategy(KISS), which can effectively remove the noise in a session and alleviate the sparsity of the data to some extent. Extensive experiments on two datasets show that the NKMH model outperforms many current popular models.

关键词： Deep learning Conferences Neural networks Predictive models Search problems Probabilistic logic Software

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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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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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