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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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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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VDTS: A Voronoi Diagram Based Tracking Schemes in Wireless Sensor Networks 12th

VDTS: A Voronoi Diagram Based Tracking Schemes in Wireless S...

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12th China Wireless Sensor Network Conference, CWSN 2018

作者： Ren, Qianqian Li, Jinbao Liu, Yong Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China School of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (纸本)9789811368332

Sleeping scheduling has been widely employed in target tracking due to its energy conservation. However, the randomness of target’s trajectory makes it difficult to implement with accuracy and real time guarantee. We propose VDTS, a novel, simple and efficient tracking technique. VDTS first constructs a Voronoi based network model, then makes nodes in the Voronoi polygon that the target is in work and others sleep. The target is hence detected by nodes closest to it. VDTS further presents a weighted centroid based algorithm to locate the target with the chosen nodes and reduce the influence of data noise on localization accuracy. We have implemented VDTS, and our extensive simulation show the excellent performs of our schemes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Target tracking

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User Local and Global Interest Depth Interaction for Recommendation Algorithm

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

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International Conference on Information Science and Control Engineering (ICISCE)

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

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.

关键词： Knowledge engineering Deep learning Information science Control engineering Social networking (online) Neural networks Feature extraction

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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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Prediction Based Reverse Auction Incentive Mechanism for Mobile Crowdsensing System 13th

Prediction Based Reverse Auction Incentive Mechanism for Mob...

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13th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2019

作者： Wang, Zhen Zhu, Jinghua Li, Doudou School of Computer Science and Technology Heilongjiang University Harbin150001 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9783030364113

With the rapid development of the Internet, Mobile Crowdsensing System (MCS) is widely used in various fields. Because of the insufficient number of users’ participation and the insufficient amount of uploaded sensing data, the research of incentive mechanism is particularly important in MCS. Reverse auction mechanism is one of the efficient incentive mechanism in MCS. The platform is responsible for publishing a group of tasks which are bidded by the workers, and only the winning workers are authorized to complete the tasks. One of the critical factors for successful bidding is the distance between the tasks and the workers. However, most workers in MCS always keep moving. So this paper proposes a prediction based reverse auction incentive mechanism—RMMP. We use the Semi-Markov model to predict the position of workers at the next moment. According to the predicated positions, our reverse auction incentive mechanism selects the winner workers with the minimum movement distance and bidding price. Experiment results on real dataset show that our RMMP mechanism has remarkable performance compared with the existing mechanisms. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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

Node classification problem based on potential information a...

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International Conference on Information Science and Control Engineering (ICISCE)

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

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.

关键词： Information science Electronic publishing Correlation Control engineering Network topology Social networking (online) Information services

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Community Search in Spatial Uncertain Network

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Journal of Physics: Conference Series 2021年第4期1952卷

作者： Wenqian Zhang Yingli Zhong Yan Yang School of Computer Science Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is called spatial uncertain network. In order to meet the existing needs, this paper studies the community search in the spatial uncertain network so that the searched communities can be relatively close in the geographical location and the relationship between each other is also very close. Based on the spatial uncertain graph, this paper proposes K-R-τ community, and proposes a linear algorithm and a two-dimensional algorithm for community search in the spatial uncertain network. A large number of experiments are carried out on several real datasets, and the results show that the algorithm proposed in this paper is effective and accurate.

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