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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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11th International Conference on Information Science and Technology, ICIST 2021

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

ISBN: (纸本)9781665412667

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. © 2021 IEEE.

关键词： Graph databases

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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...

引用

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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Using gaussian mixture model to fix errors in SFS approach based on propagation 2nd

Using gaussian mixture model to fix errors in SFS approach b...

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2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Huang, Wenmin Ma, Jiquan Zhang, Enbin College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9789811020520

A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, interdependence of propagation nodes and error accumulation. To slow convergence and interdependence of propagation nodes, stable propagation source and integration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions is proposed to fix the error of integration. Good result has been achieved in the experiment for Lambert composite image of the front illumination. © Springer Science+Business Media Singapore 2016.

关键词： Errors

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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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Negative influence maximization in social networks 4th

Negative influence maximization in social networks

引用

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Zhu, Jinghua Li, Bochong Zhang, Yuekai Li, Yaqiong 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)9789811322020

Influence maximization is one of the key research problems in social networks due to its wide applications like spread of ideas and viral marketing of products. Most of the existing work focus on social networks containing only positive relationships (e.g. friend or trust) between users, but in reality social networks containing both positive and negative relationships (e.g. foe or distrust). Ignoring the negative relations may lead to over-estimation of positive influence in practical applications. Thus, in this paper, we study influence maximization problem with negative effects (NIM). To address the NIM problem, we use the polarity Independent Cascade (IC-P) diffusion model which extends the standard Independent Cascade (IC) model with negative opinions. Then we propose the positive influence maximization algorithm (PIM) and negative influence maximization (NIM) problem. We prove that influence function of the NIM problem is monotonic and submodular, so we propose a CELF based algorithm (CELF_NIM) to solve it. Experiments results show that our algorithm has matching influence spread compared with greedy algorithm and achieves several orders of magnitude time improvement. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Economic and social effects

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Contrastive Enhanced Filter Model Based on Bidirectional Transformer for Sequential Recommendation 5

Contrastive Enhanced Filter Model Based on Bidirectional Tra...

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

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

ISBN: (纸本)9798350374407

Transformer-based sequential recommendation is highly powerful as it can capture short-term and long-term sequential recommendation. It plays a crucial role in personalized recommendation systems, aiming to extract dynamic and diverse preference information from users' realtime growing behaviors. This is mainly due to its unique selfattention mechanism, which utilizes item-item interactions within the sequences. However, in online platforms, due to the interweaving of items in user behavior, user behavior data inevitably contains noise. Currently, most methods model user preferences in the time domain, overlooking the influence of frequency patterns on user behavior. To address this issue, this paper shifts the perspective to the frequency domain and proposes a novel contrast-enhanced filtering model for sequential recommendation based on the bidirectional Transformer, called CEFRec. Specifically, the paper designs a filtering layer on top of the bidirectional Transformer, allowing learnable filters to operate on different layers' spectra. This enables the model to capture various frequency patterns and adaptively attenuate noise information within the frequency domain. Next, the model combines the cloze task mask and dropout masks to generate high-quality positive samples and utilizes contrastive learning to enhance the quality of user embeddings learned from the frequency domain. Finally, experimental results on three public benchmark datasets demonstrate that the CEFRec model outperforms state-of-theart sequential recommendation models. © 2024 IEEE.

关键词： Contrastive Learning

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

Prediction Based Reverse Auction Incentive Mechanism for Mob...

引用

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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A New Feature Fusion Method Based on Pre-Training Model for Sequence Labeling 6

A New Feature Fusion Method Based on Pre-Training Model for ...

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6th International Conference on Data Storage and Data Engineering, DSDE 2023

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

ISBN: (纸本)9798350333145

To fuse vocabulary features into the pre-training model is the mainstream data feature processing method for sequence labelling tasks. In general, the feature fusion methods that have been proposed at present are direct fusion outside the pre-training model or fusion of lexical features using attention mechanism. However, the study found that this way of vocabulary enhancement does not conform to the word formation rules of modern Chinese. In the Chinese language, it is easy to fuse irrelevant or even incorrect lexical features into the sequence using the above feature processing methods, which is bad for the experimental results of the Chinese sequence labelling task. To solve these problems, we propose to use Cosine Similarity Adapter to process lexical features in Chinese sequence labelling tasks. CSBERT is a hybrid model using this structure based on BERT, which conforms to the word formation rules of modern Chinese to a certain extent. It can fuse the features of the word into the character or eliminate the features of the word in the character according to the cosine similarity between the character vector and a word vector. The experimental results show that CSBERT has better ability to label Chinese sequences than the benchmark model. CSBERT has achieved the best experimental results such as F1-Score on 7 open datasets and the best ability of multi-label classification, which proves that the model has good practical value. © 2023 IEEE.

关键词： Classification (of information)

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CTS: Combine temporal influence and spatial influence for time-aware POI recommendation 2nd

CTS: Combine temporal influence and spatial influence for ti...

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2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Zhang, Hanbing Yang, Yan Zhang, Zhaogong School of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9789811020520

As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research *** recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation: sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user’s check-in data. Our experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially. © Springer Science+Business Media Singapore 2016.

关键词： Collaborative filtering

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