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

Node classification problem based on potential information a...

引用

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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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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FasterBERT with Double Loss Function Based on Batch Data Preprocessing 2

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

引用

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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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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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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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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Local partition subdivision algorithm for approximate nearest neighbor query 7

Local partition subdivision algorithm for approximate neares...

引用

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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引用

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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