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检索条件"机构=Key Laboratory of Database and Parallel Computing. Harbin"

共 119 条记录，以下是21-30 订阅

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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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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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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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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...

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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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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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Influence Equilibrium Problem Research Based on Common Interests in Mobile Social Network 12

Influence Equilibrium Problem Research Based on Common Inter...

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

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

引用

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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BIVD: Improved Side Adapter Network based on BiFormer 5

BIVD: Improved Side Adapter Network based on BiFormer

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

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

ISBN: (纸本)9798350374407

Currently, most open vocabulary models rely on CLIP (Contrastive Language-Image Pre-training) to classify masked regions, but CLIP models still have many drawbacks. The traditional transformer model is context aware through self attention, but this local context based mechanism is limited by the sequence length of information, it can also impact model effect. Inspired by the article BiFormer, This article replaces the traditional transformer model in the Side Adapter Network (SAN) model with a variant module BIVD block based on BiFormer blocks. This model follows the latest transformer model and extracts features using a four layer feature pyramid structure, For the reduced spatial resolution caused by patch merging, add a Visual-Decoder to reshape the visual vector sequence into a spatial feature map, and restore it to the original resolution through convolution and upsampling. This can enhance the extracted feature information through a feature pyramid without affecting the fusion with CLIP layer features. But the cost is increasing the cost of the model. This article was tested on 5 commonly used datasets and showed a stable improvement in accuracy. © 2024 IEEE.

关键词： BiFormer CLIP component side adapter Visual Decoder

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parallel optimization computing.in universal combinatorial coding

Parallel optimization computing in universal combinatorial c...

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2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, MEIC 2014

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

ISBN: (纸本)9789462520424

Universal combinatorial coding derives from the combination principle and its core is ordinal calculation. The solution of ordinal is a process of computing.permutation and combination values. But permutation and combination computing.is very time-consuming. As a result, adopting optimized methods or parallel techniques to increase ordinal calculation efficiency is very necessary. parallel optimization algorithms of the ordinal in universal combinatorial coding are advanced based on CPU multi-core and multi-thread techniques in this paper, including prime splitting and unequal parallel algorithms. Firstly, the thesis simplifies the multiplication and division calculations of the ordinal parallel computing.through prime splitting optimization. Secondly, the thesis analyzes the deficiency of equal parallel that the unbalanced computing.time of each part will lead to inefficient parallel computing.of the ordinal, and then proposes the unequal parallel algorithm to shorten the ordinal number calculation. The experiment results of prime splitting and unequal parallel algorithm proved that two parallel optimizations are better in improving the efficiency of ordinal computing. © 2014. The authors - Published by Atlantis Press.

关键词： parallel algorithms

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