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Improving Rating Estimation in Recommender Using Demographic Data and Expert Opinions

Improving Rating Estimation in Recommender Using Demographic...

2011 2nd IEEE International Conference on Software Engineering and Service Science(2011年第二届 IEEE 软件工程与服务科学国际会议 ICSESS 2011)

作者： Long Yun Yan Yang Jing Wang Ge Zhu Dept. of Computer Science and Technology Heilongjiang University Key Lab. of Database and Parallel Computing Heilongjiang Province Harbin China

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, however the rating data are very sparse, which affects prediction accuracy greatly. To solve this problem, we use expert opinions to improve prediction accuracy. Firstly, we propose a novel similarity measure in order to highlight users' background. Then, combining users' ratings with expert opinions, the prediction get a right balance in both expert professional opinions and similar users. Finally, since SVD-based collaborative filtering algorithms shows good performance on the prevention of noise, we use it to smooth the prediction. Experiments of MovieLens have shown that our proposed method improves recommendation quality obviously.

关键词： Recommendation Collaborative Filtering Data Sparsity Expert Demography

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Cross-Modality Encoder Representations Based On External Attention Mechanism

Cross-Modality Encoder Representations Based On External Att...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Yudong Zheng Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

With the prevalence of deep learning, people use multi-modality information for interpretation and reasoning. In this paper, a cross-modality encoder CMEEA (cross-modality encoder representation based on external attention mechanism) is adopted to improve the accuracy of the model. The two external storage units of CMEEA can be regarded as a dictionary of the whole dataset to improve the performance of the network, while learning more representative features of the input and reducing the computational cost. This paper uses five pretraining tasks that help the model learn internal modal and cross-modal relationships. It also verifies the generalizability of the model by applying the pretraining cross-modal model to the visual inference task NLVR 2 , and improves the previous results by 0.1%. By fine-tuning the pretrain parameters, the model improves by 1.3% on visual question answering (VQA).

关键词： Training Deep learning Analytical models Visualization Dictionaries Computational modeling Question answering (information retrieval)

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The key Algorithms of Promoter Data parallel Processing Based on OpenMP

The Key Algorithms of Promoter Data Parallel Processing Base...

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International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC

作者： Yan Shi Jun Lu Xinyi Shi Jinliang Zheng Jun Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China University of Chinese Academy of Sciences Heilongjiang University Harbin Heilongjiang CN

The key algorithm of parallel technology based on OpenMP is advanced to dispose the promoter data. The promoter data are intercepted from the upstream area of five plants sequences. The five plants contain Arabidopsis Thaliana, Soybean, Alfalfa, Rice and Beans. The processing includes disturbing the sequences in random, calculating the P value, selecting the significant motifs from the frequent motifs, labeling the sequence number and so on. The serial algorithm and the parallel algorithm are compared in the implementation procedure. The experimental result shows that the OpenMP parallel technology can effectively improve the efficiency of the promoter data analysis and processing. It is a realistic significance to the vast amounts of biological information data processing.

关键词： Instruction sets parallel algorithms Algorithm design and analysis Gene expression Distributed databases Labeling

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

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

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International Conference on Data Storage and Data Engineering (DSDE)

作者： Shichuan Yu Yan Yang School of computer science and technology Heilongjiang University Harbin China Heilongjiang Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China

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.

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H-cluster: A novel efficient algorithm for data clustering in sensor networks

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Journal of Communications 2011年第2期6卷 168-178页

作者： Guo, Longjiang Ren, Meirui Li, Jinbao Liu, Yong Ai, Chunyu School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang Province Harbin 150080 China Computer Science Troy University Troy AL 36082 United States

This paper focuses on the problem of data clustering in wireless sensor networks (WSNs). The data time window is a landmark window, from the time WSN starts working up to the current time. The objective is to group sensory data generated by sensor nodes deployed in a two-dimensional physical space by the similarity of sensory data in the multi-dimensional sensory data space. To perform in-network data clustering efficiently, we propose HilbertMap, a novel dimensionality reduction technique based on the Hilbert Curves, to map a multi-dimensional data space to a two-dimensional physical space. Through this mapping, the communications for clustering mostly occur between geographically nearby sensor nodes. We have conducted simulation experiments on both real-world and synthetic datasets. Our results show that HilbertMap improves the communication efficiency while maintaining a good clustering quality. © 2011 ACADEMY PUBLISHER.

关键词： Sensor nodes

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Research on max ordinal in universal combinatorics coding based on GPU parallel computing

Research on max ordinal in universal combinatorics coding ba...

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International Conference on Measurement, Information and Control (MIC)

作者： Zhuo Zhang Jun Lu Ting Ting Gao Juan Mo Yu Liu Key Laboratory of Database and Parallel Computing Heilongjiang Province KLDPC HLJU College of CS HLJU Harbin China College of Computer Science and Technology Heilongiiang University

The parallel computing method of max ordinal number in universal combinatorics coding based on GPU is advanced in this paper. The core of universal combinatorics coding computing is ordinal number computing while the calculation of ordinal number depends on the value of max ordinal number. The calculation of max ordinal number is divided into two parts, multiplication and division of large numbers. This paper focus on the GPU parallel computing of the multiplication in max ordinal number calculation, the calculation speed of the multiplication part is increased substantially, and thus improves the efficiency of max ordinal number calculation.

关键词： Graphics processing units parallel processing Artificial intelligence Computational efficiency

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FasterBERT with Double Loss Function Based on Batch Data Preprocessing

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

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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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A probabilistic group reverse k-nearest-neighbor query in sensor networks 1

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7th China Conference of Wireless Sensor Networks, CWSN 2013

作者： Zhu, Jinghua Kan, Bin Liu, Yong Wang, Tong Pan, Liqiang Liu, Dan School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China College of Information and Communication Engineering Harbin Engineering University Harbin 150001 China School of Computer and Science Technology Harbin School of Technology Harbin 150001 China School of Computer Science Northeast Forestry University Harbin 150001 China

ISBN: (数字)9783642541742

ISBN: (纸本)9783642541735

Group Reverse k-Nearest-Neighbor Query (GRkNN) can be used to evaluate the influences of query objects and has wide applications. Traditional algorithms are not able to obtain satisfied results for uncertain sensor network data. A probabilistic threshold group reverse k nearest neighbor query (PT-GRkNN) is proposed in this paper. PT-GRkNN exploits filter-refine framework, geometric filter and probability filter are proposed to shrink the search space, the final query results can be obtained by refine phase. Experimental results show that the efficiency of PT-GRkNN is better than the existing methods. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Sensor networks

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DMSA: DYNAMIC MULTI-SCALE UNSUPERVISED SEMANTIC SEGMENTATION BASED ON ADAPTIVE AFFINITY

arXiv

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arXiv 2023年

作者： Yang, Kun Lu, Jun College of Computer Science and Technology Heilongjiang University 150080 Harbin China Jiaxiang Industrial Technology Research Institute of HLJU Jining Shandong Province China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-scale context information. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced, which can adaptively refine the initial pseudo labels after feature fusion to obtain high quality pseudo labels. Experiments show that the proposed DSMA framework is superior to the existing methods on the saliency dataset. On the COCO 80 dataset, the MIoU is improved by 2.0, and the accuracy is improved by 5.39. On the Pascal VOC 2012 Augmented dataset, the MIoU is improved by 4.9, and the accuracy is improved by 3.4. In addition, the convergence speed of the model is also greatly improved after the introduction of the PAR module. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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parallel algorithm for approximate string matching with K differences

Parallel algorithm for approximate string matching with K di...

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2013 IEEE 8th International Conference on Networking, Architecture and Storage, NAS 2013

作者： Guo, Longjiang Du, Shufang Ren, Meirui Liu, Yu Li, Jinbao He, Jing Tian, Ning Li, Keqin Heilongjiang University School of Computer Science and Technology Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang China Kennesaw State University Department of Computer Science Kennesaw GA 30144 United States State University of New York Department of Computer Science New Paltz NY 12561 United States

ISBN: (纸本)9780769550343

Approximate string matching using the k-difference technique has been widely applied to many fields such as pattern recognition and computational biology. Data dependency exists in the traditional sequential algorithm. Therefore, it is hard to design a parallel algorithm for approximate string matching with k differences. This paper presents a technique to eliminate data dependency. Based on this technique, this paper also presents a parallel algorithm which can calculate the elements in the same row of the edit distance matrix in parallel by eliminating data dependency. The algorithm has high parallelism, but requires synchronization. To validate the proposed algorithm, it is implemented on GPU and multiple-core CPUs. Moreover, the CUDA optimization techniques are also presented in the paper. Finally, experimental results show that, compared with the traditional sequential algorithm on CPU with twenty-four cores, the proposed parallel algorithm achieves speedup of 7-42 on GPU. © 2013 IEEE.

关键词： Graphics processing unit

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