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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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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 Sleep Stage Classification Method via Combination of Time and Frequency Domain Features based on Single-Channel EEG

A Sleep Stage Classification Method via Combination of Time ...

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IEEE International Conference on Big Data and Cloud computing.(BdCloud)

作者： Caihong Zhao Wenpeng Neng College of Computer Science and Technology College of Electronic Engineering Heilongjiang University Heilongjiang Key Laboratory of Database and Parallel Computing Harbin China College of Computer Science and Technology Heilongjiang University Harbin China

Sleep staging is an important method to diagnose and treat insomnia, sleep apnea, and other sleep disorders. Compared with the multi-channel automatic sleep staging system, the single-channel EEG signal contains less information, and the traditional single analysis domain feature parameter extraction algorithm cannot meet the requirement of sleep stage classification accuracy. To solve this problem, we propose an automatic sleep staging method based on the combination of time-domain and frequency-domain features based on single-channel EEG signals. Empirical mode decomposition is used to decompose EEG signals in the time domain to obtain the decomposed signals at different time scales. Multiple local features are extracted from each decomposed signal. The frequency-domain features of EEG signals are obtained by using the frequency domain decomposition of EEG signals in various rhythms. The time-domain and frequency-domain decomposition features are combined into eigenvectors and selected for sleep staging. The experimental results show that the sleep staging method proposed in this paper with time-frequency domain features of single-channel EEG signals can approach the accuracy of sleep staging of multi-channel signals on the same data set, and superior to the sleep staging method with the same single-channel EEG signals.

关键词： Deep learning Time-frequency analysis Empirical mode decomposition Sleep Frequency-domain analysis Feature extraction Electroencephalography

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DMSA: Dynamic Multi-Scale Unsupervised Semantic Segmentation Based On Adaptive Affinity

DMSA: Dynamic Multi-Scale Unsupervised Semantic Segmentation...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kun Yang Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China Jiaxiang Industrial Technology Research Institute of HLJU Jining 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.

关键词： Training Representation learning Adaptation models Semantic segmentation Semantics Signal processing Transformers

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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

关键词： Semantic Segmentation

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An implementation of the QR iterations for finding eigenvalues of matrices with CUDA on GPU

An implementation of the QR iterations for finding eigenvalu...

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International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Meirui Meihui Weiping Zhang Tao Wang Ning Tian Jinbao Li Longjiang Guo School of Computer Science and Technology Heilongjiang University Harbin 150080 China Harbin Research Institute of Electrical Instrumentation 150028 China School of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781479925667

Based on the principle of QR loop iterations, this paper implements a parallel algorithm based on the hardware of GPU (Graphic Process Unit) by using routines from CUDA (Computer Unified Device Architecture) to find the eigenvalues of general matrices. CPU and GPU computing.card form a client-server computing.framework. Here, CPU can be regarded as a client and GPU card is considered as a computing.server. For the experiment environment, this paper chooses a GPU card with the model of NVIDIA GeForce GTX460 as a server side and a CPU with the model of Intel Core i5-760 quad-core as a client side. Win7 64-bit is selected as the operating system. The parallel implementation consists of two parts:PA H and PA QR. PA H is a procedure that transforms a matrix A into the Hessenberg matrix B. PA QR is the actual parallel algorithm of the QR iterations that is imposed on the Hessenberg matrix B for finding eigenvalues of the general matrix A. The speedup ratio of the proposed algorithm is jarless when the number of iterations becomes greater. The experimental results show that the parallel implementation with CUDA on GPU only makes use of less running time than traditional sequential algorithms. The speedup ratio of PA H is between 1.79 and 7.81. The speedup ratio of PA QR is between 3.24 and 118.9. Especially, when the order of general matrix equals 8192, the amount of iterations becomes 10000. The speedup ratio of the PA-H and the PA QR can run up to 7.81 and 118.9 respectively.

关键词： Graphics processing units Eigenvalues and eigenfunctions Hardware Kernel Instruction sets

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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 Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift

A Bidirectional Representation Recommendation Based On Relev...

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IEEE International Conference on Web Services (ICWS)

作者： Nan Wang Kun Li Xuefeng Jiang Jinbao Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China Shandong Artificial Intelligence Institute School of Mathematics and Statistics Qilu University of Technology Jinan China

The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users’ real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user’s historical interaction and the user’s new interest very weak. Meanwhile, the information contained in user’s sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user’s new interest from neighbors’ sessions. Then, we integrate the relevance information into user’s history sessions, which can effectively enhance the correlation between user’s history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users’ sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://***/LucasZen/paper_code.

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Cross-level Relational Graph Contrastive Learning for Bundle Recommendation

Cross-level Relational Graph Contrastive Learning for Bundle...

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IEEE International Conference on Web Services (ICWS)

作者： Nan Wang Jiabao Sun Jinbao Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang University Harbin China Shandong Artificial Intelligence Institute School of Mathematics and Statistics Qilu University of Technology Jinan China

Different from the traditional recommendation system which regards a single item as an atomic unit, the purpose of bundle recommendation is to recommend a set of items to users. In recent years, some work often uses the model based on GNN to solve the bundle recommendation problem. However, compared with the traditional recommendation models, the GNN-based bundle recommendation has a more serious data sparse problem, which significantly reduces the performance of the model. Meanwhile, due to the lack of modeling ability of complex interactive information and dependent information, it is still far from enough to learn the representation of users and bundles. In view of the advantages of contrast learning in mining its own supervisory signals, we propose a model named Cross-level relational graph Contrastive Learning for Bundle Recommendation (CL 2 BRec). Different from previous contrastive learning methods, our method learns representations of users and bundles from multiple relational graphs of different levels through a crossview contrastive learning paradigm. Specifically, we regard the user-item/bundle graph as a local interaction relation view, the bundle-item graph as a local dependency relation view, and the user-bundle-item tripartite graph as a global structure view to build a cross-level bundle recommendation model. Extensive experiments conducted on two benchmark datasets demonstrate that CL 2 BRec outperforms the state-of-the-art methods by over 12.56% and 8.6%, respectively.

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NKMH: A Neural Efficient Recommendation Based on Neighborhood key Information Aggregation of Modified Hawkes

NKMH: A Neural Efficient Recommendation Based on Neighborhoo...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xin Xu Nan Wang Hu Jin Yang Liu Kun Li Key Laboratory of Database and Parallel Computing of Heilongjiang Province School of Computer Science and Technology Heilongjiang University Harbin China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd School of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The rapid development of neural networks has con-tributed to the increasing maturity of recommendation systems. However, deep neural networks have poor interpretability for models and do not show strong advantages for sparse data and noisy data. Recently, Hawkes process has become more and more focused for its good interpretability with probabilistic models. Based on this, we proposes A Neural Efficient Recommendation Model Based on Neighborhood key Information Aggregation of Modified Hawkes(NKMH). The model utilizes a neural network and designs three modules to jointly fit the modified Hawkes process. It not only inherits the high interpretability of Hawkes, but also effectively solves the problem of poor prediction ability of the Hawkes process. Besides, we present a novel key information search strategy(KISS), which can effectively remove the noise in a session and alleviate the sparsity of the data to some extent. Extensive experiments on two datasets show that the NKMH model outperforms many current popular models.

关键词： Deep learning Conferences Neural networks Predictive models Search problems Probabilistic logic Software

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