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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Hao Yun Wei Ji Rui Yin Key Laboratory of Database and Parallel Computing of Heilongjiang Province Laboratory of Intelligent Communications and Multimedia Information Processing ShanDong University Qingdao China School of Information Science and Engineering ShanDong University Qingdao China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

In this paper, a radio-over-fiber distributed antenna system (ROF-DAS) and application of wireless positioning are proposed. The modified Medium Access Control (MAC) frame, optical switch array, ROF network based on star topology and Reflective Semiconductor Optical Amplifier (RSOA) are employed to design the system. Based on the structure, wireless positioning could be achieved by Time Division Multiplexing-Time Difference of Arrival (TDM-TDOA) technology combined with Chan assisted Taylor algorithm. The quality of signal transmission is evaluated by Q Factor, and the positioning accuracy is performed in terms of the antennas amount using the positioning algorithm.

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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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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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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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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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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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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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