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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Guo, Hao Chen, Jingyu Yang, Xuan Jiao, Qingliang Liu, Ming Beijing Institute of Environmental Features Beijing China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Infrared and visible image fusion has been a hot issue in image fusion. However, the parameters and result image have many proposed. In this paper, a novel visible infrared image fusion algorithm based on double-density wavelet and thermal exchange optimization is proposed. The pre-processed infrared and visible images are decomposed into detail layers and background layers by double-density wavelet transform. Thermal exchange optimization is used to estimate the number of layers and weight thresholds of decomposition. Experimental shows that both visual effects and quantitative analysis of proposed algorithm can get the best results. © 2021 IEEE.

关键词： Image fusion

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Community Search in Spatial Uncertain Network

Community Search in Spatial Uncertain Network

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2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021

作者： Zhang, Wenqian Zhong, Yingli Yang, Yan School of Computer Science Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is called spatial uncertain network. In order to meet the existing needs, this paper studies the community search in the spatial uncertain network so that the searched communities can be relatively close in the geographical location and the relationship between each other is also very close. Based on the spatial uncertain graph, this paper proposes K-R-τ community, and proposes a linear algorithm and a two-dimensional algorithm for community search in the spatial uncertain network. A large number of experiments are carried out on several real datasets, and the results show that the algorithm proposed in this paper is effective and accurate. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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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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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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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

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

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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...

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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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Local partition subdivision algorithm for approximate nearest neighbor query 7

Local partition subdivision algorithm for approximate neares...

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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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An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index 6th

An Incremental Partitioning Graph Similarity Search Based on...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Li, Yuhang Yang, Yan Zhong, Yingli School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9789811579806

Graph similarity search is a common operation of graph database, and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Sensitivity loss training based implicit feedback

Sensitivity loss training based implicit feedback

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Kun Li Nan Wang Xinyu Liu Key Laboratory of Database and Parallel Computing of Heilongjiang Province School of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (纸本)9781665408790

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.

关键词： Training Learning systems Adaptation models Sensitivity Conferences Recommender systems

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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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