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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Xu, Baolei Li, Xiaojie Shi, Canghong Sichuan University of Arts and Sciences Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province Dazhou China Chengdu University of Information Technology School of Computer Science Chengdu China Xihua University School of Computer Science and Software Engineering Chengdu China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Traditional neural fiber classification methods are generally difficult to balance classification accuracy and algorithm complexity, and there is a problem of relying on manual intervention, mainly including manual drawing of region of interest (ROI) and manual screening of target fiber bundles, resulting in uncertainty of classification results and large data error. To reduce manual intervention, balance accuracy and algorithm complexity, a simple and effective BP neural network model combined with fast image processing algorithm is proposed. Based on the neural fiber map information, this method uses the fiber coordinate information to train the classification network structure and realize the online fiber classification. While improving the classification accuracy, it also reduces the complexity of the algorithm and does not need to know the distribution of fibers in advance. Compared with several typical classification algorithms, BP neural network model has strong robustness and can effectively prevent over-fitting. The experimental results show that the method performs well in several classifiers, and the classification accuracy is optimal. The experimental training accuracy is 0.9824, and the test accuracy is 0.9774. © 2022 IEEE.

关键词： Neural networks

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Fusing Local and Global Mobility Patterns for Trajectory Recovery 28th

Fusing Local and Global Mobility Patterns for Trajectory R...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Deng, Liwei Zhao, Yan Sun, Hao Yang, Changjie Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Peking University Beijing China Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The prevalence of various mobile devices translates into a proliferation of trajectory data that enables a broad range of applications such as POI recommendation, tour recommendation, urban transportation planning, and the next location prediction. However, trajectory data in real-world is often sparse, noisy and incomplete. To make the subsequent analysis more reliable, trajectory recovery is introduced as a pre-processing step and attracts increasing attention recently. In this paper, we propose a neural attention model based on graph convolutional networks, called TRILL, to enhance the accuracy of trajectory recovery. In particular, to capture global mobility patterns that reflect inherent spatio-temporal regularity in human mobilities, we construct a directed global location transition graph and model the mobility patterns of all the trajectories at point level using graph convolutional networks. Then, a self-attention layer and a window-based cross-attention layer are sequentially adopted to refine the representations of missing locations by considering intra-trajectory and inter-trajectory information, respectively. Meanwhile, an information aggregation layer is designed to leverage all the historical information. We conduct extensive experiments using real trajectory data, which verifies the superior performance of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

arXiv

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arXiv 2023年

作者： Cui, Chenhang Ren, Yazhou Pu, Jingyu Li, Jiawei Pu, Xiaorong Wu, Tianyi Shi, Yutao He, Lifang School of Computer Science and Engineering Shenzhen Institute for Advanced Study China School of Mathematical Sciences School of Information and Communication Engineering} University of Electronic Science and Technology of China China Department of Computer Science and Engineering Lehigh University Bethlehem United States

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC. © 2023, CC BY-NC-ND.

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GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data

arXiv

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arXiv 2024年

作者： Zhao, Haochen Meng, Hui Yang, Deqian Xie, Xiaozheng Wu, Xiaoze Li, Qingfeng Niu, Jianwei School of Computer Science and Engineering Beihang University Beijing China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China Institute of Software Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Beihang University Beijing China

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation. Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled data. To address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs. A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same class. On this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated pseudo-labels. Furthermore, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-theart performance. The source code with our proposed model are available at https://***/kimjisoo12/GuidedNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Communication-Efficient Distributed Stochastic Gradient Descent with Pooling Operator

SSRN

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SSRN 2023年

作者： Cai, Zhengao Chen, Aiguo Luo, Yi Li, Jiahao Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China

Training deep neural networks on large datasets can be accelerated by distributing the computations across multiple worker nodes. The distributed stochastic gradient descent (D-SGD) algorithm combined with gradient sparsification is a typical algorithm for this distributed training model, which guarantees better convergence and can significantly reduce communication bandwidth. However, we find that the existing gradient sparsification algorithms ignore the local correlation between gradient values under certain conditions, which can incur a loss of accuracy since the update region of the global parameter will stay in a particular area for a long epoch. Based on this observation, we introduced the pooling operator to solve this problem and combined it with the error-feedback method to design a new communication-efficient distributed gradient descent algorithm. We prove our algorithm converges at the same rate as vanilla SGD when equipped with error feedback. Experiments on CNN, an LSTM, and a ResNet model demonstrated that our algorithm could compress the number of uploaded gradient bits by two or three orders of magnitude while guaranteeing the high accuracy of the model. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Twenty years of e-learning in health science: A bibliometric

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Procedia computer science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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G&L: An Attention-based Model for Improving Prefetching in Solid-state Drives

G&L: An Attention-based Model for Improving Prefetching in S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chenxu Yang Xin Man Jie Shao Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Computer Science & Engineering University of Electronic Science and Technology of China Chengdu China

Solid-state drives (SSDs) have become the storage device for many personal computers and enterprise servers. However, the access speed of SSDs is still a bottleneck for computer systems. With prefetching, SSDs are able to predict future requests before moving data to a faster storage unit. Nevertheless, the interleaved access flow of applications poses challenges for existing prefetchers. We propose a model named G&L consisting of two components, namely a generative pre-training (GPT) prefetcher and a logical block address (LBA)-I/O size table. The GPT prefetcher applies a decoder-only framework with a self-attention mechanism to forecast the next LBA. The LBA-I/O size table maps LBAs to their most frequently appeared I/Osize for prediction. To evaluate the entire performance of the G&L model, we propose an algorithm to simulate the buffer when the I/O size changes dynamically. Experiments show that the accuracy, coverage, and f1 score of the GPT prefetcher surpass the baselines in most cases. The prediction accuracy of the LBA-I/O size table exceeds the baseline on all datasets. The G&L model's prefetching accuracy, coverage, and f1 score also outperform the baseline considering variable I/O size.

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Unsupervised Multiplex Graph Learning with Complementary and Consistent Information

arXiv

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arXiv 2023年

作者： Peng, Liang Wang, Xin Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs. However, previous methods usually overlook the issues in practical applications, i.e., the out-of-sample issue and the noise issue. To address the above issues, in this paper, we propose an effective and efficient UMGL method to explore both complementary and consistent information. To do this, our method employs multiple MLP encoders rather than graph convolutional network (GCN) to conduct representation learning with two constraints, i.e., preserving the local graph structure among nodes to handle the out-of-sample issue, and maximizing the correlation of multiple node representations to handle the noise issue. Comprehensive experiments demonstrate that our proposed method achieves superior effectiveness and efficiency over the comparison methods and effectively tackles those two issues. Code is available at https://***/LarryUESTC/CoCoMG. © 2023, CC BY.

关键词： Machine learning

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Mask R-CNN Network for Back Recognition on Adam's Bending Images

Mask R-CNN Network for Back Recognition on Adam's Bending Im...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Pu Yuxin Cai Hongbin Fan Jipeng Zheng Xin School of Computer Science and Engineering University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu Chengdian Goldisc HealthData Technology Co. Ltd School of Artificial Intelligence and Big data Sichuan University of Arts and Science

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Back recognition is critical for adolescent idiopathic scoliosis screening. Existing methods mainly rely on manual work which is time-consuming and laborious and may introduce errors and mismatching impacting the accuracy of subsequent screening outcomes. In this work, we propose to utilize the Mask R-CNN network as the framework for auto-extraction of the back region on Adam's bending images. However, due to the inherent limit receptive fields, initial Mask R-CNN can hardly learn long-range dependencies which are crucial for object detection and segmentation. To this end, we introduce the explicit visual center module into Mask R-CNN. In the EVC module, a lightweight MLP is devised to extract global long-range dependencies. To compensate for MLP's lack of ability to capture features of local corner regions, a learnable visual center is incorporated. By aggregating global long-range dependencies and discriminative feature representation of local key regions, our Mask R-CNN can achieve better performance on Adam's bending images. Moreover, we collect and annotate an Adam's bending image dataset for back recognition. Experimental results on our annotated dataset demonstrate that our improved Mask R-CNN considerably improves the average precision in detection and segmentation of Mask R-CNN from 78.6% to 83.6% and 85.8% to 87.0%, respectively.

关键词： Training Image segmentation Visualization Image recognition Scoliosis Training data Object detection Bending Back Media

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