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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Contrastive graph poisson networks: semi-supervised learning with extremely limited labels 21

Contrastive graph poisson networks: semi-supervised learning...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Sheng Wan Yibing Zhan Liu Liu Baosheng Yu Shirui Pan Chen Gong PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology and JD Explore Academy JD Explore Academy The University of Sydney Department of Data Science and AI Faculty of IT Monash University

ISBN: (纸本)9781713845393

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most existing GNN models require sufficient labeled data for effective network training. Their performance can be seriously degraded when labels are extremely limited. To address this issue, we propose a new framework termed Contrastive Graph Poisson Networks (CGPN) for node classification under extremely limited labeled data. Specifically, our CGPN derives from variational inference; integrates a newly designed Graph Poisson Network (GPN) to effectively propagate the limited labels to the entire graph and a normal GNN, such as Graph Attention Network, that fexibly guides the propagation of GPN; applies a contrastive objective to further exploit the supervision information from the learning process of GPN and GNN models. Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph. We conducted extensive experiments on different types of datasets to demonstrate the superiority of CGPN.

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG

arXiv

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arXiv 2024年

作者： Chen, Jiarong Wu, Wanqing Liu, Tong Hong, Shenda National Institute of Health Data Science Peking University Beijing100191 China School of Biomedical Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Department of Micro/Nano Electronics and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin300211 China Institute of Medical Technology Health Science Center of Peking University Beijing100191 China Institute for Artificial Intelligence Peking University Beijing100871 China

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410. © 2024, CC BY.

关键词： Mean square error

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An improved bat algorithm based on hybrid with ant lion optimizer 13th

An improved bat algorithm based on hybrid with ant lion opti...

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13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Dao, Thi-Kien Chu, Shu-Chuan Pan, Jeng-Shyang Nguyen, Trong-The Ngo, Truong-Giang Nguyen, Trinh-Dong Tran, Huu-Trung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

ISBN: (纸本)9789811533075

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

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Identification of Causal Relationship between Amyloid-β Accumulation and Alzheimer’s Disease Progression via Counterfactual Inference

arXiv

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arXiv 2023年

作者： Dai, Haixing Hu, Mengxuan Li, Qing Zhang, Lu Zhao, Lin Zhu, Dajiang Diez, Ibai Sepulcre, Jorge Zhang, Fan Gao, Xingyu Liu, Manhua Li, Quanzheng Li, Sheng Liu, Tianming Li, Xiang School of Computing University of Georgia AthensGA United States State Key Lab of Cognitive Neuroscience and Learning Beijing Normal University Beijing100875 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02114 United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston02115 United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China School of Data Science The University of Virginia Charlottesville22903 United States

Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-β in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-β accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-β levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-β accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care. © 2023, CC BY.

关键词： Neurodegenerative diseases

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3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training

arXiv

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arXiv 2021年

作者： Deng, Yicheng Sun, Cheng Sun, Yongqi Zhu, Jiahui Key Lab of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Information Science and Electrical Engineering Kyushu University Fukuoka8190395 Japan

3D human pose estimation from a single image is still a challenging problem despite the large amount of work that has been performed in this field. Generally, most methods directly use neural networks and ignore certain constraints (e.g., reprojection constraints, joint angle, and bone length constraints). While a few methods consider these constraints but train the network separately, they cannot effectively solve the depth ambiguity problem. In this paper, we propose a GAN-based model for 3D human pose estimation, in which a reprojection network is employed to learn the mapping of the distribution from 3D poses to 2D poses, and a discriminator is employed for 2D-3D consistency discrimination. We adopt a novel strategy to synchronously train the generator, the reprojection network and the discriminator. Furthermore, inspired by the typical kinematic chain space (KCS) matrix, we introduce a weighted KCS matrix and take it as one of the discriminator’s inputs to impose joint angle and bone length constraints. The experimental results on Human3.6M show that our method significantly outperforms state-of-the-art methods in most cases. © 2021, CC0.

关键词： Generative adversarial networks

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Early improvement of psychopathological symptoms with add-on repetitive transcranial magnetic stimulation treatment as the key contributor to improving remission in schizophrenia

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Journal of psychiatric research 2025年 188卷 64-73页

作者： Qiang Hu Xiong Jiao XiaoChen Tang YeGang Hu LiHua Xu YanYan Wei HuiRu Cui YingYing Tang Tao Chen HaiChun Liu ZiXuan Wang Jin Gao LingYun Zeng ZhengHui Yi ChunBo Li JiJun Wang TianHong Zhang Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Neuromodulation Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai 200030 China Department of Psychiatry ZhenJiang Mental Health Center Zhenjiang China. Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Neuromodulation Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai 200030 China. Big Data Research Lab University of Waterloo Ontario Canada Labor and Worklife Program Harvard University Massachusetts United States. Department of Automation Shanghai Jiao Tong University Shanghai 200240 China. Shanghai Xinlianxin Psychological Counseling Center Shanghai China. Department of Clinical Psychology Qilu Hospital (Qingdao) Cheeloo College of Medicine Shandong University Qingdao Shandong China. Department of Psychiatric Rehabilitation Shenzhen Kangning Hospital ShenZhen GuangDong China. Nantong Fourth People's Hospital and Nantong Brain Hospital NanTong Jiangsu 226000 China. Electronic address: jijunwang27@***. Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Neuromodulation Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai 200030 China. Electronic address: zhang_tianhong@***.

Early improvement with antipsychotic treatment is thought to be important in patients with first-episode schizophrenia(FES); however, no study has examined the contribution of add-on repetitive transcranial magnetic stimulation (rTMS). This study is aimed to investigate the efficacy and early improvement of rTMS in FES and to identify the relationship between initial symptom reduction and late response. A total of 105 patients with FES were randomly assigned to two groups: active rTMS group (n = 60) and sham rTMS group (n = 45), with existing olanzapine (10-20 mg/day) combined with 20 sessions of 1Hz active/sham rTMS over the right orbitofrontal cortex (20min/session, 5times/week). The Positive and Negative Syndrome Scale(PANSS) was used as the main tool to evaluate the clinical symptoms of patients at baseline, week-2, and week-8. Early improvement was defined as an improvement in PANSS total score at week 2. Significant improvements in clinical symptoms(PANSS total, positive[PANSS-P], negative[PANSS-N], and general psychopathological symptoms[PANSS-G]) were found in both the rTMS and sham groups. Moreover, a repeated-measures general linear model showed the decrease from baseline in PANSS-total(group × visit, F = 9.906, p < 0.001), PANSS-N(group × visit, F = 4.108, p = 0.020), and PANSS-G(group × visit, F = 12.095, p < 0.001) at week 8 were significant in the active rTMS group but not in the sham group. The active rTMS group had significant early improvements in PANSS-total(z = 3.348, p = 0.001), PANSS-N(z = 2.222, p = 0.026) and PANSS-G(z = 2.624, p = 0.009), compared with the sham group. Early improvements were significantly related to the 8-week improvements. Add-on rTMS treatment as a supplement to antipsychotics could potentially increase the efficacy of early improvements and improve the ultimate remission in patients with FES. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Register Center (http://***/) IDENTIFIER: ChiCTR2000041106. REGIST

关键词： Brain stimulation Early improvement Orbitofrontal cortex Psychosis Schizophrenia

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Collaborative Edge Service Placement for Maximizing QoS with Distributed data Cleaning

Collaborative Edge Service Placement for Maximizing QoS with...

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International Workshop on Quality of Service

作者： Yuzhu Liang Wenhua Wang Xi Zheng Qin Liu Liang Wang Tian Wang School of Artificial Intelligence Beijing Normal University Beijing China School of Computing Macquarie University NSW Australia College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Northwestern Polytechnical University Xi'an China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

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Joint Event Extraction Model based on Multi-feature Fusion 8

Joint Event Extraction Model based on Multi-feature Fusion

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Wang, Shuo Rao, Yuan Fan, Xiaobing Qi, Jiangnan Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Precessiong School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiaotong University Xi’an China

Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and use the traditional language representation model for word embedding. It is impossible to model the polysemy and the features is very simple, besides, the pipeline model is also easy to generate cascading errors. Aiming at the above problems, we propose a joint event extraction model, which combines part-of-speech features, entity features and word representation of BERT model. Because of the BERT can't capture local features, we use multi-CNN to capture the local features of different scales. and further use BiLSTM to capture context features to assist joint event extraction. Experimental results show that the precision, recall and F1 of the method on the CEC dataset are 88.83%, 90.91% and 89.86%, respectively, which are 1.61%, 3.6% and 2.6% higher than the PLMEE model. © 2020 The Authors. Published by Elsevier B.V.

关键词： Pipelines

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