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

作者： Cao Yi Cai Shimin Zhou Tao Complex Lab University of Electronic Science and Technology of China Chengdu China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

Adolescents' positive mental health is deeply associated with their growth. Identifying the factors that contribute to the positive mental health of junior and senior high school students is crucial for supporting youth development in the long run. Leveraging questionnaire data, this paper utilizes machine learning algorithms and fusion methods for feature selection to examine the factors that influence the onset of adolescent positive mental health. The influential features are ranked by importance. Then, machine learning algorithms in a heuristic form are employed to assess the predictive power of the fused features. The best f1-score of the classifiers reaches 0.844. It illustrates the effectiveness of feature filtering and provides valuable references for educational administrators to potential interventions.

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Course-Graph Discovery from Academic Performance Using Nonnegative LassoNet 18th

Course-Graph Discovery from Academic Performance Using Nonn...

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18th International Conference on Computer science and Education, ICCSE 2023

作者： Liu, Mengfei Wei, Shuangshuang Liu, Shuhui Shang, Xuequn Zhang, Yupei School of Computer Science Northwestern Polytechnical University Xi’an China MIIT Big Data Storage and Management Lab Xi’an China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9789819707362

This paper focuses on the problem of mining a course graph from students’ academic grades in formal education, which is an essential topic for artificial intelligence in education (AIED). However, most current methods often suffer from hardly understanding associations in practice. To this end, we formulate this problem into a feature selection schema that the proposed nonnegative LassoNet can solve. In the study case, we use the course scores of 4,577 records in the computer science department at our university. From the study results, our method achieves about 78% accuracy in score prediction with an acceptable error, which is better than traditional regression models with shrinkage. Based on the sparse self-expressive representation, we create a course map to show the associations behind the student’s academic performance, providing pieces of evidence for education studies and triggering exciting discoveries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Football player identification based on YOLOv5 backbone and SPD-Conv 8

Football player identification based on YOLOv5 backbone and ...

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8th International Conference on Electronic Technology and Information science, ICETIS 2023

作者： Liu, Jiwei Li, Yanchao Ning, Tao Song, Jinmiao Duan, Xiaodong SEAC key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116000 China Dalian Key Lab of Digital Technology for National Culture Dalian Minzu University Dalian116600 China College of Computer Science and Engineering Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510666535

With the rapid development of computer technology and Internet technology, the information age has come. The combination of computer technology and sports is one of the most popular research fields. This paper mainly completes the construction of football player number dataset and identification of football players through the jersey number. Firstly, the dataset is constructed based on player detection and number region detection. Then in the player identification task, we uses part of the backbone network of YOLOv5 model as the feature extraction module of the player identification network. Moreover, SPD-Conv module is added to improve the network recognition performance under the condition of small size target and low resolution. A series of experiments were also done to verify the performance of our proposed model. Finally, the recognition accuracy of our proposed model reached 92.75%. © 2023 SPIE.

关键词： Sports

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

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

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation

RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

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International Conference on data Engineering

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin 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 Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations; therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands" problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA.

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Revealing the significant role of band structure asymmetry on thermoelectric bipolar conduction

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Physical Review B 2025年第4期111卷 045203-045203页

作者： Jiamin Qiu Shizhen Zhi Peng Zhao Jian Wang Xiaojing Ma Sheng Ye Chenhao Lin Xuanhe Zhang Zuoxu Wu School of Materials Science and Engineering and Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen 518055 People's Republic of China School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen 518055 China

Bipolar conduction, which is widely observed in various materials, plays a deleterious role in the thermoelectric properties. Traditionally, the single-band model is often applied to understand the transport properties of thermoelectric materials. However, it ignores the contribution from minority carriers and is incapable of quantifying the bipolar conduction. Herein, a general and feasible approach for calculating all the temperature-dependent transport properties based on the two-band model has been developed. In addition to the reduced electron Fermi energy and reduced band-gap energy, band structure asymmetry has been identified to play a significant role in bipolar conduction. Importantly, the effect of band structure asymmetry on bipolar conduction has been highlighted by using the Mg3Bi2−xSbx alloy as a typical example. Our results demonstrate that the band structure asymmetry is of great significance to the materials' thermoelectric performance.

关键词： Energy gap

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training

arXiv

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arXiv 2024年

作者： Chen, Jiamin Li, Xuhong Xu, Yanwu Du, Mengnan Xiong, Haoyi Big Data Lab Baidu Inc Haidian Beijing100085 China HDMI Lab South China University of Technology Guangdong Guangzhou China Department of Data Science New Jersey Institute of Technology Dr Martin Luther King Jr Blvd NewarkNJ07102 United States

Medical image segmentation aims to identify and locate abnormal structures in medical images, such as chest radiographs, using deep neural networks. These networks require a large number of annotated images with fine-grained masks for the regions of interest, making pre-training strategies based on classification datasets essential for sample efficiency. Based on a large-scale medical image classification dataset, our work collects explanations from well-trained classifiers to generate pseudo labels of segmentation tasks. Specifically, we offer a case study on chest radiographs and train image classifiers on the CheXpert dataset to identify 14 pathological observations in radiology. We then use Integrated Gradients (IG) method to distill and boost the explanations obtained from the classifiers, generating massive diagnosis-oriented localization labels (DoLL). These DoLL-annotated images are used for pre-training the model before fine-tuning it for downstream segmentation tasks, including COVID-19 infectious areas, lungs, heart, and clavicles. Our method outperforms other baselines, showcasing significant advantages in model performance and training efficiency across various segmentation settings. © 2024, CC BY.

关键词： Image enhancement

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Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks

arXiv

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arXiv 2023年

作者： Qu, Xiran Shang, Xuequn Zhang, Yupei School of Computer Science Northwestern Polytechnical University Xi’an 710129 China Big data Storage and Management MIIT Lab Xi’an 710129 China

This paper studies the problem of CPRP, concept prerequisite relation prediction, which is a fundamental task in using AI for education. CPRP is usually formulated into a link-prediction task on a relationship graph of concepts and solved by training the graph neural network (GNN) model. However, current directed GNNs fail to manage graph isomorphism which refers to the invariance of non-isomorphic graphs, reducing the expressivity of resulting representations. We present a permutation-equivariant directed GNN model by introducing the Weisfeiler-Leman test into directed GNN learning. Our method is then used for CPRP and evaluated on three public datasets. The experimental results show that our model delivers better prediction performance than the state-of-the-art *** Codes 68T07 © 2023, CC BY-NC-ND.

关键词： Directed graphs

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