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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zheng, Fang Zhou, Hang Li, Guoliang Ni, Fuchuan Huazhong Agricultural University Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Key Laboratory of Smart Farming Technology for Agricultural Animals Ministry of Agriculture and Rural Affairs College of Informatics Wuhan China Huazhong Agricultural University College of Informatics Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China

ISBN: (纸本)9798350386226

While convolutional neural network(CNN) have historically achieved significant success in 3D medical image segmentation, recent advancements in Vision Transformer (ViT) have led to increased popularity of Transformer architectures in this field. Leveraging the global information extraction capabilities of Transformers, we propose a novel parallel structure combining CNN and Transformer for multimodal brain tumor segmentation, named PTCNet. We introduce two key improvements to enhance multimodal segmentation:1) Introducing a novel channel attention module that replaces spatial self-attention in the Transformer to improve cross-modal information extraction.2) Enhancing the reversible feature extraction module based on residual structures to better detect small lesions. We evaluated our method on the BraTS 2023 brain metastasis dataset, achieving an average DSC of 61.42%, which is 1.12% higher than the second place. The innovative structure and improved modules of PTCNet significantly enhance performance in multimodal brain tumor segmentation tasks. Code and models are available at https://***/naiqizh/PTCNet. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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Leverage Learning Behaviour data for Students' Learning Performance Prediction and Influence Factor Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2422-2433页

作者： Ni, Qin Zhu, Yanjin Zhang, Lingying Lu, Xiaochen Zhang, Lei Shanghai International Studies University Key Laboratory of Multilingual Education with AI Shanghai201620 China Donghua University College of Information Science and Technology Shanghai201620 China Donghua University Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai201620 China Shanghai Normal University Shanghai Engineering Research Center of Intelligent Education and Bigdata and Lab for Educational Big Data and Policymaking Shanghai200234 China

Online education has become increasingly significant for university students and faculty, especially in the context of the modern remote education landscape. However, the inherent space-time separation in online education can create communication delays between teachers and students, making it challenging to monitor students' behaviors effectively. In an effort to understand the connection between students' online engagement and their learning performance, data annotation has been implemented to address the issue of accurately representing students' learning behaviors. Taking the online teaching data of Shanghai Normal University platform as the research object, the primary online education problem is explored through data mining, which includes correlation analysis, Gini importance ranking, and principal component analysis (PCA). Then, constructing the learning performance prediction model using random forest (RF) based on PCA by comparing various machine learning algorithms. As a consequence, the most influential online learning behaviors are course duration time, document learning time, test average score, and video completion rate. The overall classification accuracy of the learning performance prediction model is 87.45%, and the highest prediction accuracy for a single category is 96.52%. © 2020 IEEE.

关键词： Students

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data Processing Model of Students' Evaluation of Teaching Based on Outlier Detection 5

Data Processing Model of Students' Evaluation of Teaching Ba...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Qu, Ying Guo, Fei Hebei University of Science and Technology School of Economics and Management Data Science and Intelligent Computing Research Center Hebei China

ISBN: (纸本)9781665433181

The problem of low credibility of students in teaching evaluation is caused by ignoring non-teaching factors such as students' emotions, class culture, and course nature in the existing data of university students' teaching evaluation. A data processing model of students' teaching evaluation is constructed based on outlier detection. The outliers of the original data are screened and optimized based on students' emotional differences. Based on the analysis of non-teaching factors such as class culture and curriculum characteristics, the abnormal data of students' evaluation of teaching are processed. This improves the credibility of students' evaluation of teaching data to a certain extent. For the same sample data, the optimized data processing model of students' evaluation of teaching shows realistic evaluation results. The guidance of students' evaluation of teaching needs to be strengthened based on the original intention of teaching quality evaluation. It is suggested that evaluation models are scientifically selected, and the evaluation results are rationally used. © 2022 IEEE.

关键词： Students

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Lightweight Human Pose Estimation Based on Multi-Attention Mechanism

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Lin, Xiao Lu, Meichen Gao, Mufeng Li, Yan Institute of Artificial Intelligence on Education Research College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal University Shanghai200234 China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234 China School of Physical Education Shanghai Normal University Shanghai200234 China

Human pose estimation has received much attention from the research community because of its wide range of applications. However, current research for pose estimation is usually complex and computationally intensive, especially the feature loss problems in the feature fusion process. To address the above problems, we propose a lightweight human pose estimation network based on multi-attention mechanism (LMANet). In our method, network parameters can be significantly reduced by lightweighting the bottleneck blocks with depth-wise separable convolution on the high-resolution networks. After that, we also introduce a multi-attention mechanism to improve the model prediction accuracy, and the channel attention module is added in the initial stage of the network to enhance the local cross-channel information interaction. More importantly, we inject spatial cross-awareness module in the multi-scale feature fusion stage to reduce the spatial information loss during feature extraction. Extensive experiments on COCO2017 dataset and MPII dataset show that LMANet can guarantee a higher prediction accuracy with fewer network parameters and computational effort. Compared with the high-resolution network HRNet, the number of parameters and the computational complexity of the network are reduced by 67% and 73%, respectively. © 2023, Shanghai Jiao Tong University.

关键词： Complex networks

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding 40

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Cheng, Feng Xiao, Jiang Liu, Cunyang Zhang, Shijie Zhou, Yifan Li, Bo Li, Baochun Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (纸本)9798350317152

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics -silent assets -and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions. © 2024 IEEE.

关键词： Blockchain

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LSGraph: A Locality-centric High-performance Streaming Graph Engine 24

LSGraph: A Locality-centric High-performance Streaming Graph...

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19th European Conference on Computer Systems, EuroSys 2024

作者： Qi, Hao Wu, Yiyang He, Ligang Zhang, Yu Luo, Kang Cai, Minzhi Jin, Hai Zhang, Zhan Zhao, 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 China Department of Computer Science University of Warwick United Kingdom Zhejiang Lab China

ISBN: (纸本)9798400704376

Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high computation complexity for streaming graph analytics, or need high overhead to search and move graph data to ensure ordered neighbors during streaming graph update. This paper presents a novel locality-centric streaming graph engine, called LSGraph, to enable efficient both graph analytics and graph update. The main novelty of this engine is a differentiated hierarchical indexed streaming graph representation approach to achieve efficient data search and movement for graph update and also maintain data locality and ordered neighbors for efficient graph analytics simultaneously. Besides, a locality-aware streaming graph data update mechanism is also proposed to efficiently regulate the distance of data movement, minimizing the overhead of memory access during graph update. We have implemented LSGraph and conducted a systematic evaluation on both real-world and synthetic datasets. Compared with three cutting-edge streaming graph engines, i.e., Terrace, Aspen, and PaC-tree, LSGraph achieves 2.98×-81.08×, 1.46×-12.56×, and 1.26×-10.31× speedups during graph update, while obtaining 1.02×-4.28×, 1.58×-3.55×, and 1.20×-2.72× speedups during graph analytics, respectively. © 2024 Owner/Author.

关键词： Engines

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua Science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUdatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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