检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wu, Yucheng Han, Xiao Wang, Leye Ye, Han-Jia Key Lab of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University Nanjing China

Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping, to generate augmented graphs. However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process. We argue that incorporating an awareness of cohesive subgraphs during the graph augmentation and learning processes has the potential to enhance GCL performance. To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. In particular, CTAug comprises two specialized modules: topology augmentation enhancement and graph learning enhancement. The former module generates augmented graphs that carefully preserve cohesion properties, while the latter module bolsters the graph encoder’s ability to discern subgraph patterns. Theoretical analysis shows that CTAug can strictly improve existing GCL mechanisms. Empirical experiments verify that CTAug can achieve state-of-the-art performance for graph representation learning, especially for graphs with high degrees. The code is available at https://***/10.5281/zenodo.10594093, or https://***/wuyucheng2002/CTAug. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Multi-task MIML learning for pre-course student performance prediction

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Frontiers of computer science 2020年第5期14卷 113-121页

作者： Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual *** this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course ***,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the *** this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its *** better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)***,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified *** experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning

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Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning

arXiv

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arXiv 2023年

作者： Duan, Yue Zhao, Zhen Qi, Lei Zhou, Luping Wang, Lei Shi, Yinghuan National Key Laboratory for Novel Software Technology Nanjing University China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Computer Science and Engineering Southeast University China School of Computing and Information Technology University of Wollongong Australia

While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://***/NJUyued/SoC4SS-FGVC. Copyright © 2023, The Authors. All rights reserved.

关键词： Shrinkage

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SG-GS: Topology-aware Human Avatars with Semantically-guided Gaussian Splatting

arXiv

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arXiv 2024年

作者： Zhao, Haoyu Yang, Chen Wang, Hao Zhao, Xingyue Shen, Wei MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China School of Computer Science Wuhan University China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology China School of Software Engineering Xi’an Jiao Tong University China

Reconstructing photo-realistic and topology-aware animatable human avatars from monocular videos remains challenging in computer vision and graphics. Recently, methods using 3D Gaussians to represent the human body have emerged, offering faster optimization and real-time rendering. However, due to ignoring the crucial role of human body semantic information which represents the explicit topological and intrinsic structure within human body, they fail to achieve fine-detail reconstruction of human avatars. To address this issue, we propose SG-GS, which uses semantics-embedded 3D Gaussians, skeleton-driven rigid deformation, and non-rigid cloth dynamics deformation to create photo-realistic human avatars. We then design a Semantic Human-Body Annotator (SHA) which utilizes SMPL’s semantic prior for efficient body part semantic labeling. The generated labels are used to guide the optimization of semantic attributes of Gaussian. To capture the explicit topological structure of the human body, we employ a 3D network that integrates both topological and geometric associations for human avatar deformation. We further implement three key strategies to enhance the semantic accuracy of 3D Gaussians and rendering quality: semantic projection with 2D regularization, semantic-guided density regularization and semantic-aware regularization with neighborhood consistency. Extensive experiments demonstrate that SG-GS achieves state-of-the-art geometry and appearance reconstruction performance. Our project is at https://***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Facility Layout Optimization Using Hybrid Grey Wolf Optimizer

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computer engineering and Applications 2025年第11期61卷 351-363页

作者： Song, Qiang School of Computer Science and Software Zhaoqing University Guangdong Zhaoqing526061 China School of Information and Communication Engineering Wuhan University of Technology Wuhan430070 China

This paper investigates a complicated facility layout optimization problem with considerations of the factors such as entry/exit locations, the facility orientation and the safety distance. The problem domain is stated and the mathematical model is developed to minimize the total logistics intension. Then, a hybrid grey wolf optimizer (HGWO) is proposed for solution generation. By virtue of the problem nature, a novel encoding and decoding method is designed to adjust grey wolf optimizer (GWO) to the considered problem. Meanwhile, an evaluation function is proposed to assist the population evolution. To improve the solution performance at initialization stage, the Fuch chaotic mapping is used to create initial solutions. In addition, a hybrid solution update mechanism is defined by combining the optimal solution guidance and neighborhood learning strategies to balance the global search and local mining capabilities. Finally, experiments are carried out to address numerical optimization problems, such as the pressure vessel problem and the facility layout optimization instances. The simulation results validate the performance of the proposed HGWO. © 2025 Journal of computer engineering and Applications Beijing Co., Ltd.;science Press. All rights reserved.

关键词： Mapping

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Exploration and practice of python programming teaching model based on MBO 15

Exploration and practice of python programming teaching mode...

引用

15th International Conference on computer science and Education, ICCSE 2020

作者： Wei, Dongmei Wang, Xiuhua Wang, Ying Xihua University School of Computer Science and Software Engineering Chengdu China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172675

Cultivating students' innovative ability has been playing an important role in the process of program design courses in Colleges and Universities. This paper focuses on presenting practical experience of the programming and designing patterns in teaching Python programming. Combining MBO(Management By Objective) with teaching, an optimized teaching model of Python Programming based on MBO. The model consists of four parts: Knowledge objectives, Technology ability objectives, Thinking Developed objectives, extended ability objectives. Following the four main goals, this paper introduces relevant teaching practices based on each objective. Applying the model , the quality of planned teaching and learning can be improved. Because setting clear goals could make it easy to formulate plans, tasks, teaching contents, track learning effects and test learning consequences. According to the model, the course teaching practice will highlight the importance of teaching Program concretely and effectively. Most significantly, teachers could train students to gradually develop computational thinking and apply it in subsequent courses. © 2020 IEEE.

关键词： Python

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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Profiling and Evolution of Intellectual Property

arXiv

引用

arXiv 2022年

作者： Yu, Bowen Shao, Yingxia Li, Ang School of Computer Science National Pilot School of Software Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing100876 China

In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding. However, the increase in the number and category of information data will also increase the cost of information acquisition. For technology-based enterprises or users, in addition to general papers, patents, etc., policies related to technology or the development of their industries should also belong to a type of scientific and technological resources. The cost and difficulty of acquiring users. Extracting valuable science and technology policy resources from a huge amount of data with mixed contents and providing accurate and fast retrieval will help to break down information barriers and reduce the cost of information acquisition, which has profound social significance and social utility. This article focuses on the difficulties and problems in the field of science and technology policy, and introduces related technologies and developments. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics 5

An efficient face recognition attack method based on generat...

引用

5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Ding, Hu He, Shumeng Wu, Yanwen Jin, Yongli Gan, Lin Xu, Gaodi Yang, Houqun Hainan University School of Computer Science and Technology Hainan Haikou China Chongqing University School of Big Data and Software Engineering Chongqing China Anhui University School of Computer Science and Technology Anhui Hefei China Purdue University Elmore Family School of Computer and Electrical Engineering West Lafayette United States Hainan Association for Artificial Intelligence Hainan Haikou China

ISBN: (纸本)9781665426305

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks. © 2021 IEEE.

关键词： Face recognition

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