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arXiv 2024年

作者： Zhang, Miao Wang, Ziming Xing, Runtian Xiao, Kui Li, Zhifei Zhang, Yan Tang, Chang School of Computer Science and Information Engineering Hubei University China Key Laboratory of Intelligent Sensing System and Security China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students’ proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it’s hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel framework for Cognitive Diagnosis called Disentangling Heterogeneous knowledge Cognitive Diagnosis(DisKCD) on untested knowledge. Specifically, we leverage course grades, exercise questions, and learning resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students’ proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students’ proficiency on UKCs. Our code is available at https://***/Hubuers/DisKCD. © 2024, CC BY.

关键词： Students

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection 39

Each Fake News is Fake in its Own Way: An Attribution Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defense Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Edge Detectors Based on Pauta Criterion with Application to Hybrid Compact-WENO Finite Difference Scheme

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Advances in Applied Mathematics and Mechanics 2023年第6期15卷 1379-1406页

作者： Chunhua Zhang Zhen Gao Sa Ye Peng Li School of Mathematical Sciences Ocean University of ChinaQingdaoShandong 266100China Institute of Agricultural Information Chinese Academy of Agricultural SciencesKey Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Engineering Mechanics Shijiazhuang Tiedao UniversityShijiazhuangHebei 050043China

In the last two decades,many edge detection methods have been developed and widely used in image processing for edge detection and the hybrid compactWENO finite difference(hybrid)schemes for solving the system of hyperbolic conservation laws with solutions containing both discontinuous and complex fine-scale ***,many edge detection methods include the problem-dependent parameters such as the high order multi-resolution(MR)analysis(Harten,JCP,49(1983)).Therefore,we combined the Tukey’s boxplot method with MR analysis(Gao et al.,JSC,73(2017))to overcome this problem in a *** the Tukey’s boxplot method needs to sort the data at the beginning of Runge-Kutta time integration method,which is relatively time-consuming and *** this study,we employ the PauTa criterion and remove the problem-dependent parameters in the MR ***,two new edge detection approaches,which are based on second-order central difference scheme and Ren’s idea(Ren et al.,JCP,192(2003)),are also *** accuracy,efficiency and robustness of the hybrid scheme with the new edge detectors are verified by numerous classical one-and two-dimensional examples in the image processing and compressible Euler equations with discontinuous solutions.

关键词： Edge detection Pauta criterion multi-resolution hybrid

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Variable lanes traffic optimization control method combined with colorful asphalt materials 9

Variable lanes traffic optimization control method combined ...

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9th International Conference on Mechanical engineering, Materials, and Automation Technology, MMEAT 2023

作者： Wang, Ruihan Zhan, Xuejuan Liang, Zijun Li, Yuqi Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

ISBN: (纸本)9781510668584

Along with our country's continuous growth, rising rush hours traffic congestion, many scholars proposed setting variable lanes solution, but due to the variable lanes different times the direction of the lane change, which made the driver can not be correctly into the right lane. So we propose a traffic optimization control method for variable lanes combined with colorful asphalt materials. Firstly, enumeration method is used to list all the direction schemes and phase schemes of the variable lanes, and the optimal scheme is obtained according to the constraints. In order to better distinguish the variable lanes from the conventional lanes, the variable lanes are paved with colorful asphalt environmental materials. Then the phase timing is calculated according to the determined variable lanes, phase scheme and traffic flow. Finally, through VISSIM traffic simulation software to evaluate the results, the results show that combining with colorful asphalt materials clearly calibrate the position of the variable lanes, effective rule on the variable lanes traffic space, and on this basis, through phase timing optimization technology to further improve the intersection traffic efficiency, effectively alleviate the intersection rush hours traffic congestion. It provides a basis for the research of variable lanes optimization control combined with colorful asphalt materials. © 2023 SPIE.

关键词： Traffic congestion

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A Survey of Literature Analysis Methods Based on Representation Learning 17th

A Survey of Literature Analysis Methods Based on Representat...

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17th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2022

作者： Zhang, Qinghui Chen, Yi Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789811950957

The analysis of massive scientific literature collections can help researchers capture potential knowledge, understand disciplinary insights, and discover opportunities for academic collaboration. However, the huge volume of literature data becomes a major problem for human analysis and computer processing, which prevents scholars from rapidly obtaining disciplinary knowledge. Representation learning methods can effectively handle large-scale text and graph data, which have excelled in literature analysis tasks. In this study, we introduce the types of literature data and several typical academic networks. Then, we review the representation learning models and classify these methods into (a) word representation learning-based, including word2vec, GloVe, ELMo, Bert, etc.;(b) graph representation learning-based, including matrix factorization-based, random walk-based and deep learning-based methods, such as DeepWalk, node2vec, GCN, etc. Finally, we discuss the opportunities and present three challenges of representation learning-based approaches for literature analysis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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Analyzing the Convergence of Federated Learning with Biased Client Participation 9th

Analyzing the Convergence of Federated Learning with Bias...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Learning Concept Prerequisite Relation via Global knowledge Relation Optimization 39

Learning Concept Prerequisite Relation via Global Knowledge ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Miao Wang, Jiawei Xiao, Kui Wang, Shihui Zhang, Yan Chen, Hao Li, Zhifei School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China Key Laboratory of Intelligent Sensing System and Security Ministry of Education China

ISBN: (纸本)157735897X

Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global knowledge Relation Optimization Model(GKROM). Specifically, we capture the impact of different knowledge relation types on document and concept semantic representations separately, integrating the document and concept semantic representations. Then, we introduce multi-objective learning to optimize the knowledge relation network from a global perspective. Through the above optimization, GKROM learns richer semantic representations for concepts and documents, improving the accuracy of concept prerequisite relation learning. Extensive experiments on public datasets demonstrate the effectiveness of our GKROM, achieving state-of-the-art performance in concept prerequisite relation learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5166-5181页

作者： Yang, Cuixin Dong, Rongkang Xiao, Jun Zhang, Cong Lam, Kin-Man Zhou, Fei Qiu, Guoping The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong Shenzhen University College of Electronic and Information Engineering Shenzhen518060 China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen518060 China University of Nottingham NottinghamNG8 1BB United Kingdom School of Computer Science NingboNG8 1BB China

As virtual and augmented reality applications gain popularity, omnidirectional image (ODI) super-resolution has become increasingly important. Unlike 2D plain images that are formed on a plane, ODIs are projected onto spherical surfaces. Applying established image super-resolution methods to ODIs, therefore, requires performing equirectangular projection (ERP) to map the ODIs onto a plane. ODI super-resolution needs to take into account geometric distortion resulting from ERP. However, without considering such geometric distortion of ERP images, previous methods only utilize a limited range of pixels and may easily miss self-similar textures for reconstruction. In this paper, we introduce a novel Geometric Distortion Guided Transformer for Omnidirectional image Super-Resolution (GDGT-OSR). Specifically, a distortion modulated rectangle-window selfattention mechanism, integrated with deformable self-attention, is proposed to better perceive the distortion and thus involve more self-similar textures. Distortion modulation is achieved through a newly devised distortion guidance generator that produces guidance for the rectangular windows by exploiting the variability of distortion across latitudes. Furthermore, we propose a dynamic feature aggregation scheme to adaptively fuse the features from different self-attention modules. We present extensive experimental results on public datasets and show that the new GDGT-OSR outperforms methods in existing literature. © 1991-2012 IEEE.

关键词： Image resolution

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A Transformer-based Network for Multi-view 3D Mesh Generation 9

A Transformer-based Network for Multi-view 3D Mesh Generatio...

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9th IEEE Smart World Congress, SWC 2023

作者： Shi, Wuzhen Liu, Zhijie Li, Yingxiang Wen, Yang Liu, Yutao Shenzhen University Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Ocean University of China School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350319804

Three-dimensional (3D) reconstruction has been widely used in cultural relics protection, architectural design, e-commerce and other fields. In recent years, deep network-based 3D mesh generation has become mainstream. However, the existing 3D mesh generation based on convolutional neural network mainly focuses on local information, and can only update the feature of the central vertex through adjacent vertices, which greatly affects the efficiency of feature transfer between remote vertices. In order to obtain better 3D reconstruction results, we explore a 3D mesh reconstruction network combining transformer and graph convolution, which effectively takes into account the utility of global and local features. The proposed method alternately uses transformers and graph convolutions at multiple scales, so as to effectively perceive the global structure of the target of interest through the transformer and perceive local details through the graph convolution. Furthermore, our method effectively fuses information from different viewpoints to improve the performance of 3D mesh reconstruction. Experimental results show that the proposed method effectively improves the accuracy of 3D mesh reconstruction. © 2023 IEEE.

关键词： deep network graph convolution Mesh generation multi-view 3D reconstruction transformer

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