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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.

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Disentangled Hypergraph Collaborative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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IEEE International Conference on Web Services (ICWS)

作者： Xiao Liu Shunmei Meng Qianmu Li Xiaolong Xu Lianyong Qi Wanchun Dou Jing Zhang Xuyun Zhang Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China School of Computer and Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum (East China) China School of Cyber Science and Engineering Southeast University China School of Computing Macquarie University Australia

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

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AUGRMIXAT: A DATA PROCESSING AND TRAINING METHOD FOR IMPROVING MULTIPLE ROBUSTNESS AND GENERALIZATION PERFORMANCE

arXiv

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arXiv 2022年

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Electronic Science and Engineering Nanjing University China

Deep neural networks are powerful, but they also have shortcomings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. However, we find that the specific robustness is often improved at the sacrifice of the additional robustness or generalization ability of the neural network model. In particular, adversarial training methods significantly hurt the generalization performance on unperturbed data when improving adversarial robustness. In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models. Finally, we validate the effectiveness of AugRmixAT on the CIFAR-10/100 and Tiny-ImageNet datasets. The experiments demonstrate that AugRmixAT can improve the model's generalization performance while enhancing the white-box robustness, black-box robustness, common corruption robustness, and partial occlusion robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural network models

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Vulnerable Region-Aware Greybox Fuzzing

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Journal of computer science & technology 2021年第5期36卷 1212-1228页

作者： Ling-Yun Situ Zhi-Qiang Zuo Le Guan Lin-Zhang Wang Xuan-Dong Li Jin Shi Peng Liu State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Information Management Nanjing UniversityNanjing 210023China Department of Computer Science University of GeorgiaAthensGA 30602U.S.A. College of Information Sciences and Technology Pennsylvania State UniversityState CollegePA 16802U.S.A

Fuzzing is known to be one of the most effective techniques to uncover security vulnerabilities of large-scale software *** fuzzing,it is crucial to distribute the fuzzing resource appropriately so as to achieve the best fuzzing performance under a limited *** distribution strategies of American Fuzzy Lop(AFL)based greybox fuzzing focus on increasing coverage blindly without considering the metrics of code regions,thus lacking the insight regarding which region is more likely to be vulnerable and deserves more fuzzing *** tackle the above drawback by proposing a vulnerable region-aware greybox fuzzing ***,we distribute more fuzzing resources towards regions that are more likely to be vulnerable based on four kinds of code *** implemented the approach as an extension to AFL named ***-scale experimental evaluations validate the effectiveness and efficiency of RegionFuzz-11 new bugs including three new CVEs are successfully uncovered by RegionFuzz.

关键词： vulnerability detection greybox fuzzing code metrics resource distribution

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Multi-view Multi-label Learning with Dual-Attention Networks for Stroke Screen

Multi-view Multi-label Learning with Dual-Attention Networks...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Shen, Jundong Zhang, Yi Yu, Cheng Wang, Chongjun National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728162157

With the acceleration of urbanization, there is an explosive growth trend of the stroke burden in China. Consequently, it is necessary to adopt a regular screen to reduce brain damage and other complications. For one thing, the risk factors screened can be divided into 6 subsets: medical history, family history, fasting blood, lifestyle, physical characteristics, and demographic information. For another, the screen results can be categorized into 5 groups: normal carotid plaque, thickening, unstable plaque, and arterial stenosis. To address the above problems, Multi-View Multi-label (MVML) learning provides a fundamental framework for handling such tasks, which treats the risk factors as multi-view data and the screen results as multi-label data. Besides, it is a challenging issue of how to effectively utilize view correlations and label correlations in MVML learning. In this paper, we propose a novel Multi-view Multi-label Learning with Dual-Attention Networks (MML-DAN) for stroke screen, which can strike a balance between label-specific views and label correlations. Meanwhile, the attention mechanism aims to focus on the most pertinent information of the corresponding labels rather than using all available risk factors equally. Experiments on one real-world dataset of stroke screen demonstrate the superiority of the proposed MML-DAN for stroke screen prediction. To further validate the effectiveness and generalization capability of MML-DAN, extensive experiments on 5 benchmark MVML datasets show that MML-DAN significantly outperforms other state-of-the-art MVML algorithms. © 2020 IEEE.

关键词： computer vision

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Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

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Intelligent Automation & Soft Computing 2024年第4期39卷 725-744页

作者： Jielin Jiang Chao Cui Xiaolong Xu Yan Cui School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing210023China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210038China

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric ***,identifying fabric defects forms a crucial part of the fabric production *** fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively *** learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex ***,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect *** MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric *** this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric *** this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned *** experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

关键词： Fabric defect detection multi-layer features deformable convolution

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3lab/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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RAT: A Refactoring-Aware Tool for Tracking Code History

RAT: A Refactoring-Aware Tool for Tracking Code History

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Feifei Niu Junqian Shao Chaofan Xu Christoph Mayr-Dorn Wesley K. G. Assunção Liguo Huang Chuanyi Li Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina USA Department of Computer Science and Engineering Southern Methodist University Dallas Texash USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT_Demo Demo Video: https://***/VI_xwUaIPp4

关键词： Location awareness software maintenance Codes Source coding computer bugs Maintenance engineering History

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SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

arXiv

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arXiv 2025年

作者： Peng, Yubo Xiang, Luping Yang, Kun Jiang, Feibo Wang, Kezhi Wu, Dapeng Oliver State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Suzhou China School of Information Science and Engineering Hunan Normal University Changsha China Department of Computer Science Brunel University London United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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LECF: recommendation via learnable edge collaborative filtering

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science China(Information sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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