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International Conference on Computer Supported Cooperative Work in Design

作者： You Chen Hua Dai Mingfeng Jiang Qu Lu Pengyue Li Bohan Li Geng Yang Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

During infectious disease outbreaks, tracking contacted objects is important for suppressing the spread of the virus and using trajectories of moving objects to discover contacted objects is one of effective approaches. Existing algorithms focus on individual contact event discovery but lack the ability to obtain the k-degree contact events. In this paper, we propose efficient k-degree contact pattern mining algorithms that are capable of mining k-degree contact events. The definition of k-degree contact event is first formulated. Based on the definition, a sliding window-based baseline k-degree contact pattern mining algorithm (KCPMA) is presented. To improve the mining efficiency, the sample-point checking strategy and R-tree index are adopted and the optimized mining algorithm (KCPMA+) is proposed. Comprehensive experiments on real datasets demonstrate that the proposed algorithms are effective and efficient in mining k-degree contact events.

关键词： Costs Infectious diseases Federated learning Computer viruses Trajectory Indexes data mining

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space 20

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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20th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploiting Robust Model Watermarking Against the Model Fine-Tuning Attack via Flat Minima Aware Optimizers

Exploiting Robust Model Watermarking Against the Model Fine-...

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

作者： Dongdong Lin Yue Li Bin Li Jiwu Huang Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep neural networks (DNNs), model watermarking has emerged as a widely adopted technique for safeguarding model copyrights. A prevalent method involves utilizing a watermark decoder to retrieve watermark bits from generated outputs, but such methods are often vulnerable to model fine-tuning attacks. Traditionally, this challenge is mitigated through adversarial training or data augmentation, both of which significantly increase the computational burden. In this paper, we present a solution employing Flat Minima Aware (FMA) optimizers to bolster the robustness of model watermarking without requiring additional training data. By optimizing the watermark loss with flat minima awareness, our approaches significantly enhance the robustness of watermarks against the model fine-tuning attack. Comprehensive experiments have demonstrated our method’s superior ability to preserve watermark integrity. These findings suggest that this innovative optimization strategy offers a robust and efficient pathway for protecting models, thereby contributing to more secure and reliable model copyright protection mechanisms.

关键词： Training Resistance Computational modeling Training data Watermarking Signal processing Diffusion models Robustness Speech processing Optimization

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Anomalous Hall effects in magnetic weak topological insulator films

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Physical Review B 2025年第4期111卷 045409-045409页

作者： Rui Chen Xiao-Xia Yi Bin Zhou Dong-Hui Xu Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics Chongqing University Chongqing 400044 China Center of Quantum Materials and Devices Chongqing University Chongqing 400044 China

The interplay between magnetism and strong topological insulator gives rise to distinct new topological phases and various intriguing phenomena, attracting significant attention in recent years. However, magnetic effects in weak topological insulators remain largely unexplored. In this work, we systematically investigate the magnetic effect on thin films of weak topological insulators. We focus on ferromagnetic and antiferromagnetic effects, which have been extensively studied in strong topological insulators, as well as the recently highlighted altermagnetic effect. We reveal that the interplay between magnetism and weak topological insulators leads to a variety of Hall effects in the absence of an external magnetic field, including the metallic quantum anomalous Hall effect without chiral edge states, the quantum anomalous Hall effect with a higher Hall conductance plateau, the quantized layer Hall effect, the metallic half-quantized valleylike Hall effect, and a quantized valleylike Hall effect. This work provides valuable insights for exploring magnetic effect on weak topological insulators.

关键词： Topological insulators

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Community Detection Based on Deep Network Embedding with Dual Self-supervised Training 3rd

Community Detection Based on Deep Network Embedding with Dua...

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3rd International Conference on Big data and security, ICBDS 2021

作者： Chen, Yunfang Mao, Haotian Wang, Li Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

We propose a community discovery method based on deep auto-encoding (DGAE_DST). Firstly, we use the pre-trained two-layer neural network and k-means algorithm to initialize the centroid vector, and then use the DNN module and the GCN module to capture the structure features and attributes respectively. The attribute information is fed to the GCN layer to integrate the structure information and attributes. Finally, DNN and GCN respectively output the reconstructed adjacency matrix and feature matrix, and minimize their loss from the original matrix. The experimental results on four datasets show that the DGAE_DST method has higher stability and accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Disorder-enhanced layer Hall effect in a magnetic sandwich heterostructure

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Physical Review B 2024年第11期109卷 115301-115301页

作者： Xiao-Xia Yi Chun-Bo Hua Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

The layer Hall effect is a type of Hall effect where electrons on the top and bottom layers deflect in opposite directions. In this paper, we investigate the disorder effects on the layer Hall effect in a magnetic sandwich heterostructure that supports the axion insulator and normal insulator phases. We demonstrate that the layer Hall effect in the axion insulator phase is robust against weak disorder. Most interestingly, we find that the layer Hall effect in the normal insulator phase is significantly enhanced with increasing disorder strength. Theoretically, the disorder-enhanced layer Hall effect at weak disorder is explained by using the Born approximation. Experimentally, we suggest the disorder-enhanced layer Hall effect could be detected by applying a perpendicular electric field.

关键词： Topological Hall effect Dilute magnetic topological insulators Topological insulators

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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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Design Guidance for Lightweight Object Detection Models 3rd

Design Guidance for Lightweight Object Detection Models

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3rd International Conference on Big data and security, ICBDS 2021

作者： Wang, Rui Wang, Xueli Chen, Yunfang Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on a few steps of the model implementation and lack a global perspective. Therefore, this paper proposes a general lightweight model implementation framework, including network construction indicators, lightweight backbone network design, and model optimization. By analyzing the complexity indicator of network structure, the factors that affect network performance such as depth and width are summarized. On this basis, combine the One-Shot Aggregation (OSA) idea and Cross-Stage Partial Network (CSPNet) transformation to construct a general lightweight detection network CSPOSA. Further specific optimization strategies are proposed to prune the network structure and training process. For the network structure, the width and depth of the network are adjusted, the amount of parameters of the model is compressed. The training process is divided into the first, middle and last three stages to improve the detection performance of the model without adding extra computation. Taking embedded platform helmet detection as the experimental scene, the parameter amount of the realized model is 1/10 of the mainstream models YOLOv3 and YOLOv4, and the detection accuracy is similar, so it is more suitable for deployment on devices with limited computing power. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Computing power

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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RFC:a feature selection algorithm for software defect prediction

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Journal of systems Engineering and Electronics 2021年第2期32卷 389-398页

作者： XU Xiaolong CHEN Wen WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China School of Computing and Engineering University of West LondonLondon W55RFUK

Software defect prediction(SDP)is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects,so as to effectively predict defects in the new ***,there are redundant and irrelevant features in the software defect datasets affecting the performance of defect *** order to identify and remove the redundant and irrelevant features in software defect datasets,we propose ReliefF-based clustering(RFC),a clusterbased feature selection ***,the correlation between features is calculated based on the symmetric *** to the correlation degree,RFC partitions features into k clusters based on the k-medoids algorithm,and finally selects the representative features from each cluster to form the final feature *** the experiments,we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration(NASA)software defect prediction datasets in terms of area under curve(AUC)and *** experimental results show that RFC can effectively improve the performance of SDP.

关键词： software defect prediction(SDP) feature selection cluster

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