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IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Ying’An Cui Xue Li Yande Wu Department of Computer Science and Engineering Xi’an University of Technology Xi’an China School of International Business Shaanxi Normal University Xi’an China Software R&D Department 2 China Unicom Western Innovation Research Institute Co. Ltd Xi’an China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In modern organization theories, the social network established by cyber criminals is termed the crime cyber black industry chain. Under the current technical level, treating a crime cyber black industry chain as a whole dataset has to face severe challenges in many aspects, so the research of the sampling method specifically for it has been one of the current hot topics. Based on the structural feature of a cybercrime industry chain, the overall framework and the detailed algorithm of a multi-stage sampling method are proposed. Tested with real cybercrime datasets, our proposed multi-stage sampling method outperforms existing mainstream single-stage sampling methods at macro, meso, and micro scales, indicating it can solve practical problems with high efficiency and low cost.

关键词： Industries Costs Social networking (online) Organizations Sampling methods Information and communication technology Reliability

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High-Resolution Natural Image Matting by Refining Low-resolution Alpha Mattes

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IEEE Transactions on Image Processing 2025年 34卷 3323-3335页

作者： Ye, Xianmin Liang, Yihui Tan, Mian Feng, Fujian Wang, Lin Huang, Han Guizhou Minzu University College of Data Science and Information Engineering Guiyang 550025 China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang 550025 China University of Electronic Science and Technology of China School of Computer Science Zhongshan Institute Zhongshan 528400 China South China University of Technology School of Software Engineering Guangzhou 510006 China Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China Guangzhou 510006 China Guangdong Engineering Center for Large Model and GenAI Technology Guangzhou 510006 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China

High-resolution natural image matting plays an important role in image editing, film-making and remote sensing due to its ability of accurately extract the foreground from a natural background. However, due to the complexity brought about by the proliferation of resolution, the existing image matting methods cannot obtain high-quality alpha mattes on high-resolution images in reasonable time. To overcome this challenge, we introduce a high-resolution image matting framework based on alpha matte refinement from low-resolution to high-resolution (HRIMF-AMR). The proposed framework transforms the complex high-resolution image matting problem into low-resolution image matting problem and high-resolution alpha matte refinement problem. While the first problem is solved by adopting an existing image matting method, the latter is addressed by applying the Detail Difference Feature Extractor (DDFE) designed as a part of our work. The DDFE extracts detail difference features from high-resolution images by measuring the image feature difference between high-resolution images and low-resolution images. The low-resolution alpha matte is refined according to the extracted detail difference feature, providing the high-resolution alpha matte. In addition, the Matte Detail Resolution Difference (MDRD) loss is introduced to train the DDFE, which imposes an additional constraint on the extraction of detail difference features with mattes. Experimental results show that integrating HRIMF-AMR significantly enhances the performance of existing matting methods on high-resolution images of Transparent-460 and Alphamatting. © 1992-2012 IEEE.

关键词： alpha matte detail detail difference feature High-resolution image matting natural image matting

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Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

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computers, Materials & Continua 2020年第4期63卷 489-507页

作者： Yong Yu Yongjun Luo Tong Li Shudong Li Xiaobo Wu Jinzhuo Liu Yu Jiang School of Software Key Laboratory in Software Engineering of Yunnan ProvinceYunnan UniversityKunming650091China School of Big Data Yunnan Agricultural UniversityKunming650201China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current *** this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is *** edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between *** can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender *** verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.

关键词： Personalized recommendation one-mode projection weighted bipartite network novelty recommendation diversity

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CHIEF: Clustering with Higher-order Motifs in Big Networks

arXiv

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arXiv 2022年

作者： Xia, Feng Yu, Shuo Liu, Chengfei Li, Jianxin Lee, Ivan School of Engineering IT and Physical Sciences Federation University Australia Ballarat Australia School of Software Dalian University of Technology Dalian China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia School of IT Deakin University Melbourne Australia STEM University of South Australia Adelaide Australia

Clustering a group of vertices in networks facilitates applications across different domains, such as social computing and Internet of Things. However, challenges arises for clustering networks with increased scale. This paper proposes a solution which consists of two motif clustering techniques: standard acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms first find the maximal k-edge-connected subgraphs within the target networks to lower the network scale, then employ higher-order motifs in clustering. In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs. For CHIEF-ST, we illustrate that all target motifs will be kept after this procedure when the minimum node degree of the target motif is equal or greater than k. For CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the Laplacian matrix are relatively stable after this step. That is, CHIEF-ST has no influence on motif clustering, whereas CHIEF-AP introduces limited yet acceptable impact. In the second procedure, we employ higher-order motifs, i.e., heterogeneous four-node motifs clustering in higher-order dense networks. The contributions of CHIEF are twofold: (1) improved efficiency of motif clustering for big networks;(2) verification of higher-order motif significance. The proposed solutions are found to outperform baseline approaches according to experiments on real and synthetic networks, which demonstrates CHIEF's strength in large network analysis. Meanwhile, higher-order motifs are proved to perform better than traditional triangle motifs in clustering. © 2022, CC BY-NC-SA.

关键词： Eigenvalues and eigenfunctions

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Generalizable Metric Network for Cross-domain Person Re-identification

arXiv

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arXiv 2023年

作者： Qi, Lei Liu, Ziang Shi, Yinghuan Geng, Xin School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education 211189 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Learning Hierarchical Graph Representation for Image Manipulation Detection

arXiv

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arXiv 2022年

作者： Pan, Wenyan Zhou, Zhili Ling, Miaogen Geng, Xin Wu, Q.M. Jonathan School of Computer and Software & Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering University of Windsor WindsorON Canada

The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images to locate the manipulated regions. However, these approaches ignore the feature correlations, i.e., feature inconsistencies, between manipulated regions and non-manipulated regions, leading to inferior detection performance. To address this issue, we propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches: the backbone network branch and the hierarchical graph representation learning (HGRL) branch for image manipulation detection. Specifically, the feature maps of a given image are extracted by the backbone network branch, and then the feature correlations within the feature maps are modeled as a set of fully-connected graphs for learning the hierarchical graph representation by the HGRL branch. The learned hierarchical graph representation can sufficiently capture the feature correlations across different scales, and thus it provides high discriminability for distinguishing manipulated and non-manipulated regions. Extensive experiments on four public datasets demonstrate that the proposed HGCN-Net not only provides promising detection accuracy, but also achieves strong robustness under a variety of common image attacks in the task of image manipulation detection, compared to the state-of-the-arts. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Self-supervised Multi-task Distillation for Few-shot Classification

Self-supervised Multi-task Distillation for Few-shot Classif...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Enze Ji Shi Chen Tiandong Ji Jing Li Zhikui Chen Computer Network Information Center Chinese Academy of Sciences Beijing China Big Data Institute Hong Kong University of Science and Technology Hong Kong China School of Software Technology Dalian University of Technology Dalian China State Grid Liaoning Marketing Service Center Shenyang China

Few-shot classification has gained significant attention owing to the effectiveness in classifying unseen classes with a few annotated images. Although previous works achieve encouraging classification performance, they heavily rely on one-hot labels during the meta-learning process, which may result in the supervision collapse and limited generalization. To address these challenges, the few-shot classification based on a self-supervised multi-task distillation (SMD) is proposed for mitigating the nuisance arising from one-hot labels. Specifically, SMD formulates multiple auxiliary tasks to enhance the cross entropy classification in a multi-task learning manner, including the self-supervised classification task and the self-distilled classification task. These auxiliary tasks do not rely on one-hot labels in meta-learning, which can effectively enhance generalization performance of the model. Finally, extensive experiment results on two benchmark datasets, i.e., CIFAR-FS and FC-100, demonstrate the superiority and effectiveness of SMD.

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A Look at Service Meshes 12

A Look at Service Meshes

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Koschel, Arne Bertram, Marvin Bischof, Richard Schulze, Kevin Schaaf, Marc Astrova, Irina University of Applied Sciences and Arts Hannover Faculty Iv Department of Computer Science Hannover Germany University of Applied Sciences Northwestern Switzerland Institute of Information Systems Olten Switzerland Tallinn University of Technology School of It Department of Software Science Tallinn Estonia

ISBN: (纸本)9781665400329

Service meshes can be seen as an infrastructure layer for microservice-based applications that are specifically suited for distributed application architectures. It is the goal to introduce the concept of service meshes and its use for microservices with the example of an open source service mesh called Istio. This paper gives an introduction into the service mesh concept and its relation to microservices. It also gives an overview of selected features provided by Istio as relevant to the above concept and provides a small sample setup that demonstrates the core features. © 2021 IEEE.

关键词： Mesh generation

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Detection of Backdoor Attacks Using Targeted Universal Adversarial Perturbations for Deep Neural Networks

SSRN

引用

SSRN 2023年

作者： Qu, Yubin Huang, Song Chen, Xiang Wang, Xingya College of Command and Control Engineering Army Engineering University of PLA Nanjing210007 China School of Information Engineering Jiangsu College of Engineering and Technology Nantong226001 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China School of Information Science and Technology Nantong University Nantong226019 China College of Computer Science and Technology Nanjing Tech University China

The backdoors using targeted universal adversarial perturbations against deep neurall networks has been explored. This backdoor does not require data poisoning or model tampering. Rretraining deep neural network models using adversarial training for security is time-consuming and does not apply to deep neural networks in runtime. We want to detect backdoors using a black-box testing approach. We observe that after superimposing random noise on the input of a backdoor attack, the output still tends to remain the same, so we propose Sequential Analysis method based on the Metamorphosis Testing (SAMT). We designed two metamorphic relations for test case generation. Using sequential sampling, we calculate the label stability rate and infer whether the image to be verified contains a backdoor based on the sequential probability ratio change. The experimental results show that our method has a higher backdoor detection success rate than the state-of-the-art universal adversarial perturbations detection algorithms. Moreover, our method does not need to use the model structure of deep neural networks, which has more adaptability and generalization ability. Based on our proposed method, we can simply add a backdoor detection layer to detect backdoors as early as possible, which can eventually alleviate the harm of such backdoors. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Cloud Computing: Serverless 12

Cloud Computing: Serverless

引用

12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Koschel, Arne Klassen, Samuel Jdiya, Kerim Schaaf, Marc Astrova, Irina University of Applied Sciences and Arts Hannover Faculty Iv Department of Computer Science Hannover Germany Institute of Information Systems University of Applied Sciences Northwestern Switzerland Olten Switzerland Tallinn University of Technology School of It Department of Software Science Tallinn Estonia

ISBN: (纸本)9781665400329

A serverless architecture is a new approach to offering services over the Internet. It combines BaaS (Backend-as-a-service) and FaaS (Function-as-a-service). With the serverless architecture no own or rented infrastructures are needed anymore. In addition, the company does not have to worry about scaling any longer, as this happens automatically and immediately. Furthermore, there is no need any longer for maintenance work on the servers, as this is completely taken over by the provider. Administrators are also no longer needed for the same reason. Finally, many ready-made functions are offered, with which the development effort can be reduced. As a result, the serverless architecture is very well suited to many application scenarios, and it can save considerable costs (server costs, maintenance costs, personnel costs, electricity costs, etc.). The company only must subdivide the source code of the application and upload it to the provider's server. The rest is done by the provider. © 2021 IEEE.

关键词： Cloud computing

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