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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Chen, Minghui Ji, Yingjun Wang, Aoyuan Wang, Jiacun Liaoning University College Of Information Shenyang China Monmouth University Department Of Computer Science And Software Engineering West Long BranchNJ United States

ISBN: (纸本)9798350315790

To address the issue of sensitivity to external interference and noise in traditional moving object recognition and tracking algorithms, this paper proposes a novel algorithm that combines Image Fusion-based Frame Difference (IFD) with Differential Translation Transformation (DTT). Firstly, image enhancement and noise reduction are applied to adjust contrast and brightness in the images of moving objects. Subsequently, the IFD algorithm is employed to adjust the blending of new frame with the background, smoothly fusing them. Pixel differences between the fusion frame and the background are calculated to identify moving objects in the image. Matching is performed between the target areas obtained by the IFD algorithm based on pixel displacement differences between consecutive frames and the target displacements obtained by the DTT algorithm. This allows for recognition and tracking of the targets. To validate the effectiveness of the algorithm, experiments are conducted on two sets of moving-object image data, resulting in accuracies of 90.12% and 90.87%. © 2024 IEEE.

关键词： Image fusion

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RDH-DES: Reversible Data Hiding over Distributed Encrypted-Image Servers Based on Secret Sharing

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ACM Transactions on Multimedia Computing, Communications and Applications 2023年第1期19卷 1-19页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Xia, Zhihua Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Nanjing University of Information Science and Technology No. 219 Ningliu Road Nanjing Jiangsu210044 China Department of Computer Science and Information Engineering National Dong Hwa University No. 1 Sec. 2 Da Hsueh Rd. Hualien974301 Taiwan

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective. © 2023 Association for Computing Machinery.

关键词： Steganography

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Learning Age of information-Oriented Energy-Efficient Scheduling with Non-Orthogonal Multiple Access 13

Learning Age of Information-Oriented Energy-Efficient Schedu...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Jinhua Cao, Yi Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350355079

We propose a deep Q-network (DQN)-based user scheduling scheme aimed at low network-wide age of information (AoI) in a non-orthogonal multiple access (NOMA) uplink status update system. In our system, end users can harvest energy from wireless signals received on the radio-frequency band to extend the lifespan of their batteries. Given the battery replacement cost in many practical scenarios, our objective is to design a scheduling scheme to maintain a low AoI while efficiently utilizing energy harvesting and reducing the number of battery replacements. Considering the instantaneous AoI, remaining energy, and channel condition of end users, we model the user scheduling problem as a Markov decision process (MDP), which is solved by DQN. In particular, the choice of transmission power to save energy while enabling a high NOMA decoding rate is incorporated in the design of DQN. Experiments indicate that our DQN-based scheduling scheme achieves a low average AoI of the system with significantly fewer battery replacements compared to various benchmarking schemes. © 2024 IEEE.

关键词： Markov processes

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An Empirical Evaluation of the Effectiveness of Spider and Proxy Modes for Web Security Testing 10

An Empirical Evaluation of the Effectiveness of Spider and P...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Chiu, Kuan-Wei Yang, Shih-Sheng Lee, Shin-Jie Lee, Wen-Tin National Cheng Kung University Department of Computer Science and Information Engineering Taiwan National Kaohsiung Normal University Department of Software Engineering and Management Taiwan

ISBN: (纸本)9798350304770

Spider and proxy modes are two commonly employed methods supported by dynamic application security testing (DAST) software. Despite efforts to enhance the automated spider's efficiency, deep exploration of web applications is still constrained by the need for manual intervention in certain complex UI operations. In this regard, the proxy mode serves a crucial intermediary, intercepting and inspecting request messages exchanged between the browser and the web application during manual or scripted browsing activities. This study aims to assess the efficacy of these two modes in terms of code coverage and the number of requests, utilizing two popular PHP-based open-source web applications. The experimental findings demonstrate that employing a hybrid mode (Spider-Last) yields a significant improvement compared to using the spider or proxy mode independently. © 2023 IEEE.

关键词： software testing

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Towards Gradient Equalization and Feature Diversification for Long-Tailed Multi-Label Image Recognition

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IEEE Transactions on Multimedia 2025年 27卷 3489-3500页

作者： Chen, Zhao-Min Cui, Quan Zhang, Xiaoqin Deng, Ruoxi Xia, Chaoqun Lu, Shijian Nanjing University National Key Laboratory for Novel Software Technology Nanjing210093 China Waseda University Graduate School of Information Production and Systems Fukuoka169-8050 Japan Wenzhou University College of Computer Science and Artificial Intelligence Wenzhou325060 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem, i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted;2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group- wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals. © 1999-2012 IEEE.

关键词： Labeled data

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Comp-RPKI: A Decentralized Protocol for Full Route Origin Validation 9

Comp-RPKI: A Decentralized Protocol for Full Route Origin Va...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Wu, Yanwei Wang, Yun Dejulius, Jack Abboud, Riad Woitel, Melvin Osadchyy, Bohdan Richardson, Kyree University of Wisconsin Computer Science and Software Engineering PlattevilleWI United States Bradley University Computer Science and Information Systems PeoriaIL United States

ISBN: (纸本)9798350331240

The Resource Public Key Infrastructure (RPKI) is an essential technology for securing the Border Gateway Protocol (BGP) routing on the Internet. Although the global RPKI-ROV (Route Origin Validation) rate of unique Prefix-Origin Pairs(IPv4) has been improved from 17% in June 2019 (pre-pandemic) to 41.12% in March 2023 (post-pandemic) [1] - [3], the overall RPKI-ROV rate remains below 50%. This indicates a significant insufficiency in RPKI-ROV deployment for ensuring secure BGP routing and persistent security challenges continue to persist in the Internet. To address this, we first develop software to analyze and visualize the relationships among Autonomous Systems (ASes), degree distribution, and their RPKI validation status, given a region in IL. Next, we investigate the RPKI validation rates along real-time routing paths from Peoria, IL to all 50 states in the USA. Based on the analysis, we propose a novel lightweight, decentralized, easy-to-deploy, offline protocol called Comp-RPKI, which aims to complement and address the partial deployment of RPKI. Comp-RPKI aims to ensure every segment of a routing path is validated either through online RPKI or secured by offline Comp-RPKI, thereby achieving full RKPI-ROV coverage and enhancing BGP security. © 2023 IEEE.

关键词： Border Gateway Protocol

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Towards Improving the Quality of Requirement and Testing Process in Agile software Development:An Empirical Study

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computers, Materials & Continua 2024年第9期80卷 3761-3784页

作者： Irum Ilays Yaser Hafeez Nabil Almashfi Sadia Ali Mamoona Humayun Muhammad Aqib Ghadah Alwakid University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia School of Arts Humanities and Social Sciences University of RoehamptonLondonSW155PJUK Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia

software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing ***,it is difficult to identify all faults in *** requirement changes continuously,it increases the irrelevancy and redundancy during *** to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements *** this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based *** research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change *** compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based ***,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection ***,the proposed model was evaluated by conducting *** results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user *** predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user ***,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier ***,it provides guidelines for practitioners and researchers in the modern *** the future,we will provide the working prototype of this model for proof of concept.

关键词： Requirement prediction software testing agile software development semantic analysis case-based reasoning

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MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTUAL COLONOSCOPY 18

MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTU...

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18th International Conference on Interfaces and Human computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on computer science and information Systems 2024, MCCSIS 2024

作者： Ma, Ming Sun, Chenyang Lei, Na Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University United States School of Software Technology Dalian University of Technology China International School of Information Science & Engineering Dalian University of Technology China

ISBN: (纸本)9789898704580

Colorectal polyps can evolve into colon cancer over time. Early screening or detection of colon polyps using computer-aided detection (CAD) techniques along with removal of the polyps can lower the risk of colon cancer. As an alternative to optimal colonoscopy (OC), virtual colonoscopy (VC) has the advantage of non-invasiveness in the detection of colon polyps. Recent decades have witnessed rapid advancement in VC based polyp detection and false positive (FP) reduction techniques using machine learning or deep learning methods. However, the majority of existing methods are based on the computation using computed tomography (CT) images of patient data, which are in some cases very computationally expensive, particularly in the scenario of necessity of large dataset for model training. In this paper, we propose a novel automatic polyp detection method termed Mesh-PolypDet using the surface meshes of colorectal data. The initial detection of polyp candidates is performed on 3D colon surface meshes, and the subsequent FP reduction is carried out using the visual transformer and conformal mapping. Experiments are conducted to evaluate the performance of the proposed method in comparison with other existing methods. © 2024 Interfaces and Human computer Interaction. All Rights Reserved.

关键词： Conformal mapping

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On the Adversarial Robustness of Hierarchical Classification

On the Adversarial Robustness of Hierarchical Classification

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ran Zeng, Simeng Wu, Wenhui Jia, Yuheng Ng, Wing W. Y. Wang, Xizhao School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665410205

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier;and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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