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A disk I/O optimized system for concurrent graph processing jobs

Frontiers of computer science 2024年第3期18卷 13-29页

作者： Xianghao XU Fang WANG Hong JIANG Yongli CHENG Dan FENG Peng FANG School of computer science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of computer science&Engineering University of Texas at ArlingtonArlingtonTX 76019USA College of computer and Data science Fuzhou UniversityFuzhou 350108China Zhejiang Lab Hangzhou 311121China

In order to analyze and process the large graphs with high cost efficiency,researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity *** the other hand,with the rapidly growing need of analyzing graphs in the real-world,graph processing systems have to efficiently handle massive concurrent graph processing(CGP)***,due to the inherent design for single graph processing job,existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP *** this paper,we propose GraphCP,a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP *** proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices,which efficiently overcomes above challenges faced by existing out-of-core graph processing ***,GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future *** addition,GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access *** evaluation results show that GraphCP is 20.5×and 8.9×faster than two out-of-core graph processing systems GridGraph and GraphZ,and 3.5×and 1.7×faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

关键词： graph processing disk I/O concurrent jobs

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique Ahmad Muhammad Asim Department of computer science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of computer science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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Efficient Untargeted White-Box Adversarial Attacks Based on Simple Initialization

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Chinese Journal of Electronics 2024年第4期33卷 979-988页

作者： Yunyi ZHOU Haichang GAO Jianping HE Shudong ZHANG Zihui WU School of computer science and Technology Xidian University

Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points, thereby increasing the diversity of AEs. Given the non-convex nature of the loss function, employing randomness to augment the attack's success rate may lead to considerable computational overhead. To overcome this challenge,we introduce the one-hot mean square error loss to guide the initialization. This loss is combined with the strongest first-order attack, the projected gradient descent, alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process. Through experimental validation, we demonstrate that our method outperforms baseline attacks in constrained attack budget scenarios and regular experimental settings. This establishes it as a reliable measure for assessing the robustness of deep learning models. We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models. We aspire to provide valuable insights for the community in designing attack and defense mechanisms.

关键词： Deep learning Additives Perturbation methods Noise Mean square error methods Robustness Glass box

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of computer science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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The Squeeze & Excitation Normalization Based nnU-Net for Segmenting Head & Neck Tumors

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Chinese Journal of Electronics 2024年第3期33卷 766-775页

作者： Juanying XIE Ying PENG Mingzhao WANG School of computer science Shaanxi Normal University

Head and neck cancer is one of the most common malignancies in the *** propose SE-nnU-Net by adapting SE(squeeze and excitation) normalization into nnU-Net,so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific *** basic module referred to convolution-ReLU-SE is designed for *** the encoder it is combined with residual structure while in the decoder without residual *** loss function combines Dice loss and Focal *** specific data preprocessing and augmentation techniques are developed,and specific network architecture is ***,the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of *** SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges,respectively,using different experimental *** experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745,0.821,and 0.725 in terms of Dice,Precision,and Recall,respectively,while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95,*** SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors diagnoses.

关键词： Image segmentation Head Data preprocessing Artificial neural networks Network architecture Data models Neck

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EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

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computers, Materials & Continua 2024年第6期79卷 4021-4044页

作者： Zhenjiang Dong Xin Ge Yuehua Huang Jiankuo Dong Jiang Xu School of computer science Nanjing University of Posts and TelecommunicationsNanjing210023China School of computer Nanjing University of Information Science and TechnologyNanjing210044China

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of *** delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited ***,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving *** such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private *** its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained *** to enhance efficiency have been made,but scalability remains a *** the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising ***,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)*** the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU *** experimental results demonstrate the effectiveness of *** an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/***,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade *** solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same *** summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded

关键词： Secure two-party computation embedded GPU acceleration privacy-preserving machine learning edge computing

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The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

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computers, Materials & Continua 2024年第4期79卷 329-349页

作者： Qiang Fu JunWang Changfu Si Jiawei Liu College of computer science and Technology Shenyang University of Chemical TechnologyShenyang110142China College of computer science and Engineering Northeastern UniversityShenyang110169China

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security *** paper proposes a model for the industrial control *** includes a malware containment strategy that integrates intrusion detection,quarantine,and ***,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment *** addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction ***,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is *** otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control *** earlier the immunization of key nodes,the *** the time exceeds the threshold,immunizing key nodes is almost *** analysis provides a better way to contain the malware in the industrial control network.

关键词： Key nodes dynamic model industrial control network simulation

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Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

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computers, Materials & Continua 2024年第5期79卷 2647-2672页

作者： Muchang Rao Hang Qin School of computer science Yangtze UniversityJingzhou434000China

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational *** computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary ***,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable *** tackle this challenge,in this study,we integrate fog computing and cloud *** begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task ***,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task *** overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task *** proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting *** experimental results unequivocally demonstrate the superior performance of EHEO across all assessed ***,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

关键词： Artificial intelligence of things fog computing task scheduling equilibrium optimizer differential evaluation algorithm local search

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Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of computer science and Technology Beijing Institute of Technology Advanced Reasearch Institute of Multidisciplinary science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

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Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

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computers, Materials & Continua 2024年第2期78卷 2391-2418页

作者： Ruchun Jia Jianwei Zhang Yi Lin College of computer science Sichuan UniversityChengdu610065China

With the popularization of the Internet and the development of technology,cyber threats are increasing day by *** such as malware,hacking,and data breaches have had a serious impact on *** network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time *** security defense methods and tools have been unable to cope with the complex and changing network security *** paper proposes a machine-learning security defense algorithm based on metadata association *** control over unauthorized users through privacy,integrity,and *** user model is established and the mapping between the user model and the metadata of the data source is *** analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be *** and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense *** experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is *** method can effectively resist data intrusion and has high air traffic control *** can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

关键词： Data-oriented architecture metadata correlation features machine learning security defense data source integration

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