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Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

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npj Computational Materials 2021年第1期7卷 867-877页

作者： Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park Gyuwon Kim Seungchul Lee Hyoung Seop Kim Department of Materials AI&Big-Data Korea Institute of Materials Science(KIMS)Changwon-siRepublic of Korea Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Department of Materials Science and Engineering Pohrepresent the average grainang University of Science and Technology(POSTECH)PohangRepublic of Korea Graduate School of Artificial Intelligence Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Graduate Institute of Ferrous Technology Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea

The digitized format of microstructures,or digital microstructures,plays a crucial role in modern-day materials ***,the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the *** resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical ***,a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution(LR)microstructure data for microstructure characterization and finite element(FE)mechanical *** characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution(HR)data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution,which can be only partially considered or entirely disregarded in LR data-based analysis.

关键词： microstructure Microstructure analysis

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Differentially Private Frequency Tables Based on Random Sampling

Differentially Private Frequency Tables Based on Random Samp...

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IEEE International Conference on Big data

作者： Takumi Sugiyama Kazuhiro Minami Graduate school of Economics Chuo University Tokyo Japan Department of Statisticl Data Science Institute of Statistical Mathematics Tokyo Japan

Previous research proposes a random sampling-based techniques [4], [16] to produce differentially private k anonymized data based on random sampling. However, their approach considers all variables in an original dataset as quasi-identifiers with full domain transformation to guarantee differential privacy. Since all the equivalence classes in anonymized data contain identical records with the same set of attribute values, that anonymized data is not useful for the analysis of microdata. However, we consider random sampling a promising approach to guaranteeing differential privacy on frequency tables since k-anonymized data in which all variables are treated as quasi-identifiers is equivalent to a frequency *** this paper, we evaluate the feasibility of producing deferentially private frequency tables based on random sampling. Our experiments show that we can produce frequency tables of high data utility when we set small $\epsilon$ to choose a low sampling rate for a dataset with a large number of records.

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Approaching Centralized Multi-cell Coordinated Beamforming with Limited Backhaul Signaling

Approaching Centralized Multi-cell Coordinated Beamforming w...

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IEEE International Conference on Communications (ICC)

作者： Tenghao Cai Songyang Ge Yanqing Xu Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Decentralized multi-cell coordinated beamforming (D-MCBF) is a promising technique to improve the system spectral efficiency, but it usually requires the base stations (BSs) to frequently exchange large amounts of information in order to approach the centralized MCBF solution. However, in practical environments with time-varying channels and limited backhaul bandwidth, frequent information exchange causes delays and thus the existing D-MCBF methods suffer significant performance loss. In this paper, we aim to design a D-MCBF method that can approach the centralized MCBF solution while with only a few inter-BS information exchanges. By assuming that the BSs can exchange the interference channel powers, we firstly formulate a virtual power control based sum rate maximization (VPC-SRM) problem where each BS individually optimizes the beamformers for its served users and at the same time “virtually” optimizes the transmission powers of other BSs. Thus, the VPC-SRM problem is a surrogate of the centralized SRM problem and can be efficiently handled by existing iterative algorithms. To provide a good initial point, we further propose a fully decentralized leakage-based SRM formulation. Simulation results show that the proposed D-MCBF algorithms can perform closely with the centralized method with only two times of information exchange even when the channel is time-varying.

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A subjective questionnaire system based on privacy-friendly incentive protocols 2

A subjective questionnaire system based on privacy-friendly ...

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2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Liu, Xin Sun, Shiwen School of Information Engineering Shandong Youth University of Political Science Shandong Jinan250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Shandong Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Shandong Jinan250103 China

ISBN: (纸本)9781510666665

Questionnaire survey systems are a kind of computer-aided survey tool, which are helpful for the questionnaire publisher to collect the opinions and appeals of the respondents. Nevertheless, it is difficult for the questionnaire publisher to solve tricky questions such as respondents’ inactivity in answering questions, especially in the subjective questionnaire. The paper extends the privacy-friendly incentive protocols of Camenisch et al. based on the technique of signatures of knowledge and the Au-Susilo-Mu signature and put forward a subjective questionnaire system oriented to professional course teaching. The new system controls the range of participants by specific attributes. It also allows participants to remain anonymous at all phases, so they can truly feedback on their subjective evaluations. Moreover, when students are not within the scope of the survey, they are encouraged to help to review the collected subjective questionnaire answers. Furthermore, the new system introduces a token mechanism to stimulate students’ participation. Security analysis shows that the new system satisfies correctness, balance, anonymity, and unlinkability. © 2023 SPIE.

关键词： Network security

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A LEGAL PREDICTION MODEL USING SUPPORT VECTOR MACHINE AND K-MEANS CLUSTERING ALGORITHM FOR PREDICTING JUDGEMENTS AND MAKING DECISIONS

A LEGAL PREDICTION MODEL USING SUPPORT VECTOR MACHINE AND K-...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Rani, A. Jaya Mabel Bharathwaj, K.S. Swaroopan, N.M. Jothi Kumar, K. Hari Geetha, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of EEE R.M.K. Engineering College Chennai India Department of Computer Science St Jude's College KanyaKumari District Tamil Nadu Thoothoor India Department of EEE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences ThandalamTamil Nadu Chennai India

ISBN: (纸本)9798350305708

The legal community has displayed significant interest in harnessing predictive models to anticipate case outcomes, providing judges and attorneys with valuable tools to enhance their decision-making processes. These models offer the potential to expedite choices, offer support for arguments, and strengthen defence strategies. However, accurately predicting legal rulings and case results is a complex endeavour, involving multifaceted stages such as sourcing appropriate bulk case documents, extracting and refining data, and navigating the intricate nature of legal paper complexities. In this innovative machine learning-powered legal prediction model is introduced, named "Legal Acumen." Beyond predictions, this model also offers informed decisions based on the appropriate laws, providing an added layer of insight and value. "Legal Acumen" not only generates predictions about judgments from the Supreme Court but also incorporates a feature that aids end users: upon uploading a new case description document to a designated folder, the system rapidly comprehends its contents and generates both a prediction and a legally informed decision. The understanding indicates that "Legal Acumen" stands among the pioneering legal prediction models striving to anticipate decisions from the Indian Supreme Court. For automatic prediction of Legal Acumen used different types of machine learning algorithms such as Support vector machine, Decision Tree algorithm, Etc., But this paper proposed two-step process. First step used Support vector machine for the prediction of judgement. Then the second step explained about the punishment of the criminal based on K-Means Clustering algorithm. © 2023 IEEE.

关键词： Acumen appeals court decisions Indian judgments prediction supreme

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

arXiv

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arXiv 2024年

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali James Watt School of Engineering University of Glasgow Glasgow United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Clemson University United States

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency allocation

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Quantifying the Benefits of Implementing Big data Analytics on Consumers’ Online Purchase Intentions in e-Commerce

Quantifying the Benefits of Implementing Big Data Analytics ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Wei, Justin Ang Kang Mansor, Mahayaudin M. Derasit, Zuraidah Bakar, Mohd Aftar Abu Ul-Saufie, Ahmad Zia Hamzah, Firdaus Mohamad Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331530303

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth. ©2024 IEEE.

关键词： Marketplaces

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

arXiv

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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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