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Wavelets and Sparsity XVIII 2019

作者： Li, Yan-Ran Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong

ISBN: (数字)9781510629707

ISBN: (纸本)9781510629691

In this paper, a 3-dimension directional Haar tight framelet (3DHF) is used to detect the related features between coil images in parallel magnetic resonance imaging (pMRI). Such a Haar tight framelet has an extremely simple geometric structure in the sense that all the high-pass filters in its underlying filter bank have only two nonzero coefficients with opposite signs. A pMRI optimization model, which we coined 3DHF-SPIRiT, by regularizing the 3DHF features on the 3-D coil image data is proposed to reduce the aliasing artifacts caused by the downsampling operation in the k-space (Fourier) domain, which can be solved by alternating direction method of multipliers (ADMM) scheme. Numerical experiments are provided to demonstrate the superiority and efficiency of our 3DHF-SPIRiT model. © 2019 SPIE.

关键词： Magnetic resonance imaging

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A learning-based optimization algorithm: Image registration optimizer network

arXiv

引用

arXiv 2020年

作者： Wang, Jia Wang, Ping Li, Biao Gao, Yinghui Zhao, Siyi Key Laboratory of ATR College of Electronic Science and Technology National University of Defense Technology Changsha410073 China Domestic foundation Software Engineering Research Center College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Remote sensing image registration is valuable for image-based navigation system despite posing many challenges. As the search space of registration is usually non-convex, the optimization algorithm, which aims to search the best transformation parameters, is a challenging step. Conventional optimization algorithms can hardly reconcile the contradiction of simultaneous rapid convergence and the global optimization. In this paper, a novel learning-based optimization algorithm named Image Registration Optimizer Network (IRON) is proposed, which can predict the global optimum after single iteration. The IRON is trained by a 3D tensor (9×9×9), which consists of similar metric values. The elements of the 3D tensor correspond to the 9×9×9 neighbors of the initial parameters in the search space. Then, the tensor’s label is a vector that points to the global optimal parameters from the initial parameters. Because of the special architecture, the IRON could predict the global optimum directly for any initialization. The experimental results demonstrate that the proposed algorithm performs better than other classical optimization algorithms as it has higher accuracy, lower root of mean square error (RMSE), and more efficiency. Our IRON codes are available for further study1. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

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Research on enterprise service governance based on service mesh 6

Research on enterprise service governance based on service m...

引用

6th Annual International Conference on computer science and Applications, CSA 2020

作者： Tian, Yuchuan Hao, Haitian Xu, Xiaojian Liang, Bing School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Department of Computer Science University of Maryland College ParkMD20742 United States Software Development Center China CITIC Bank Beijing100010 China STATE GRID Beijing Changping Electric Power Company Changping district Beijing102200 China

With the technology development, enterprise applications are rapidly changing from the traditional centralized application architecture to the distributed, microservices architecture (MSA). However, with the further development of MSA, enterprises are facing new challenges while enjoying the convenience brought by microservices, e.g. how to manage a large number of services, how to quickly locate and troubleshoot these services, how to avoid the performance issues, etc. As the next generation of MSA, service mesh is attracting more and more attentions and can be used to solve above problems. On top of service mesh, this paper proposes one mechanism that largely customizes service mesh implementation technologies. Furthermore, we implement this mechanism in a real project of one local large financial company. The final result illustrates that the mechanism is not only feasible but also effective to solve aforementioned service governance problems. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Mesh generation

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DCS: A Policy Framework for the Detection of Correlated Data Streams 9th

DCS: A Policy Framework for the Detection of Correlated Data...

引用

9th International Workshop on Business Intelligence for the Real-Time Enterprise, BIRTE 2015, 10th International Workshop on Enabling Real-Time Business Intelligence, BIRTE 2016 and 11th International Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017 held in conjunction with the International Conference on Very Large Data Bases, VLDB 2017

作者： Alseghayer, Rakan Petrov, Daniel Chrysanthis, Panos K. Sharaf, Mohamed Labrinidis, Alexandros University of Pittsburgh PittsburghPA United States Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9783030241230

There is an increasing demand for real-time analysis of large volumes of data streams that are produced at high velocity. The most recent data needs to be processed within a specified delay target in order for the analysis to lead to actionable result. To this end, in this paper, we present an effective solution for detecting the correlation of such data streams within a micro-batch of a fixed time interval. Our solution, coined DCS, for Detection of Correlated Data Streams, combines (1) incremental sliding-window computation of aggregates, to avoid unnecessary re-computations, (2) intelligent scheduling of computation steps and operations, driven by a utility function within a micro-batch, and (3) an exploration policy that tunes the utility function. Specifically, we propose nine policies that explore correlated pairs of live data streams across consecutive micro-batches. Our experimental evaluation on a real world dataset shows that some policies are more suitable to identifying high numbers of correlated pairs of live data streams, already known from previous micro-batches, while others are more suitable to identifying previously unseen pairs of live data streams across consecutive micro-batches. © Springer Nature Switzerland AG 2019.

关键词： Artificial intelligence

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A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

arXiv

引用

arXiv 2022年

作者： Rehman, Abdur Abbas, Sagheer Khan, M.A. Ghazal, Taher M. Adnan, Khan Muhammad Mosavi, Amir School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Riphah School of Computing and Innovation Faculty of Computing Riphah International university Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Selangor Bangi43600 Malaysia Department of Software Gachon University Seongnam13120 Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava81107 Slovakia John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary Faculty of Civil Engineering TU-Dresden Dresden01062 Germany

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the e

关键词： Blockchain

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Green and Rapid and Instrumental One-Pot Method for the Synthesis of Imidazolines Having Potential Anti-SARS-CoV-2 Main Protease Activity

SSRN

引用

SSRN 2023年

作者： Azimi, Sabikeh Merza, Muna S. Ghasemi, Fathemeh Dhahi, Hasan Ali Baradarbarjastehbaf, Farid Moosavi, Mehdi Kargar, Pouya Ghamari Len, Christophe Department of Chemistry Faculty of Sciences University of Birjand Birjand97175-615 Iran Prosthetic dental Techniques Department Al-Mustaqbal University College Babylon51001 Iraq MSC in Software Engineering Kowsar The Institute of Higher Education Ministry of Science Research and Technology Computer Engineering Department Qazvin Iran National University of Science and Technology Dhi Qar Iraq Faculty of Pharmacy Department of Pharmaceutical Technology and Biopharmacy University of Pécs Pécs Hungary Department of Chemistry Faculty of Chemistry Mazandaran University Babolsar Iran Chimie ParisTech PSL University CNRS Institute of Chemistry for Life and Health Sciences 11 rue Pierre et Marie Curie ParisF-75005 France Peoples Friendship University of Russia [RUDN University 6 Miklukho Maklaya str. Moscow117198 Russia

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is responsible for ongoing epidemics in humans and some other mammals and has been declared a public health emergency of international concern. In this project, several small non-peptide molecules were synthesized to inhibit the major proteinase (Mpro) of SARS-CoV-2 using rational strategies of drug design and medicinal chemistry. Mpro is a key enzyme of coronaviruses and plays an essential role in mediating viral replication and transcription in human lung epithelial and stem cells, making it an attractive drug target for SARS-CoV. The antiviral potential of imidazoline derivatives as inhibitors of (SARS-CoV-2) Mpro was evaluated using in-silico techniques such as molecular docking simulation, molecular dynamics (MD) and ADMET prediction. The docking scores of these imidazoline derivatives were compared to that of the N3 crystal inhibitor and showed that most of these compounds, particularly compound E07, interacted satisfactorily in the active site of the coronavirus and strongly interacted with the residues (Met 165, Gln 166, Met 165, His 41, and Gln 189). Furthermore, the results were confirmed by MD simulations after exposure to long-term MD simulations and ADMET predictions. © 2023, The Authors. All rights reserved.

关键词： Coronavirus

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Link-delay-aware reinforcement scheduling for data aggregation in massive IoT

TechRxiv

引用

TechRxiv 2021年

作者： Vo, Van-Vi Nguyen, Tien-Dung Le, Duc-Tai Kim, Moonseong Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electronics and Telecommunications Hanoi University of Science and Technology Ha Noi 10999 Viet Nam Department of IT Convergence Software Seoul Theological University Bucheon14754 Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme. © 2021, CC BY.

关键词： Wireless sensor networks

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Neural Image Completion Based on Label Differentiation

引用

Journal of Physics: Conference Series 2021年第1期1757卷

作者： Xianyi Zhu Zixiao Ye Guanghua Tan Sheng Duan Yaohui Liu Dong Liu College of Software and Communication Engineering Xiangnan University Chenzhou 423000 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China

Deep learning based image completion methods are generally based on three technologies, namely the auto encoder based method, the generation of adversarial networks based method and the recurrent network based method. However, many methods have very single results. In order to obtain the diversity of the completion results, this paper proposes a neural image completion method based on label differentiation, called LD-PICNet (Label Differentiation PICNet). This method can not only generate a complete image with clear and good semantic information, but also actively edit the tags on the generated results to maximize the diversity of the output. Specifically, this paper introduces an auxiliary classifier, which uses a single label of the image ground truth to reconstruct the image, and uses the label to actively increase the difference of the latent vectors during image completion to achieve variability of output. In addition, this paper also introduces a depth-weighted loss function based on information entropy. Different data sets are used to conduct experiments. The model's ability to complete different types of targets is tested. The results show that this method has the ability to generate diversified results, and it has higher clarity than the other methods.

关键词：

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Energy-Efficient Computation Offloading for Multimedia Workflows in Mobile Cloud Computing 13th

Energy-Efficient Computation Offloading for Multimedia Workf...

引用

13th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

作者： Huang, Tao Chen, Yi Xue, Shengjun Ji, Haojun Xue, Yuan Qi, Lianyong Xu, Xiaolong School of Computer Science and Technology Silicon Lake College Suzhou China Shanghai Jiading District Meteorological Bureau Jiading China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Information Science and Engineering Qufu Normal University Qufu China

ISBN: (纸本)9783030129705

In recent years, mobile cloud computing (MCC) is utilized to process multimedia workflows due to the limitation of battery capacity of mobile devices, which influences the experience of multimedia applications on the mobile devices. Computation offloading based on cloudlet is introduced as a novel paradigm to relieve the high latency which offloading computation to remote cloud causes. However, it is still a challenge for mobile devices to offload computation of multimedia workflows in cloudlet-based cloud computation environment to reduce energy consumption, which meets time constraints at the same time. In view of the challenge, an energy-efficient computation offloading method of multimedia workflow with multi-objective optimization is proposed in this paper. Technically, an offloading method based on cloudlet using Differential Evolution (DE) algorithm is proposed to optimize the energy consumption of the mobile devices with time constraints. Finally, massive experimental evaluations and comparison analysis validate the efficiency of our proposed method. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： computation offloading

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

引用

arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： Data visualization

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