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4th Euro-China Conference on Intelligent Data Analysis and applications, ECC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China School of Computer Science and Technology Harbin Institute of Technology - Shenzhen Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9783319685267

Recently, Yu et al. proposed a secure shared data integrity verification protocol called SDVIP 2 to ensure the integrity of outsourced file in the cloud. Unfortunately, we exploit the vulnerability of their protocol in this paper. We demonstrate an active adversary can modify the outsourced file such that the auditor is unable to detect in the auditing process. At the end of this paper, we also provide two suggestions to fix the proposed attack. © 2018, Springer International Publishing AG.

关键词： Cloud storage

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2022 Review of Data-Driven Plasma science

arXiv

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Ultra-sharp nanowire arrays natively permeate, record, and stimulate intracellular activity in neuronal and cardiac networks

arXiv

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arXiv 2021年

作者： Liu, Ren Lee, Jihwan Tchoe, Youngbin Pre, Deborah Bourhis, Andrew M. D'Antonio-Chronowska, Agnieszka Robin, Gaelle Lee, Sang Heon Ro, Yun Goo Vatsyayan, Ritwik Tonsfeldt, Karen J. Hossain, Lorraine A. Phipps, M. Lisa Yoo, Jinkyoung Nogan, John Martinez, Jennifer S. Frazer, Kelly A. Bang, Anne G. Dayeh, Shadi A. Integrated Electronics and Biointerfaces Laboratory Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive San DiegoCA92093 United States Conrad Prebys Center for Chemical Genomics Sanford Burnham Prebys Medical Discovery Institute 10901 North Torrey Pines Road San DiegoCA92037 United States Department of Pediatrics University of California San Diego 9500 Gilman Drive San DiegoCA92093 United States Center for Reproductive Science and Medicine Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego 9500 Gilman Drive San DiegoCA92093 United States Center for Integrated Nanotechnologies Los Alamos National Laboratory Los AlamosNM87545 United States Center for Integrated Nanotechnologies Sandia National Laboratories AlbuquerqueNM87185 United States Center for Materials Interfaces in Research and Applications Department of Applied Physics and Materials Science Northern Arizona University 624 S. Knoles Dr FlagstaffAZ86011 United States

Intracellular access with high spatiotemporal resolution can enhance our understanding of how neurons or cardiomyocytes regulate and orchestrate network activity, and how this activity can be affected with pharmacology or other interventional modalities. Nanoscale devices often employ electroporation to transiently permeate the cell membrane and record intracellular potentials, which tend to decrease rapidly to extracellular potential amplitudes with time. Here, we report innovative scalable, vertical, ultra-sharp nanowire arrays that are individually addressable to enable long-term, native recordings of intracellular potentials. We report large action potential amplitudes that are indicative of intracellular access from 3D tissue-like networks of neurons and cardiomyocytes across recording days and that do not decrease to extracellular amplitudes for the duration of the recording of several minutes. Our findings are validated with cross-sectional microscopy, pharmacology, and electrical interventions. Our experiments and simulations demonstrate that individual electrical addressability of nanowires is necessary for high-fidelity intracellular electrophysiological recordings. This study advances our understanding of and control over high-quality multi-channel intracellular recordings, and paves the way toward predictive, high-throughput, and low-cost electrophysiological drug screening platforms. Copyright © 2021, The Authors. All rights reserved.

关键词： Electrophysiology

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A scalable deep learning platform for identifying geologic features from seismic attributes

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Leading Edge 2017年第3期36卷 249-256页

作者： Huang, Lei Dong, Xishuang Clee, T. Edward Prairie View AandM University Department of Computer Science United States Prairie View AandM University Department of Electrical and Computer Engineering United States TEC Applications Analysis United States

The modern requirement for analyzing and interpreting ever-larger volumes of seismic data to identify prospective hydrocarbon prospects within stringent time deadlines represents an ongoing challenge in petroleum exploration. To provide a computer-based aid in addressing this challenge, we have developed a "big data" platform to facilitate the work of geophysicists in interpreting and analyzing large volumes of seismic data with scalable performance. We have constructed this platform on a modern distributed-memory infrastructure, providing a customized seismic analytics software development toolkit, and a Web-based graphical workflow interface along with a remote 3D visualization capability. These support the management of seismic data volumes, attributes processing, seismic analytics model development, workflow execution, and 3D volume visualization on a scalable, distributed computing platform. Early experiences show that computationally demanding deep learning methods such as convolutional neural networks (CNN) provide improved results over traditional methods such as support vector machines (SVMs) and logistic regression for identifying geologic faults in 3D seismic volumes. Our experiments show encouraging accuracy in identifying faults by combining CNN and traditional machine learning models with a variety of seismic attributes, and the platform is able to deliver scalable performance. © 2017 by The Society of Exploration Geophysicists.

关键词： Neural networks

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Extra-Functional Properties Driven Component Selection for Component based System

Extra-Functional Properties Driven Component Selection for C...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Prasenjit Banerjee Narayan Debnath Anirban Sarkar Dept. of Computer Science and Engineering National Institute of Technology Durgapur India International Society for Computers and Their Applications Winona MN USA

Currently, software companies are considering Component-based software (CBS) along with extra-functional properties (EFPs) with prime importance to improve the quality of the system design and development. It reduces their time and cost for the system development with the necessary focus on quality measurements. The quality of the final system can be measured efficiently by adapting the EFPs measurement at the time of system design. However, this adaptation is a complex engineering task, as the priorities of EFPs will differ from domain to domain. The identification of the correct set of extrafunctional properties and the methods of their adaptation into the CBS are very important in this regard. Once the correct set of extra functional properties is identified, it can be further used to select the correct set of components in component-based system. This paper has proposed a methodology for selecting the correct set of components using a selected set of extra-functional properties. It facilitates the system designers to identify properly the components for system design and make fit for reuse. It also reduces the time and cost of design.

关键词： Component architectures Proposals Software System analysis and design Unified modeling language Companies Time measurement

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Network Intrusion Detection Mechanisms Using Outlier Detection

Network Intrusion Detection Mechanisms Using Outlier Detecti...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Ch. Nagamani Suneetha Chittineni Department of Computer Science and Engineering Andhra Loyola Institute of Engg &Tech Guntur Vijayawada Department of Computer Applications RVR & JC College of Engg & Tech Chowdavaram Guntur

The recognition of intrusions has increased impressive enthusiasm for information mining with the acknowledgment that anomalies can be the key disclosure to be produced using extensive network databases. Intrusions emerge because of different reasons, for example, mechanical deficiencies, changes in framework conduct, fake conduct, human blunder and instrument mistake. Surely, for some applications the revelation of Intrusions prompts more intriguing and helpful outcomes than the disclosure of inliers. Discovery of anomalies can prompt recognizable proof of framework blames with the goal that executives can take preventive measures previously they heighten. A network database framework comprises of a sorted out posting of pages alongside programming to control the network information. This database framework has been intended to empower network operations, oversee accumulations of information, show scientific outcomes and to get to these information utilizing networks. It likewise empowers network clients to gather limitless measure of information on unbounded territories of utilization, break down it and return it into helpful data. Network databases are ordinarily used to help information control utilizing dynamic capacities on sites or for putting away area subordinate data. This database holds a surrogate for each network route. The formation of these surrogates is called ordering and each network database does this errand in an unexpected way. In this paper, a structure for compelling access control and Intrusion Detection using outliers has been proposed and used to give viable Security to network databases. The design of this framework comprises of two noteworthy subsystems to be specific, Access Control Subsystem and Intrusion Detection Subsystem. In this paper preprocessing module is considered which clarifies the preparing of preprocessing the accessible information. And rain forest method is discussed which is used for intrusion detection.

关键词： Forestry Databases Vegetation Intrusion detection XML Conferences

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Network analysis of synonymous codon usage

arXiv

引用

arXiv 2019年

作者： Newaz, Khalique Wright, Gabriel Piland, Jacob Li, Jun Clark, Patricia Emrich, Scott Milenković, Tijana Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Interdisciplinary Center for Network Science and Applications University of Notre Dame Notre DameIN46556 United States Eck Institute for Global Health University of Notre Dame Notre DameIN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States Department of Chemistry and Biochemistry University of Notre Dame Notre DameIN46556 United States Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionary conserved. Analyses of positions of rare codons in proteins' 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. We analyze a protein set recently annotated with codon usage information, considering non-redundant proteins with sufficient structural information. We model the proteins' structures as networks and study potential differences between network positions of amino acids encoded by evolutionary conserved rare, evolutionary non-conserved rare, and commonly used codons. In 84% of the proteins, at least one of the three codon categories occupies significantly more or less network-central positions than the other codon categories. Different protein groups showing different codon centrality trends (i.e., different types of relationships between network positions of the three codon categories) are enriched in different biological functions, implying the existence of a link between codon usage, protein folding, and protein function. Copyright © 2019, The Authors. All rights reserved.

关键词： Protein folding

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Enhancing Internet of Battle Things using Ultrasonic assisted Non-Destructive Testing (Technical solution)

Enhancing Internet of Battle Things using Ultrasonic assiste...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： P. Radha N. Selvakumar J. Raja Sekar J.V. Johnsonselva Department of Computer Applications Mepco Schlenk Engineering College Sivakasi Tamilnadu India Department of Mechanical Engineering Mepco Schlenk Engineering College Sivakasi Tamilnadu India Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi Tamilnadu India

ISBN: (纸本)9781538615096

The subsystem of IoMT (Internet of Military of Things) called IoBT (Internet of Battle of Things) is the major resource of the military where the various stack holders of the battlefield and different categories of equipment are tightly integrated through the internet. The proposed architecture mentioned in this paper will be helpful to design IoBT effectively for warfare using irresistible technologies like information technology, embedded technology, and network technology. The role of Machine intelligence is essential in IoBT to create smart things and provide accurate solutions without human intervention. Non-Destructive Testing (NDT) is used in Industries to examine and analyze the invisible defects of equipment. Generally, the ultrasonic waves are used to examine and analyze the internal defects of materials. Hence the proposed architecture of IoBT is enhanced by ultrasonic based NDT to study the properties of the things of the battlefield without causing any damage.

关键词： Acoustics Probes computer architecture Intelligent sensors Testing Military computing

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Adaptive Neural Dynamic Surface Control for Flexible Joint Manipulator with Prescribed Performance 29

Adaptive Neural Dynamic Surface Control for Flexible Joint M...

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第29届中国控制与决策会议

作者： Min Wang Huiping Ye School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

ISBN: (纸本)9781509046584

This paper focuses on adaptive neural control for a class of flexible joint manipulator with unknown dynamics under the output tracking error *** facilitate the design of control law,the constrained tracking error is transformed into unconstrained variable by introducing a performance function,the unknown dynamics of manipulator are accurately approximated by radial basis function(RBF) neural network(NN).Then,a novel adaptive neural control scheme is proposed using the derivative of the filter's output as the input of NN instead of traditional intermediate *** to structure features of the considered flexible joint manipulator,the proposed control scheme not only decreases the dimension of NN inputs,but also reduces the number of NN ***,it can be verified that all the signals in the closed-loop system are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero with the prescribed *** results on a single-link flexible joint manipulator are given to illustrate the effectiveness of the proposed scheme.

关键词： Adaptive Neural Control Dynamic Surface Control Prescribed Performance Flexible Joint Manipulator

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Neural Control of Robotic Manipulator with Guaranteed Tracking Precision and Velocity Constraints 36

Neural Control of Robotic Manipulator with Guaranteed Tracki...

引用

第36届中国控制会议

作者： Min Wang Yanwen Zhang School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

ISBN: (纸本)9781538629185

This paper focuses on adaptive neural control design for n-link robot manipulator systems in the presence of tracking error and velocity state constraints.A performance function is utilized to characterize the prescribed performance of the tracking error.A new transformed function is used to guarantee that tracking errors satisfy the prescribed performance and the velocity states are not to violate their *** designed transformed function is not only of simple structure,but also overcomes the possible high-gain control problem caused by the existing transformed ***,a novel adaptive neural control scheme is proposed by combining independently Lyapunov functions *** proposed scheme guarantees all closed-loop signals are uniformly ultimately bounded,the output tracking errors satisfy the prescribed transient and steady-state tracking performances,and the velocity states are ensured to remain in the predefined bounded *** studies on a 2-link robot are performed and the effectiveness of the proposed scheme is illustrated by simulation results.

关键词： Adaptive Neural Control Tracking Error Constraints State Constraints Performance Transformation Robot Manipulators

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