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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Yong-Yeon Jo Sang-Wook Kim Moonjun Chung Hyunok Oh Department of Computer and Software Hanyang University Korea Division of Computer Science and Engineering Hanyang University Korea Department of Information Systems Hanyang University Korea

Due to an explosive growth of Internet applications, the amount of data has increased enormously. In order to store and process this big data more efficiently, a solid-state device (SSD) has replaced a hard disk drive (HDD) as a primary storage media. In spite of high internal bandwidth, SSD has its performance bottleneck on the host interface whose bandwidth is relatively low. To overcome the problem of performance bottleneck in big data processing, the notion of intelligent SSD (iSSD) was proposed to give computing power to SSD. However, its real implementation has not been provided to the public yet. In this paper, we are going to verify the potential of iSSD in handling data-intensive algorithms. To the end, we first develop an iSSD simulator and then evaluate the performance of data mining algorithms inside iSSD on the top of it in comparison with that by the host CPU. The results reveal that data mining with iSSD outperforms that with host CPUs up to around 300%.

关键词： Data mining Random access memory Itemsets Instruction sets Electronic mail Bandwidth Performance evaluation

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Fast prime generation algorithms using proposed GCD test on mobile smart devices

Fast prime generation algorithms using proposed GCD test on ...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Hosung Jo Heejin Park Embedded Software Research Center Hanyang University Seoul Korea Division of Computer Science and Engineering Hanyang University Seoul Korea

As mobile smart devices are widely used, mobile security becomes more and more important. However, the performance of these devices are not powerful enough to use the same security algorithms as PC's. Public key cryptosystem such as RSA needs big primes to enhance the security, however, a generating big primes takes a substantial time even on a PC. In this paper, we proposed two prime generation algorithms for mobile smart devices using GCD primality test. We analyzed and compared the running times of our algorithm with the widely used TD-MR combination on Samsung Galaxy Tab 10.1. The experimental results showed only a 2% error and our algorithm is about 20% faster than the TD-MR combination.

关键词： Tunneling magnetoresistance Mobile communication Random number generation Probabilistic logic Time measurement Algorithm design and analysis

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Her2 challenge contest: A detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues

arXiv

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arXiv 2017年

作者： Qaiser, Talha Mukherjee, Abhik Reddy Pb, Chaitanya Munugoti, Sai Dileep Tallam, Vamsi Pitkäaho, Tomi Lehtimäki, Taina Naughton, Thomas Berseth, Matt Pedraza, Aníbal Mukundan, Ramakrishnan Smith, Matthew Bhalerao, Abhir Rodner, Erik Simon, Marcel Denzler, Joachim Huang, Chao-Hui Bueno, Gloria Snead, David Ellis, Ian O. Ilyas, Mohammad Rajpoot, Nasir Department of Computer Science University of Warwick United Kingdom Department of Histopathology Division of Cancer and Stem Cells School of Medicine University of Nottingham United Kingdom Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati India Department of Computer Science Maynooth University Ireland NLP Logix LLC United States VISILAB E.T.S.I.I University of Castilla-La Mancha Ciudad Real Spain Department of Computer Science and Software Engineering University of Canterbury New Zealand Department of Statistics University of Warwick United Kingdom Computer Vision Group Friedrich Schiller University of Jena Germany MSD International GmbH Singapore Singapore Agency for Science Technology and Research Singapore Department of Pathology University Hospitals Coventry and Warwickshire United Kingdom Nottingham Molecular Pathology Node University of Nottingham United Kingdom

Aims Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. Methods and Results The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the "ground truth" (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. Conclusions This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring. Copyright © 2017, The Authors. All rights reserved.

关键词： Automation

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Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy

Deep convolutional neural networks for detecting secondary s...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongjian Li Dong Si Tao Zeng Shuiwang Ji Jing He Department of Computer Science Old Dominion University Norfolk Virginia Division of Computing and Software Systems University of Washington Bothell Bothell WA School of Electrical Engineering and Computer Science Washington State University Pullman WA

ISBN: (纸本)9781509016129

The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.

关键词： Proteins Convolution Image resolution Three-dimensional displays Deconvolution Protein engineering Computational modeling

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A patient-centric decision guidance system for detecting glycemia of diabetes patients

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International Journal of Applied Decision Sciences 2016年第4期9卷 366-399页

作者： Ngan, Chun-Kit Li, Lin Division of Engineering and Information Science Great Valley School of Graduate Professional Studies Pennsylvania State University 30 East Swedesford Road MalvernPA19355 United States Department of Computer Science and Software Engineering College of Science and Engineering Seattle University 901 12th Avenue Engr 526 SeattleWA98122 United States

We propose the development of a patient-centric decision guidance system for detecting glycemia of diabetes patients. The proposed approach combines the strengths of both domain-knowledge-based and machine-learning-based approaches to learn the detection parameters. The contributions of this work are four-fold: 1) develop the new extended glycemic expert query parametric estimation (G-EQPE) model and the sequential-parallel-modularised (SPM) architecture to learn the optimal hypo- and hyperglycemic detection parameters simultaneously with a lower computational cost;2) provide the user-friendly GUI tools and the well-defined JavaScript object notation (JSON) schemas for healthcare system specialists to maintain the system operations;3) enable the specialists to construct the customised JSONiq queries for data manipulation, data monitoring, parameter learning, and report generation;4) conduct the experimental study and present the superior detection results in terms of accuracy, sensitivity, and specificity by using the learned detection parameters (i.e., 99 mg/dL and 172 mg/dL). Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Learning systems

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Perceptional approach to design of industrial plant monitoring systems 8

Perceptional approach to design of industrial plant monitori...

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8th International Conference on Advances in computer-Human Interactions, ACHI 2015

作者： Göktürk, Mehmet Bakir, Mustafa Aydoǧan, Burak Aydin, Mehmet Department of Computer Engineering Gebze Institute of Technology Turkey Process Improvement and Software Division TÜPRAŞ Izmit Petroleum Refinery Turkey

ISBN: (纸本)9781612083827

In this paper, we have investigated the perceptional attitudes of a massive scale industrial plant staff towards process monitoring systems and focused on human factors that are influential in design of plant monitoring systems. The study is a part of a plant-wide monitoring system which is under development, aimed to help staff to monitor processes and plant performance in detail. The paper first gives focused introduction on large scale enterprise and plant monitoring and management information systems, then discusses human machine interaction relevance of these systems to the staff performance and perception. Examples from the literature and previous work are presented. Primary human factors in proactive monitoring and highly automated systems are briefly discussed. A design survey study follows the introduction and relevant literature sections. The survey is designed to collect perceptional status of the staff against such systems, including the perception of their current performance. Results are analyzed and discussed in order to enhance system design decisions in such plant monitoring systems. We believe that such perceptual studies, performed before attempting to implement large scale monitoring systems that are highly interactive to the existing staff, should be considered as essential part of the design process. The results of this study is being used as inputs in implementation of a recent petroleum plant monitoring system. Copyright © IARIA, 2015.

关键词： Automation

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Analysis and Detection of Anomalous Network Traffic

Analysis and Detection of Anomalous Network Traffic

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International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Hae-Duck J. Jeong Hyeonggeun Kim WonHwi Ahn Jung-Hee Oh Dawoon Lee Sang-Kug Ye Jongsuk R. Lee Dept. of Computer Software Korean Bible University Seoul South Korea Division of LBS Solution SK MNS Seoul South Korea Dept. of Computational Science & Engineering Korea Institutue of Science and Technology Information Daejeon South Korea

ISBN: (纸本)9781509009855

Rapid development of information and communications networks and the widespread distribution of smartphones have contributed to the steady increase in Internet utilization. This increase in Internet consumption has resulted in the creation of various services including web services, SNS (Social Networking Services), Internet banking, and remote processing systems, enhancing the quality of life globally. However, serious information security problems have surfaced alongside these services, leading to Internet privacy invasions and network attacks. This paper presents a process to detect anomalous traffic using selfsimilarity analysis in the ATMSim environment as a research method to resolve these problems. In order to measure anomalous traffic, normal traffic for each attack including ARP spoofing and DDoS was measured for 48 hours. Hadoop is employed to process the massive traffic data collected and MapReduce was used after storing the data in HDFS. The detection system ATMSim, which is a new platform operating on Hadoop, is used to identify anomalous traffic, and a comparative analysis of the normal and anomalous traffic was performed through a self-similarity analysis. The collected traffic was divided into four categories according to the attack method: normal LAN traffic, DDoS attack, ARP spoofing, and DDoS and ARP spoofing attacks. The anomalous traffic detection system ATMSim was used to simulate for each scenario and distinguish normal and anomalous traffic in order to determine whether real attacks can be effectively identified. Graphic and quantitative analyses based on the self-similarity estimation for the four different traffic types showed that, unlike normal traffic such as the LAN traffic, the graphic analysis revealed a burstiness phenomenon when anomalous traffic occurred and the self-similarity estimation values were also high.

关键词： computer crime Estimation Local area networks Internet Stochastic processes Real-time systems Analytical models

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The use of a smartphone based mobile application for analysing the batting backlift technique in cricket

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Cogent Medicine 2016年第1期3卷 Article: 1214338页

作者： Noorbhai, Habib noorbhaim@cput.ac.za Chhaya, Meraj Mahomed Anis merajchhaya@*** Noakes, Timothy timothy.noakes@uct.ac.za Faculty of Business Department of Sport Management Cape Peninsula University of Technology Cape Town South Africa Faculty of Science Academy of Computer Science and Software Engineering University of Johannesburg Johannesburg South Africa Faculty of Health Sciences Division of Exercise Science and Sports Medicine Department of Human Biology University of Cape Town Cape Town South Africa

The batting backlift technique has been shown to be a contributing factor to successful batsmanship. No study or platform has yet been conceptualised to specifically design a mobile application to analyse the batting technique of cricket batsmen, especially the batting backlift technique in cricket. The backlift in cricket software system was divided into three components: a frontal view interface, a lateral view interface, and a back-end system. Android, Open computer Vision (CV) and JavaScript were used for this particular project as they are both well documented and supported by a software development platform called Qt 5.3. The use of the backlift in cricket mobile application can be used to evaluate the backlift type of a batsman by analysing and tracking the bat position of batsmen in cricket. The development of the backlift in cricket mobile application is the first complimentary mobile application that can record and analyse the backlift of a batsman through a smartphone without the need for connectivity from parent software on computers. The mobile application provides real-time data that can be stored on the user’s phone and device for subsequent usage and analysis.

关键词： mobile application performance analysis cricket batting backlift technique coaching

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Robust control of flat systems using sliding mode approach

Robust control of flat systems using sliding mode approach

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American Control Conference

作者： Liming Chen Yingmin Jia Junping Du Seventh Research Division and the Center for Information and Control School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications 100876 China

ISBN: (纸本)9781467386838

In this paper, sliding mode approach is used to deal with the robust control of flat systems. Model of flat systems with disturbance and parametric uncertainty is established. A scheme to design sliding mode control laws is proposed to guarantee the robustness of the systems. For the case that the bounds of disturbance and parametric uncertainty are unknown, adaptive controllers are designed to estimate the bounds or unknown parameters. The stability is proved with Lyapunov method. Simulations of a magnetic levitation system are given to illustrate the effectiveness of the approach.

关键词： Sliding mode control Uncertainty Tracking Lyapunov methods Robustness Feedforward neural networks Adaptive control feedforward neural nets Variable structure systems Lyapunov methods Robust control adaptive control Magnetic levitation systems tracking Uncertainty Robustness Sliding mode FLAT adaptive controller disturbance

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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