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How Meta-heuristic Algorithms Contribute to Deep Learning in the Hype of Big Data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th international conference on Advanced computing, Networking and informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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Mobile Edge computing: progress and Challenges

Mobile Edge Computing: Progress and Challenges

引用

ieee international conference on Mobile Cloud computing, Services, and Engineering (MobileCloud)

作者： Hongxing Li Guochu Shou Yihong Hu Zhigang Guo Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Mobile Edge computing (MEC) provides mobile and cloud computing capabilities within the access network, and aims to unite the telco and IT at the mobile network edge. this paper presents an investigation on the progress of MEC, and proposes a platform, named WiCloud, to provide edge networking, proximate computing and data acquisition for innovative services. Furthermore, the open challenges that must be addressed before the commercial deployment of MEC are discussed.

关键词： Mobile communication Cloud computing Mobile computing Servers Wireless communication Smart phones Wireless sensor networks

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Social Engineering Attack Strategies and Defence Approaches

Social Engineering Attack Strategies and Defence Approaches

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international conference on Future Internet of things and Cloud (FiCloud)

作者： Ibrahim Ghafir Vaclav Prenosil Ahmad Alhejailan Mohammad Hammoudeh FI Masaryk University School of Computing Manchester Metropolitan University Faculty of Informatics School of Computing Manchester School of Computing Manchester Masaryk University Brno Czech Republic Faculty of Informatics School of Computing Manchester School of Computing Manchester Metropolitan University Manchester UK

ISBN: (纸本)9781509040537

this paper examines the role and value of information security awareness efforts in defending against social engineering attacks. It categories the different social engineering threats and tactics used in targeting employees and the approaches to defend against such attacks. While we review these techniques, we attempt to develop a thorough understanding of human security threats, with a suitable balance between structured improvements to defend human weaknesses, and efficiently focused security training and awareness building. Finally, the paper shows that a multi-layered shield can mitigate various security risks and minimize the damage to systems and data.

关键词： Companies Psychology Social network services Training

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An adaptive information-theoretic approach for identifying temporal correlations in big data sets 4

An adaptive information-theoretic approach for identifying t...

引用

4th ieee international conference on Big Data, Big Data 2016

作者： Ho, Nguyen Vo, Huy Vu, Mai Dipartimento di Elettronica Informazione e Bioingegneria Politécnico di Milano Milan Italy Center for Urban Science and Progress New York University New York United States Department of Computer Science City College of New York New York United States Department of Electrical and Computer Engineering Tufts University MedfordMA United States

ISBN: (纸本)9781467390040

In the past two decades, new developments in computing, sensing and crowdsourced data have resulted in an explosion in the availability of quantitative information. the possibilities of analyzing this so-called 'big data' to inform research and the decision-making process are virtually endless. In general analyses have to be done across multiple data sets in order to bring out the most value of big data. A first important step is to identify temporal correlations between data sets. Given the characteristics of big data in term of volume and velocity, techniques that identify correlations not only need to be scalable, but also need to help users in ordering the correlation across temporal resolutions so that they can focus on important relationships. there is a large body of work in this area, however, most of them either only deal with small data sets, using a fixed temporal resolution, or does not provide a quantifiable measure of a correlation significance. In this paper, we present a method based on mutual information to identify correlations in large data sets. Discovered correlations are suggested to users in an order based on their significance. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability using big data platforms. We also provide a comprehensive evaluation using real-world data sets from NYC Open Data, and compare our findings against a recent study. © 2016 ieee.

关键词： Big data

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Modeling and analysis of complex computer systems-the MTCCS approach

Modeling and analysis of complex computer systems-the MTCCS ...

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ieee international conference on Engineering of Complex Computer Systems

作者： H. Toetenel R.L. Spelberg S. Stuurman J. van Katwijk Faculty of Technical Mathematics and Informatics Delft University of Technnology Delft Netherlands

the paper presents results from work in progress on finding a method for formal specification and verification of real time concurrent systems that incorporate a non trivial data component. We have extended Timed CCS, a timed CCS variant with a model oriented data language based on VDM. the semantics of the extension, called MTCCS is expressed in a combination of denotational and operational style. We show how verification of temporal logic properties based on symbolic model checking can be made possible for such a combination notation.

关键词： Real time systems Carbon capture and storage Formal specifications Logic Protocols Mathematics informatics Paper technology Concurrent computing Distributed computing

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the education of leaders in the face of 4th digital revolution 11

The education of leaders in the face of 4th digital revoluti...

引用

11th international Multi-conference on Complexity, informatics and Cybernetics, IMCIC 2020

作者： Dobrowolska, Malgorzata Deja, Agnieszka Silesian University of Technology International Center for Interdisciplinary Research Gliwice44-100 Poland

ISBN: (纸本)9781950492329

the fourth digital revolution forces modern education ofpersonnel and leaders responsible for introduction of newtechnologies 4.0. the article presents a new vision of educationof engineers responsible for technologies-automatization,robotization, computerization taking into account the standardsof higher education and the needs of the market of the so-calledfast and short forms of education-as well as the content ofsoftware, which next to professional knowledge-the technologyof Industry 4.0 also takes into consideration the area ofmanagerial competences, managerial skills and specialisticinterdisciplinary knowledge of persons holding managerialfunctions both in the sector of small and medium enterprises andin large organizations. Within the framework of practicalimplications, the model solution MBA Master of BusinessAdministration program of profile Industry 4.0 is presented. theprogram was performed under one test group in SUT in Poland,financed from the funds of the Ministry of Science and HigherEducation. Copyright 2020. © by the international Institute of informatics and Systemics.

关键词： Industry 4.0

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Notice of Violation of ieee Publication Principles: A Music Identification System Based on Audio Fingerprint

Notice of Violation of IEEE Publication Principles: A Music ...

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international conference on Applied computing and Information Technology/international conference on Computational Science and Intelligence (ACIT-CSI)

作者： Yong Fan Shuang Feng School of Computer Communication University of China Beijing China

Notice of Violation of ieee Publication Principles "A Music Identification System Based On Audio Fingerprint" by Yong Fan, Shuang Feng in the 4th international conference on Applied computing and Information Technology/3rd international conference on Computational Science/Intelligence and Applied informatics/1st international conference on Big Data, Cloud computing, Data Science and Engineering, December 2016, pp. 363-367 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of ieee's Publication Principles. this paper duplicates content from the source cited below. the original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. "Audio Fingerprinting with Python and Numpy" by Will Drevo on http://***/fingerprinting-and-audio-recognition-with-python/ November 2013 Music recognition is more and more widely used in our life. this paper presents a music identification system based on audio fingerprinting. In this paper, a set of music recognition system based on the audio fingerprint is designed, which is based on the audio fingerprint, and constructs the audio fingerprint database to realize the function application of the target music recognition. the system firstly encodes and decodes the audio file and then obtains the time-frequency spectrogram. then extracts the peak feature points and maps the samples to the *** the fingerprint database and identifies the targets in the last. the experimental results show that the system has strong robustness and high recognition rate in music recognition.

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An Analysis of Scientific Production in Big Data Knowledge Domain on Google Books, YouTube and ieee Explore® Digital Library 20

An Analysis of Scientific Production in Big Data Knowledge D...

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4th international conference on Cloud and Big Data computing, ICCBDC 2020

作者： Gotsev, Lyubomir Shoikova, Elena University of Library Studies and Information Technologies Tsarigradsko Shose Blvd 119 Tsarigradsko Shose Blvd. Sofia1784 Bulgaria

ISBN: (纸本)9781450375382

the paper aims to reveal the current state of book, video and article production in Big Data Knowledge Domain on particular platforms by examining the capabilities of Application Programming Interface (API) technology in conducting scientific data-driven research. Queries append public records from Google Books, YouTube and ieee Explore® Digital Library to two research paradigms (sets of data sets): Big Data (incl. Analysis, Engineering, Architecture, Governance, Management, Frameworks) and Big Data interdisciplinary fields (Data Science, Data Mining, Deep Learning, Machine Learning, Artificial Intelligence). Metadata from more than 25 000 conference papers, 2000 books over the past 50 years, and 4 000 videos for the last 12 years, matching the searching criteria, has been stored and analyzed. the outputs are summarized in statistics, forecasting, rating key findings by various attributes: title, author, publisher, research field, category, subject, publication year, description, view count, and a combination of mentioned metadata in cross-tables. Nearly a half of billion video views;a half of million article reference count;a twofold increase in the number of papers in Machine learning over past three years compared to the total number in the same field for entire 1988-2016 period;1:2:12 overall books-to-videos-to conference papers ratio;61.3% of last year's video production just in a month (Jan-2020);the earliest found usage of "Artificial Intelligence"expression in a printed law document dated 1848 are few curious examples of analysis findings. the paper presents non-commercial research and retrieved data is collected entirely from public records. © 2020 ACM.

关键词： Big data

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An Adaptive Information-theoretic Approach for Identifying Temporal Correlations in Big Data Sets

An Adaptive Information-Theoretic Approach for Identifying T...

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4th ieee international conference on Big Data (Big Data)

作者： Nguyen Ho Huy Vo Mai Vu Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy NYU Ctr Urban Sci & Progress New York NY USA CUNY City Coll Dept Comp Sci New York NY USA Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

ISBN: (纸本)9781467390057

In the past two decades, new developments in computing, sensing and crowdsourced data have resulted in an explosion in the availability of quantitative information. the possibilities of analyzing this so-called "big data" to inform research and the decision-making process are virtually endless. In general analyses have to be done across multiple data sets in order to bring out the most value of big data. A first important step is to identify temporal correlations between data sets. Given the characteristics of big data in term of volume and velocity, techniques that identify correlations not only need to be scalable, but also need to help users in ordering the correlation across temporal resolutions so that they can focus on important relationships. there is a large body of work in this area, however, most of them either only deal with small data sets, using a fixed temporal resolution, or does not provide a quantifiable measure of a correlation significance. In this paper, we present a method based on mutual information to identify correlations in large data sets. Discovered correlations are suggested to users in an order based on their significance. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability using big data platforms. We also provide a comprehensive evaluation using real-world data sets from NYC Open Data, and compare our findings against a recent study.

关键词： temporal correlation mutual information Big Data adaptive sliding window streaming

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A Survey on Network Security Monitoring Systems

A Survey on Network Security Monitoring Systems

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ieee international conference on Future Internet of things and Cloud Workshops (FiCloudW)

作者： Ibrahim Ghafir Vaclav Prenosil Jakub Svoboda Mohammad Hammoudeh FI Manchester Metropolitan University Faculty of Informatics Masaryk University Brno Czech Republic School of Computing Manchester Metropolitan University Manchester UK

Network monitoring is a difficult and demanding task that is a vital part of a network administrator's job. Network administrators are constantly striving to maintain smooth operation of their networks. If a network were to be down even for a small period of time, productivity within a company would decline, and in the case of public service departments the ability to provide essential services would be compromised. there are several approaches to network security monitoring. this paper provides the readers with a critical review of the prominent implementations of the current network monitoring approaches.

关键词： Monitoring Decoding Protocols Security Pattern matching Communication networks Inspection

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