WorldWideScience

Sample records for estonian-language computer network

  1. Online Estonian Language Learning

    Science.gov (United States)

    Teral, Maarika; Rammo, Sirje

    2014-01-01

    This presentation focuses on computer-assisted learning of Estonian, one of the lesser taught European languages belonging to the Finno-Ugric language family. Impulses for this paper came from Estonian courses that started in the University of Tartu in 2010, 2011 and 2012. In all the courses the students gain introductory knowledge of Estonian and…

  2. [Mati Erelt. Estonian Language] / Katrin Hiietamm

    Index Scriptorium Estoniae

    Hiietamm, Katrin

    2004-01-01

    Arvustus: Estonian language / [Estonian Academy of Sciences] ; edited by Mati Erelt.Tallinn : Teaduste Akadeemia Kirjastus, 2003. 412, [1] lk. : ill., kaart. (Linguistica Uralica. Supplementary series, 0868-4731 ; vol. 1)

  3. computer networks

    Directory of Open Access Journals (Sweden)

    N. U. Ahmed

    2002-01-01

    Full Text Available In this paper, we construct a new dynamic model for the Token Bucket (TB algorithm used in computer networks and use systems approach for its analysis. This model is then augmented by adding a dynamic model for a multiplexor at an access node where the TB exercises a policing function. In the model, traffic policing, multiplexing and network utilization are formally defined. Based on the model, we study such issues as (quality of service QoS, traffic sizing and network dimensioning. Also we propose an algorithm using feedback control to improve QoS and network utilization. Applying MPEG video traces as the input traffic to the model, we verify the usefulness and effectiveness of our model.

  4. Väliseestlased ja nende keel. Pidepunkte uurimisloost / A study of the Estonian language in diaspora

    Directory of Open Access Journals (Sweden)

    Jüri Viikberg

    2012-01-01

    Full Text Available The varieties of the Estonian language outside Estonia, differing from standard Estonian spoken in Estonia, are regional and generally oral language variants, influenced by local factors and variable intergenerational use. Containing loans from the dominant language, the oral language of the older generation still retains features either redundant or marginal in the current Estonian language geographic area. The first written documents on expatriate Estonians date back to the 19th century, but it was only after the Republic of Estonia was established in 1918 that a wider interest was taken in compatriots living abroad. In 1928, the Expatriate Estonian Society (Välis-Eesti Ühing was founded and the Expatriate Estonian Congress (Välis-Eesti kongress started to be held every five years. After Estonia was annexed by the Soviet Union in 1940, the word väliseestlane ’expatriate Estonian’ was used only in connection with Estonians living outside the Soviet Union. In the 1950s and 1960s, linguists became specifically interested in the Estonian settlements of Caucasus, Siberia and Ussuriland. Scholars, hoping to find in Siberia or Caucasus the archaic language of former settlers still alive, discovered that vernacular Estonian was not influenced so much by archaisms (caused by separation from the homeland as influences from long-term contact with other languages.Since the late 1990s, the study of the varieties of Estonian used outside Estonia has taken a new direction. This can be recognised by the increased interest in the varieties of Estonian in various new countries of residence (e.g. Denmark, Finland, Germany. The focus of interest moving to western countries did not mean a loss of interest in the areas of the former Soviet empire. In 1996, the Centre for Migration and Diaspora Studies was set up in the Tartu University Institute of Geography. Presently, one of the prestigious research projects (2010−2013 of Finno-Ugric languages, the EU

  5. Estonian Language of Technology as a Factor Supporting the Evolution of Engineering Thinking

    Directory of Open Access Journals (Sweden)

    Mägi, Vahur

    2013-03-01

    Full Text Available Casual mention of teaching technology subjects in Estonian schools dates back several centuries. Navigation and construction were amongthe earliest professional skills that were taught. As both of them required mathematical thinking skills, teaching the subjects was usually accompanied by explaining the principles of mathematics. The first technology book in Estonian was published about two centuries ago and it dealed with geodesy. The earliest Estonian glossaries of technological terminology were published in the fields of physics and chemistry. The rise of Estonian as a language of higher education and science in the country came about in the 1920s and 1930s. Faculty members of the Tallinn School of Technology then published the first textbooks composed in the Estonian language for students of technology. The Estonian Society for Technology and the Estonian Association of Engineers became seriously involved in linguistic activities. Together with the Vocational Teachers’ Assembly of Tartu they published an illustrated technology glossary for machinery and tools terms. It was followed by a glossary of construction and building terms, compiled under the lead of the University of Technology. In addition, journals of technology introducedinnovations in the lexicon of technology to the general public. The postwar period in the development of the lexicon of technical terms was of little significance at first. A surge in language creativity could be detected in the 1960s, when terminology became a target of constantly growing attention to the development of technology lexicon. Series of technology glossaries were published. This tendency has continued to this day.

  6. Computer networks monitoring

    OpenAIRE

    Antončič , Polona

    2012-01-01

    The present thesis entitled Computer Networks Monitoring introduces the basics of computer networks, the aim and the computer data reclamation from networking devices, software for the system follow-up together with the case of monitoring a real network with tens of network devices. The networks represent an important part in the modern information technology and serve for the exchange of data and sources which makes their impeccability of crucial importance. Correct and efficient sys...

  7. Introduction to computer networking

    CERN Document Server

    Robertazzi, Thomas G

    2017-01-01

    This book gives a broad look at both fundamental networking technology and new areas that support it and use it. It is a concise introduction to the most prominent, recent technological topics in computer networking. Topics include network technology such as wired and wireless networks, enabling technologies such as data centers, software defined networking, cloud and grid computing and applications such as networks on chips, space networking and network security. The accessible writing style and non-mathematical treatment makes this a useful book for the student, network and communications engineer, computer scientist and IT professional. • Features a concise, accessible treatment of computer networking, focusing on new technological topics; • Provides non-mathematical introduction to networks in their most common forms today;< • Includes new developments in switching, optical networks, WiFi, Bluetooth, LTE, 5G, and quantum cryptography.

  8. Basics of Computer Networking

    CERN Document Server

    Robertazzi, Thomas

    2012-01-01

    Springer Brief Basics of Computer Networking provides a non-mathematical introduction to the world of networks. This book covers both technology for wired and wireless networks. Coverage includes transmission media, local area networks, wide area networks, and network security. Written in a very accessible style for the interested layman by the author of a widely used textbook with many years of experience explaining concepts to the beginner.

  9. Computer network defense system

    Science.gov (United States)

    Urias, Vincent; Stout, William M. S.; Loverro, Caleb

    2017-08-22

    A method and apparatus for protecting virtual machines. A computer system creates a copy of a group of the virtual machines in an operating network in a deception network to form a group of cloned virtual machines in the deception network when the group of the virtual machines is accessed by an adversary. The computer system creates an emulation of components from the operating network in the deception network. The components are accessible by the group of the cloned virtual machines as if the group of the cloned virtual machines was in the operating network. The computer system moves network connections for the group of the virtual machines in the operating network used by the adversary from the group of the virtual machines in the operating network to the group of the cloned virtual machines, enabling protecting the group of the virtual machines from actions performed by the adversary.

  10. Computer-communication networks

    CERN Document Server

    Meditch, James S

    1983-01-01

    Computer- Communication Networks presents a collection of articles the focus of which is on the field of modeling, analysis, design, and performance optimization. It discusses the problem of modeling the performance of local area networks under file transfer. It addresses the design of multi-hop, mobile-user radio networks. Some of the topics covered in the book are the distributed packet switching queuing network design, some investigations on communication switching techniques in computer networks and the minimum hop flow assignment and routing subject to an average message delay constraint

  11. Hyperswitch Communication Network Computer

    Science.gov (United States)

    Peterson, John C.; Chow, Edward T.; Priel, Moshe; Upchurch, Edwin T.

    1993-01-01

    Hyperswitch Communications Network (HCN) computer is prototype multiple-processor computer being developed. Incorporates improved version of hyperswitch communication network described in "Hyperswitch Network For Hypercube Computer" (NPO-16905). Designed to support high-level software and expansion of itself. HCN computer is message-passing, multiple-instruction/multiple-data computer offering significant advantages over older single-processor and bus-based multiple-processor computers, with respect to price/performance ratio, reliability, availability, and manufacturing. Design of HCN operating-system software provides flexible computing environment accommodating both parallel and distributed processing. Also achieves balance among following competing factors; performance in processing and communications, ease of use, and tolerance of (and recovery from) faults.

  12. Computer Networks and Globalization

    Directory of Open Access Journals (Sweden)

    J. Magliaro

    2007-07-01

    Full Text Available Communication and information computer networks connect the world in ways that make globalization more natural and inequity more subtle. As educators, we look at these phenomena holistically analyzing them from the realist’s view, thus exploring tensions, (in equity and (injustice, and from the idealist’s view, thus embracing connectivity, convergence and development of a collective consciousness. In an increasingly market- driven world we find examples of openness and human generosity that are based on networks, specifically the Internet. After addressing open movements in publishing, software industry and education, we describe the possibility of a dialectic equilibrium between globalization and indigenousness in view of ecologically designed future smart networks

  13. Computing networks from cluster to cloud computing

    CERN Document Server

    Vicat-Blanc, Pascale; Guillier, Romaric; Soudan, Sebastien

    2013-01-01

    "Computing Networks" explores the core of the new distributed computing infrastructures we are using today:  the networking systems of clusters, grids and clouds. It helps network designers and distributed-application developers and users to better understand the technologies, specificities, constraints and benefits of these different infrastructures' communication systems. Cloud Computing will give the possibility for millions of users to process data anytime, anywhere, while being eco-friendly. In order to deliver this emerging traffic in a timely, cost-efficient, energy-efficient, and

  14. Computing and networking at JINR

    CERN Document Server

    Zaikin, N S; Strizh, T A

    2001-01-01

    This paper describes the computing and networking facilities at the Joint Institute for Nuclear Research. The Joint Institute for Nuclear Research (JINR) is an international intergovernmental organization located in Dubna, a small town on the bank of the Volga river 120 km north from Moscow. At present JINR has 18 Member States. The Institute consists of 7 scientific Laboratories and some subdivisions. JINR has scientific cooperation with such scientific centres as CERN, FNAL, DESY etc. and is equipped with the powerful and fast computation means integrated into the worldwide computer networks. The Laboratory of Information Technologies (LIT) is responsible for Computing and Networking at JINR. (5 refs).

  15. Administration of remote computer networks

    OpenAIRE

    Fjeldbo, Stig Jarle

    2005-01-01

    Master i nettverks- og systemadministrasjon Today's computer networks have gone from typically being a small local area network, to wide area networks, where users and servers are interconnected with each other from all over the world. This development has gradually expanded as bandwidth has become higher and cheaper. But when dealing with the network traffic, bandwidth is only one of the important properties. Delay, jitter and reliability are also important properties for t...

  16. Understanding and designing computer networks

    CERN Document Server

    King, Graham

    1995-01-01

    Understanding and Designing Computer Networks considers the ubiquitous nature of data networks, with particular reference to internetworking and the efficient management of all aspects of networked integrated data systems. In addition it looks at the next phase of networking developments; efficiency and security are covered in the sections dealing with data compression and data encryption; and future examples of network operations, such as network parallelism, are introduced.A comprehensive case study is used throughout the text to apply and illustrate new techniques and concepts as th

  17. Computer Networks A Systems Approach

    CERN Document Server

    Peterson, Larry L

    2011-01-01

    This best-selling and classic book teaches you the key principles of computer networks with examples drawn from the real world of network and protocol design. Using the Internet as the primary example, the authors explain various protocols and networking technologies. Their systems-oriented approach encourages you to think about how individual network components fit into a larger, complex system of interactions. Whatever your perspective, whether it be that of an application developer, network administrator, or a designer of network equipment or protocols, you will come away with a "big pictur

  18. Risks in Networked Computer Systems

    OpenAIRE

    Klingsheim, André N.

    2008-01-01

    Networked computer systems yield great value to businesses and governments, but also create risks. The eight papers in this thesis highlight vulnerabilities in computer systems that lead to security and privacy risks. A broad range of systems is discussed in this thesis: Norwegian online banking systems, the Norwegian Automated Teller Machine (ATM) system during the 90's, mobile phones, web applications, and wireless networks. One paper also comments on legal risks to bank cust...

  19. Wireless Computational Networking Architectures

    Science.gov (United States)

    2013-12-01

    2] T. Ho, M. Medard, R. Kotter , D. Karger, M. Effros, J. Shi, and B. Leong, “A Random Linear Network Coding Approach to Multicast,” IEEE...218, January 2008. [10] R. Kotter and F. R. Kschischang, “Coding for Errors and Erasures in Random Network Coding,” IEEE Transactions on...Systems, Johns Hopkins University, Baltimore, Maryland, 2011. 6. B. W. Suter and Z. Yan U.S. Patent Pending 13/949,319 Rank Deficient Decoding

  20. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    H. Paugam-Moisy; S.M. Bohte (Sander); G. Rozenberg; T.H.W. Baeck (Thomas); J.N. Kok (Joost)

    2012-01-01

    htmlabstractAbstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions

  1. Computational Social Network Analysis

    CERN Document Server

    Hassanien, Aboul-Ella

    2010-01-01

    Presents insight into the social behaviour of animals (including the study of animal tracks and learning by members of the same species). This book provides web-based evidence of social interaction, perceptual learning, information granulation and the behaviour of humans and affinities between web-based social networks

  2. Analysis of computer networks

    CERN Document Server

    Gebali, Fayez

    2015-01-01

    This textbook presents the mathematical theory and techniques necessary for analyzing and modeling high-performance global networks, such as the Internet. The three main building blocks of high-performance networks are links, switching equipment connecting the links together, and software employed at the end nodes and intermediate switches. This book provides the basic techniques for modeling and analyzing these last two components. Topics covered include, but are not limited to: Markov chains and queuing analysis, traffic modeling, interconnection networks and switch architectures and buffering strategies.   ·         Provides techniques for modeling and analysis of network software and switching equipment; ·         Discusses design options used to build efficient switching equipment; ·         Includes many worked examples of the application of discrete-time Markov chains to communication systems; ·         Covers the mathematical theory and techniques necessary for ana...

  3. Computer Network Security- The Challenges of Securing a Computer Network

    Science.gov (United States)

    Scotti, Vincent, Jr.

    2011-01-01

    This article is intended to give the reader an overall perspective on what it takes to design, implement, enforce and secure a computer network in the federal and corporate world to insure the confidentiality, integrity and availability of information. While we will be giving you an overview of network design and security, this article will concentrate on the technology and human factors of securing a network and the challenges faced by those doing so. It will cover the large number of policies and the limits of technology and physical efforts to enforce such policies.

  4. Data Logistics in Network Computing

    CERN Multimedia

    CERN. Geneva; Marquina, Miguel Angel

    2005-01-01

    In distributed computing environments, performance is often dominated by the time that it takes to move data over a network. In the case of data-centric applications, or Data Grids, this problem of data movement becomes one of the overriding concerns. This talk describes techniques for improving data movement in Grid environments that we refer to as 'logistics.' We demonstrate that by using storage and cooperative forwarding 'in' the network, we can improve end to end throughput in many cases. Our approach offers clear performance benefits for high-bandwidth, high-latency networks. This talk will introduce the Logistical Session Layer (LSL) and provide experimental results from that system.

  5. Collective network for computer structures

    Energy Technology Data Exchange (ETDEWEB)

    Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  6. Optimal monitoring of computer networks

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, V.V.; Flanagan, D.

    1997-08-01

    The authors apply the ideas from optimal design theory to the very specific area of monitoring large computer networks. The behavior of these networks is so complex and uncertain that it is quite natural to use the statistical methods of experimental design which were originated in such areas as biology, behavioral sciences and agriculture, where the random character of phenomena is a crucial component and systems are too complicated to be described by some sophisticated deterministic models. They want to emphasize that only the first steps have been completed, and relatively simple underlying concepts about network functions have been used. Their immediate goal is to initiate studies focused on developing efficient experimental design techniques which can be used by practitioners working with large networks operating and evolving in a random environment.

  7. Markov Networks in Evolutionary Computation

    CERN Document Server

    Shakya, Siddhartha

    2012-01-01

    Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs).  EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current researc...

  8. A Multilayer Model of Computer Networks

    OpenAIRE

    Shchurov, Andrey A.

    2015-01-01

    The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...

  9. Personal computer local networks report

    CERN Document Server

    1991-01-01

    Please note this is a Short Discount publication. Since the first microcomputer local networks of the late 1970's and early 80's, personal computer LANs have expanded in popularity, especially since the introduction of IBMs first PC in 1981. The late 1980s has seen a maturing in the industry with only a few vendors maintaining a large share of the market. This report is intended to give the reader a thorough understanding of the technology used to build these systems ... from cable to chips ... to ... protocols to servers. The report also fully defines PC LANs and the marketplace, with in-

  10. Terminal-oriented computer-communication networks.

    Science.gov (United States)

    Schwartz, M.; Boorstyn, R. R.; Pickholtz, R. L.

    1972-01-01

    Four examples of currently operating computer-communication networks are described in this tutorial paper. They include the TYMNET network, the GE Information Services network, the NASDAQ over-the-counter stock-quotation system, and the Computer Sciences Infonet. These networks all use programmable concentrators for combining a multiplicity of terminals. Included in the discussion for each network is a description of the overall network structure, the handling and transmission of messages, communication requirements, routing and reliability consideration where applicable, operating data and design specifications where available, and unique design features in the area of computer communications.

  11. Computer networks ISE a systems approach

    CERN Document Server

    Peterson, Larry L

    2007-01-01

    Computer Networks, 4E is the only introductory computer networking book written by authors who have had first-hand experience with many of the protocols discussed in the book, who have actually designed some of them as well, and who are still actively designing the computer networks today. This newly revised edition continues to provide an enduring, practical understanding of networks and their building blocks through rich, example-based instruction. The authors' focus is on the why of network design, not just the specifications comprising today's systems but how key technologies and p

  12. Computer Network Defense Through Radial Wave Functions

    OpenAIRE

    Malloy, Ian

    2016-01-01

    The purpose of this research was to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has be...

  13. Computer network and knowledge sharing. Computer network to chishiki kyoyu

    Energy Technology Data Exchange (ETDEWEB)

    Yoshimura, S. (The University of Tokyo, Tokyo (Japan))

    1991-10-20

    The infomation system has changed from the on-line data base as a simple knowledge sharing, used in the times when devices were expensive, to dialogue type approaches as a result of TSS advancement. This paper describes the advantages in and methods of utilizing personal computer communications from the standpoint of a person engaged in chemistry education. The electronic mail has a number of advatages; you can reach a person as immediately as in the telephone but need not to interrupt the receiver primes work, you can get to it more easily than writing a letter. Particularly the electronic signboard has a large living know-how effect that ''someone who happens to know it can answer''. The Japan Chemical Society has opened the ''Square of Chemistry'' in the NIFTY Serve. Although the Society provides information, it is important that the participants make proposals positively and provide topics. Such a network is expanding to a woridwide scale.

  14. Mobile Agents in Networking and Distributed Computing

    CERN Document Server

    Cao, Jiannong

    2012-01-01

    The book focuses on mobile agents, which are computer programs that can autonomously migrate between network sites. This text introduces the concepts and principles of mobile agents, provides an overview of mobile agent technology, and focuses on applications in networking and distributed computing.

  15. Automated classification of computer network attacks

    CSIR Research Space (South Africa)

    Van Heerden, R

    2013-11-01

    Full Text Available In this paper we demonstrate how an automated reasoner, HermiT, is used to classify instances of computer network based attacks in conjunction with a network attack ontology. The ontology describes different types of network attacks through classes...

  16. Integrating network awareness in ATLAS distributed computing

    CERN Document Server

    De, K; The ATLAS collaboration; Klimentov, A; Maeno, T; Mckee, S; Nilsson, P; Petrosyan, A; Vukotic, I; Wenaus, T

    2014-01-01

    A crucial contributor to the success of the massively scaled global computing system that delivers the analysis needs of the LHC experiments is the networking infrastructure upon which the system is built. The experiments have been able to exploit excellent high-bandwidth networking in adapting their computing models for the most efficient utilization of resources. New advanced networking technologies now becoming available such as software defined networks hold the potential of further leveraging the network to optimize workflows and dataflows, through proactive control of the network fabric on the part of high level applications such as experiment workload management and data management systems. End to end monitoring of networking and data flow performance further allows applications to adapt based on real time conditions. We will describe efforts underway in ATLAS on integrating network awareness at the application level, particularly in workload management.

  17. Network Management of the SPLICE Computer Network.

    Science.gov (United States)

    1982-12-01

    and the Lawrence Livermore Nttionl Laboratory Octopus lietwork [Ref. 24]. Additionally, the :oiex Distributed Network Coatrol Systems 200 and 330...Alexander A., litftqiifivl 93 24. University of Calif~cnia Lavr i ce LJ~vermoce Laboratory Letter Wloe Requa): to -aptN -1raq. Ope, maya & Postgraduaate

  18. Computational network design from functional specifications

    KAUST Repository

    Peng, Chi Han

    2016-07-11

    Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.

  19. Reliable Interconnection Networks for Parallel Computers

    Science.gov (United States)

    1991-10-01

    AD-A259 498111IIIIIIII il1111 1 111 1 1 1 il i Technical Report 1294 R l a leliable Interconnection Networks for Parallel Computers ELECTE I S .JAN...SUBTITLE S. FUNDING NUMBERS Reliable Interconnection Networks for Parallel Computers N00014-80-C-0622 N00014-85-K-0124 N00014-91-J-1698 6. AUTHOR(S) Larry...are presented. 14. SUBJECT TERMS (key words) IS. NUMBER OF PAGES networks fault tolerance parallel computers 78 reliable routors 16. PRICE CODE

  20. Parallel computing and networking; Heiretsu keisanki to network

    Energy Technology Data Exchange (ETDEWEB)

    Asakawa, E.; Tsuru, T. [Japan National Oil Corp., Tokyo (Japan); Matsuoka, T. [Japan Petroleum Exploration Co. Ltd., Tokyo (Japan)

    1996-05-01

    This paper describes the trend of parallel computers used in geophysical exploration. Around 1993 was the early days when the parallel computers began to be used for geophysical exploration. Classification of these computers those days was mainly MIMD (multiple instruction stream, multiple data stream), SIMD (single instruction stream, multiple data stream) and the like. Parallel computers were publicized in the 1994 meeting of the Geophysical Exploration Society as a `high precision imaging technology`. Concerning the library of parallel computers, there was a shift to PVM (parallel virtual machine) in 1993 and to MPI (message passing interface) in 1995. In addition, the compiler of FORTRAN90 was released with support implemented for data parallel and vector computers. In 1993, networks used were Ethernet, FDDI, CDDI and HIPPI. In 1995, the OC-3 products under ATM began to propagate. However, ATM remains to be an interoffice high speed network because the ATM service has not spread yet for the public network. 1 ref.

  1. Computer networking a top-down approach

    CERN Document Server

    Kurose, James

    2017-01-01

    Unique among computer networking texts, the Seventh Edition of the popular Computer Networking: A Top Down Approach builds on the author’s long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating readers by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for readers interested in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The Seventh Edition has been updated to reflect the most important and exciting recent advances in networking.

  2. Conceptual metaphors in computer networking terminology ...

    African Journals Online (AJOL)

    Lakoff & Johnson, 1980) is used as a basic framework for analysing and explaining the occurrence of metaphor in the terminology used by computer networking professionals in the information technology (IT) industry. An analysis of linguistic ...

  3. Computer Network Equipment for Intrusion Detection Research

    National Research Council Canada - National Science Library

    Ye, Nong

    2000-01-01

    .... To test the process model, the system-level intrusion detection techniques and the working prototype of the intrusion detection system, a set of computer and network equipment has been purchased...

  4. Computational Complexity of Bosons in Linear Networks

    Science.gov (United States)

    2017-03-01

    AFRL-AFOSR-JP-TR-2017-0020 Computational complexity of bosons in linear networks Andrew White THE UNIVERSITY OF QUEENSLAND Final Report 07/27/2016...DATES COVERED (From - To) 02 Mar 2013 to 01 Mar 2016 4. TITLE AND SUBTITLE Computational complexity of bosons in linear networks 5a.  CONTRACT NUMBER 5b...direct exploration of the effect of partial distinguishability in the complexity class of the resulting sampling distribution. Our demultiplexed source

  5. Computer Networks and African Studies Centers.

    Science.gov (United States)

    Kuntz, Patricia S.

    The use of electronic communication in the 12 Title VI African Studies Centers is discussed, and the networks available for their use are reviewed. It is argued that the African Studies Centers should be on the cutting edge of contemporary electronic communication and that computer networks should be a fundamental aspect of their programs. An…

  6. A computer network attack taxonomy and ontology

    CSIR Research Space (South Africa)

    Van Heerden, RP

    2012-01-01

    Full Text Available of attacks, means that an attack could be mitigated accordingly. The authors extend a previous, initial taxonomy of computer network attacks which forms the basis of a proposed network attack ontology in this paper. The objective of this ontology...

  7. Virtual Network Computing Testbed for Cybersecurity Research

    Science.gov (United States)

    2015-08-17

    Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 212-346-1012 W911NF-12-1-0393 61504-CS-RIP.2 Final Report a. REPORT 14. ABSTRACT 16...Technology, 2007. [8] Pullen, J. M., 2000. The network workbench : network simulation software for academic investigation of Internet concepts. Comput

  8. EFFICIENCY METRICS COMPUTING IN COMBINED SENSOR NETWORKS

    OpenAIRE

    Luntovskyy, Andriy; Vasyutynskyy, Volodymyr

    2014-01-01

    This paper discusses the computer-aided design of combined networks for offices and building automation systems based on diverse wired and wireless standards. The design requirements for these networks are often contradictive and have to consider performance, energy and cost efficiency together. For usual office communication, quality of service is more important. In the wireless sensor networks, the energy efficiency is a critical requirement to ensure their long life, to reduce maintenance ...

  9. Autonomic computing enabled cooperative networked design

    CERN Document Server

    Wodczak, Michal

    2014-01-01

    This book introduces the concept of autonomic computing driven cooperative networked system design from an architectural perspective. As such it leverages and capitalises on the relevant advancements in both the realms of autonomic computing and networking by welding them closely together. In particular, a multi-faceted Autonomic Cooperative System Architectural Model is defined which incorporates the notion of Autonomic Cooperative Behaviour being orchestrated by the Autonomic Cooperative Networking Protocol of a cross-layer nature. The overall proposed solution not only advocates for the inc

  10. Spontaneous ad hoc mobile cloud computing network.

    Science.gov (United States)

    Lacuesta, Raquel; Lloret, Jaime; Sendra, Sandra; Peñalver, Lourdes

    2014-01-01

    Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.

  11. Spontaneous Ad Hoc Mobile Cloud Computing Network

    Directory of Open Access Journals (Sweden)

    Raquel Lacuesta

    2014-01-01

    Full Text Available Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.

  12. Algorithms and networking for computer games

    CERN Document Server

    Smed, Jouni

    2006-01-01

    Algorithms and Networking for Computer Games is an essential guide to solving the algorithmic and networking problems of modern commercial computer games, written from the perspective of a computer scientist. Combining algorithmic knowledge and game-related problems, the authors discuss all the common difficulties encountered in game programming. The first part of the book tackles algorithmic problems by presenting how they can be solved practically. As well as ""classical"" topics such as random numbers, tournaments and game trees, the authors focus on how to find a path in, create the terrai

  13. Computer methods in electric network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Saver, P.; Hajj, I.; Pai, M.; Trick, T.

    1983-06-01

    The computational algorithms utilized in power system analysis have more than just a minor overlap with those used in electronic circuit computer aided design. This paper describes the computer methods that are common to both areas and highlights the differences in application through brief examples. Recognizing this commonality has stimulated the exchange of useful techniques in both areas and has the potential of fostering new approaches to electric network analysis through the interchange of ideas.

  14. Computer network time synchronization the network time protocol

    CERN Document Server

    Mills, David L

    2006-01-01

    What started with the sundial has, thus far, been refined to a level of precision based on atomic resonance: Time. Our obsession with time is evident in this continued scaling down to nanosecond resolution and beyond. But this obsession is not without warrant. Precision and time synchronization are critical in many applications, such as air traffic control and stock trading, and pose complex and important challenges in modern information networks.Penned by David L. Mills, the original developer of the Network Time Protocol (NTP), Computer Network Time Synchronization: The Network Time Protocol

  15. Social networks a framework of computational intelligence

    CERN Document Server

    Chen, Shyi-Ming

    2014-01-01

    This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms.  The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling.  Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social network...

  16. Professional networking using computer-mediated communication.

    Science.gov (United States)

    Washer, Peter

    Traditionally, professionals have networked with others in their field through attending conferences, professional organizations, direct mailing, and via the workplace. Recently, there have been new possibilities to network with other professionals using the internet. This article looks at the possibilities that the internet offers for professional networking, particularly e-mailing lists, newsgroups and membership databases, and compares them against more traditional methods of professional networking. The different types of computer-mediated communication are discussed and their relative merits and disadvantages are examined. The benefits and potential pitfalls of internet professional networking, as it relates to the nursing profession, are examined. Practical advice is offered on how the internet can be used as a means to foster professional networks of academic, clinical or research interests.

  17. Natural computing for vehicular networks

    OpenAIRE

    Toutouh El Alamin, Jamal

    2016-01-01

    La presente tesis aborda el diseño inteligente de soluciones para el despliegue de redes vehiculares ad-hoc (vehicular ad hoc networks, VANETs). Estas son redes de comunicación inalámbrica formada principalmente por vehículos y elementos de infraestructura vial. Las VANETs ofrecen la oportunidad para desarrollar aplicaciones revolucionarias en el ámbito de la seguridad y eficiencia vial. Al ser un dominio tan novedoso, existe una serie de cuestiones abiertas, como el diseño de la infraestruct...

  18. Computing chemical organizations in biological networks.

    Science.gov (United States)

    Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

    2008-07-15

    Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

  19. International Symposium on Computing and Network Sustainability

    CERN Document Server

    Akashe, Shyam

    2017-01-01

    The book is compilation of technical papers presented at International Research Symposium on Computing and Network Sustainability (IRSCNS 2016) held in Goa, India on 1st and 2nd July 2016. The areas covered in the book are sustainable computing and security, sustainable systems and technologies, sustainable methodologies and applications, sustainable networks applications and solutions, user-centered services and systems and mobile data management. The novel and recent technologies presented in the book are going to be helpful for researchers and industries in their advanced works.

  20. On computer vision in wireless sensor networks.

    Energy Technology Data Exchange (ETDEWEB)

    Berry, Nina M.; Ko, Teresa H.

    2004-09-01

    Wireless sensor networks allow detailed sensing of otherwise unknown and inaccessible environments. While it would be beneficial to include cameras in a wireless sensor network because images are so rich in information, the power cost of transmitting an image across the wireless network can dramatically shorten the lifespan of the sensor nodes. This paper describe a new paradigm for the incorporation of imaging into wireless networks. Rather than focusing on transmitting images across the network, we show how an image can be processed locally for key features using simple detectors. Contrasted with traditional event detection systems that trigger an image capture, this enables a new class of sensors which uses a low power imaging sensor to detect a variety of visual cues. Sharing these features among relevant nodes cues specific actions to better provide information about the environment. We report on various existing techniques developed for traditional computer vision research which can aid in this work.

  1. Computation, cryptography, and network security

    CERN Document Server

    Rassias, Michael

    2015-01-01

    Analysis, assessment, and data management are core competencies for operation research analysts. This volume addresses a number of issues and developed methods for improving those skills. It is an outgrowth of a conference held in April 2013 at the Hellenic Military Academy, and brings together a broad variety of mathematical methods and theories with several applications. It discusses directions and pursuits of scientists that pertain to engineering sciences. It is also presents the theoretical background required for algorithms and techniques applied to a large variety of concrete problems. A number of open questions as well as new future areas are also highlighted.   This book will appeal to operations research analysts, engineers, community decision makers, academics, the military community, practitioners sharing the current “state-of-the-art,” and analysts from coalition partners. Topics covered include Operations Research, Games and Control Theory, Computational Number Theory and Information Securi...

  2. Student Motivation in Computer Networking Courses

    Directory of Open Access Journals (Sweden)

    Wen-Jung Hsin

    2007-01-01

    Full Text Available This paper introduces several hands-on projects that have been used to motivate students in learning various computer networking concepts. These projects are shown to be very useful and applicable to the learners’ daily tasks and activities such as emailing, Web browsing, and online shopping and banking, and lead to an unexpected byproduct, self-motivation.

  3. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

    Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

    2017-01-01

    Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

  4. Student Motivation in Computer Networking Courses

    Directory of Open Access Journals (Sweden)

    Wen-Jung Hsin, PhD

    2007-08-01

    Full Text Available This paper introduces several hands-on projects that have been used to motivate students in learning various computer networking concepts. These projects are shown to be very useful and applicable to the learners’ daily tasks and activities such as emailing, Web browsing, and online shopping and banking, and lead to an unexpected byproduct, self-motivation.

  5. Non-harmful insertion of data mimicking computer network attacks

    Energy Technology Data Exchange (ETDEWEB)

    Neil, Joshua Charles; Kent, Alexander; Hash, Jr, Curtis Lee

    2016-06-21

    Non-harmful data mimicking computer network attacks may be inserted in a computer network. Anomalous real network connections may be generated between a plurality of computing systems in the network. Data mimicking an attack may also be generated. The generated data may be transmitted between the plurality of computing systems using the real network connections and measured to determine whether an attack is detected.

  6. [Renewal of NIHS computer network system].

    Science.gov (United States)

    Segawa, Katsunori; Nakano, Tatsuya; Saito, Yoshiro

    2012-01-01

    Updated version of National Institute of Health Sciences Computer Network System (NIHS-NET) is described. In order to reduce its electric power consumption, the main server system was newly built using the virtual machine technology. The service that each machine provided in the previous network system should be maintained as much as possible. Thus, the individual server was constructed for each service, because a virtual server often show decrement in its performance as compared with a physical server. As a result, though the number of virtual servers was increased and the network communication became complicated among the servers, the conventional service was able to be maintained, and security level was able to be rather improved, along with saving electrical powers. The updated NIHS-NET bears multiple security countermeasures. To maximal use of these measures, awareness for the network security by all users is expected.

  7. Fuzzy logic, neural networks, and soft computing

    Science.gov (United States)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  8. Spiking network simulation code for petascale computers

    Science.gov (United States)

    Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.; Plesser, Hans E.; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

    2014-01-01

    Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682

  9. Spiking network simulation code for petascale computers.

    Science.gov (United States)

    Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

    2014-01-01

    Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today.

  10. International Symposium on Complex Computing-Networks

    CERN Document Server

    Sevgi, L; CCN2005; Complex computing networks: Brain-like and wave-oriented electrodynamic algorithms

    2006-01-01

    This book uniquely combines new advances in the electromagnetic and the circuits&systems theory. It integrates both fields regarding computational aspects of common interest. Emphasized subjects are those methods which mimic brain-like and electrodynamic behaviour; among these are cellular neural networks, chaos and chaotic dynamics, attractor-based computation and stream ciphers. The book contains carefully selected contributions from the Symposium CCN2005. Pictures from the bestowal of Honorary Doctorate degrees to Leon O. Chua and Leopold B. Felsen are included.

  11. Fast computation of minimum hybridization networks.

    Science.gov (United States)

    Albrecht, Benjamin; Scornavacca, Celine; Cenci, Alberto; Huson, Daniel H

    2012-01-15

    Hybridization events in evolution may lead to incongruent gene trees. One approach to determining possible interspecific hybridization events is to compute a hybridization network that attempts to reconcile incongruent gene trees using a minimum number of hybridization events. We describe how to compute a representative set of minimum hybridization networks for two given bifurcating input trees, using a parallel algorithm and provide a user-friendly implementation. A simulation study suggests that our program performs significantly better than existing software on biologically relevant data. Finally, we demonstrate the application of such methods in the context of the evolution of the Aegilops/Triticum genera. The algorithm is implemented in the program Dendroscope 3, which is freely available from www.dendroscope.org and runs on all three major operating systems.

  12. Integrating Wireless Sensor Networks with Computational Grids

    Science.gov (United States)

    Preve, Nikolaos

    Wireless sensor networks (WSNs) have been greatly developed and emerged their significance in a wide range of important applications such as ac quisition and process in formation from the physical world. The evolvement of Grid computing has been based on coordination of distributed and shared re sources. A Sensor Grid network can integrate these two leading technologies enabling real-time sensor data collection, the sharing of computational and stor age grid resources for sensor data processing and management. Several issues have occurred from this integration which dispute the modern design of sensor grids. In order to address these issues, in this paper we propose a sensor grid ar chitecture supporting it by a testbed which focuses on the design issues and on the improvement of our sensor grid architecture design.

  13. Computer network defense through radial wave functions

    Science.gov (United States)

    Malloy, Ian J.

    The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.

  14. The research of computer network security and protection strategy

    Science.gov (United States)

    He, Jian

    2017-05-01

    With the widespread popularity of computer network applications, its security is also received a high degree of attention. Factors affecting the safety of network is complex, for to do a good job of network security is a systematic work, has the high challenge. For safety and reliability problems of computer network system, this paper combined with practical work experience, from the threat of network security, security technology, network some Suggestions and measures for the system design principle, in order to make the masses of users in computer networks to enhance safety awareness and master certain network security technology.

  15. Using satellite communications for a mobile computer network

    Science.gov (United States)

    Wyman, Douglas J.

    1993-01-01

    The topics discussed include the following: patrol car automation, mobile computer network, network requirements, network design overview, MCN mobile network software, MCN hub operation, mobile satellite software, hub satellite software, the benefits of patrol car automation, the benefits of satellite mobile computing, and national law enforcement satellite.

  16. Analysis of Computer Network Information Based on "Big Data"

    Science.gov (United States)

    Li, Tianli

    2017-11-01

    With the development of the current era, computer network and large data gradually become part of the people's life, people use the computer to provide convenience for their own life, but at the same time there are many network information problems has to pay attention. This paper analyzes the information security of computer network based on "big data" analysis, and puts forward some solutions.

  17. Design and implementation of a local computer network

    Energy Technology Data Exchange (ETDEWEB)

    Fortune, P. J.; Lidinsky, W. P.; Zelle, B. R.

    1977-01-01

    An intralaboratory computer communications network was designed and is being implemented at Argonne National Laboratory. Parameters which were considered to be important in the network design are discussed; and the network, including its hardware and software components, is described. A discussion of the relationship between computer networks and distributed processing systems is also presented. The problems which the network is designed to solve and the consequent network structure represent considerations which are of general interest. 5 figures.

  18. Computational capabilities of graph neural networks.

    Science.gov (United States)

    Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

    2009-01-01

    In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.

  19. Social sciences via network analysis and computation

    CERN Document Server

    Kanduc, Tadej

    2015-01-01

    In recent years information and communication technologies have gained significant importance in the social sciences. Because there is such rapid growth of knowledge, methods and computer infrastructure, research can now seamlessly connect interdisciplinary fields such as business process management, data processing and mathematics. This study presents some of the latest results, practices and state-of-the-art approaches in network analysis, machine learning, data mining, data clustering and classifications in the contents of social sciences. It also covers various real-life examples such as t

  20. Computer network security and cyber ethics

    CERN Document Server

    Kizza, Joseph Migga

    2014-01-01

    In its 4th edition, this book remains focused on increasing public awareness of the nature and motives of cyber vandalism and cybercriminals, the weaknesses inherent in cyberspace infrastructure, and the means available to protect ourselves and our society. This new edition aims to integrate security education and awareness with discussions of morality and ethics. The reader will gain an understanding of how the security of information in general and of computer networks in particular, on which our national critical infrastructure and, indeed, our lives depend, is based squarely on the individ

  1. Some queuing network models of computer systems

    Science.gov (United States)

    Herndon, E. S.

    1980-01-01

    Queuing network models of a computer system operating with a single workload type are presented. Program algorithms are adapted for use on the Texas Instruments SR-52 programmable calculator. By slightly altering the algorithm to process the G and H matrices row by row instead of column by column, six devices and an unlimited job/terminal population could be handled on the SR-52. Techniques are also introduced for handling a simple load dependent server and for studying interactive systems with fixed multiprogramming limits.

  2. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  3. Choice Of Computer Networking Cables And Their Effect On Data ...

    African Journals Online (AJOL)

    Computer networking is the order of the day in this Information and Communication Technology (ICT) age. Although a network can be through a wireless device most local connections are done using cables. There are three main computer-networking cables namely coaxial cable, unshielded twisted pair cable and the optic ...

  4. Computational Aspects of Sensor Network Protocols (Distributed Sensor Network Simulator

    Directory of Open Access Journals (Sweden)

    Vasanth Iyer

    2009-08-01

    Full Text Available In this work, we model the sensor networks as an unsupervised learning and clustering process. We classify nodes according to its static distribution to form known class densities (CCPD. These densities are chosen from specific cross-layer features which maximizes lifetime of power-aware routing algorithms. To circumvent computational complexities of a power-ware communication STACK we introduce path-loss models at the nodes only for high density deployments. We study the cluster heads and formulate the data handling capacity for an expected deployment and use localized probability models to fuse the data with its side information before transmission. So each cluster head has a unique Pmax but not all cluster heads have the same measured value. In a lossless mode if there are no faults in the sensor network then we can show that the highest probability given by Pmax is ambiguous if its frequency is ≤ n/2 otherwise it can be determined by a local function. We further show that the event detection at the cluster heads can be modelled with a pattern 2m and m, the number of bits can be a correlated pattern of 2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy < 1. These local algorithms are further studied to optimize on power, fault detection and to maximize on the distributed routing algorithm used at the higher layers. From these bounds in large network, it is observed that the power dissipation is network size invariant. The performance of the routing algorithms solely based on success of finding healthy nodes in a large distribution. It is also observed that if the network size is kept constant and the density of the nodes is kept closer then the local pathloss model effects the performance of the routing algorithms. We also obtain the maximum intensity of transmitting nodes for a given category of routing algorithms for an outage constraint, i.e., the lifetime of sensor network.

  5. On Distributed Computation in Noisy Random Planar Networks

    OpenAIRE

    Kanoria, Y.; Manjunath, D.

    2007-01-01

    We consider distributed computation of functions of distributed data in random planar networks with noisy wireless links. We present a new algorithm for computation of the maximum value which is order optimal in the number of transmissions and computation time.We also adapt the histogram computation algorithm of Ying et al to make the histogram computation time optimal.

  6. Mobile Computing and Ubiquitous Networking: Concepts, Technologies and Challenges.

    Science.gov (United States)

    Pierre, Samuel

    2001-01-01

    Analyzes concepts, technologies and challenges related to mobile computing and networking. Defines basic concepts of cellular systems. Describes the evolution of wireless technologies that constitute the foundations of mobile computing and ubiquitous networking. Presents characterization and issues of mobile computing. Analyzes economical and…

  7. Chemical Reaction Networks for Computing Polynomials.

    Science.gov (United States)

    Salehi, Sayed Ahmad; Parhi, Keshab K; Riedel, Marc D

    2017-01-20

    Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: a type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.

  8. Planning and management of cloud computing networks

    Science.gov (United States)

    Larumbe, Federico

    The evolution of the Internet has a great impact on a big part of the population. People use it to communicate, query information, receive news, work, and as entertainment. Its extraordinary usefulness as a communication media made the number of applications and technological resources explode. However, that network expansion comes at the cost of an important power consumption. If the power consumption of telecommunication networks and data centers is considered as the power consumption of a country, it would rank at the 5 th place in the world. Furthermore, the number of servers in the world is expected to grow by a factor of 10 between 2013 and 2020. This context motivates us to study techniques and methods to allocate cloud computing resources in an optimal way with respect to cost, quality of service (QoS), power consumption, and environmental impact. The results we obtained from our test cases show that besides minimizing capital expenditures (CAPEX) and operational expenditures (OPEX), the response time can be reduced up to 6 times, power consumption by 30%, and CO2 emissions by a factor of 60. Cloud computing provides dynamic access to IT resources as a service. In this paradigm, programs are executed in servers connected to the Internet that users access from their computers and mobile devices. The first advantage of this architecture is to reduce the time of application deployment and interoperability, because a new user only needs a web browser and does not need to install software on local computers with specific operating systems. Second, applications and information are available from everywhere and with any device with an Internet access. Also, servers and IT resources can be dynamically allocated depending on the number of users and workload, a feature called elasticity. This thesis studies the resource management of cloud computing networks and is divided in three main stages. We start by analyzing the planning of cloud computing networks to get a

  9. 2013 International Conference on Computer Engineering and Network

    CERN Document Server

    Zhu, Tingshao

    2014-01-01

    This book aims to examine innovation in the fields of computer engineering and networking. The book covers important emerging topics in computer engineering and networking, and it will help researchers and engineers improve their knowledge of state-of-art in related areas. The book presents papers from The Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013) which was held on July 20-21, in Shanghai, China.

  10. AUTOMATIC CONTROL OF INTELLECTUAL RIGHTS IN THE GLOBAL COMPUTER NETWORKS

    OpenAIRE

    Anatoly P. Yakimaho; Victoriya V. Bessarabova

    2013-01-01

    The problems of use of subjects of intellectual property in the global computer networks are stated. The main attention is focused on the ways of problems solutions arising during the work in computer networks. Legal problems of information society are considered. The analysis of global computer networks as places for the organization of collective management by copyrights in the world scale is carried out. Issues of creation of a system of automatic control of property rights of authors and ...

  11. High Performance Networks From Supercomputing to Cloud Computing

    CERN Document Server

    Abts, Dennis

    2011-01-01

    Datacenter networks provide the communication substrate for large parallel computer systems that form the ecosystem for high performance computing (HPC) systems and modern Internet applications. The design of new datacenter networks is motivated by an array of applications ranging from communication intensive climatology, complex material simulations and molecular dynamics to such Internet applications as Web search, language translation, collaborative Internet applications, streaming video and voice-over-IP. For both Supercomputing and Cloud Computing the network enables distributed applicati

  12. Network Computer Technology. Phase I: Viability and Promise within NASA's Desktop Computing Environment

    Science.gov (United States)

    Paluzzi, Peter; Miller, Rosalind; Kurihara, West; Eskey, Megan

    1998-01-01

    Over the past several months, major industry vendors have made a business case for the network computer as a win-win solution toward lowering total cost of ownership. This report provides results from Phase I of the Ames Research Center network computer evaluation project. It identifies factors to be considered for determining cost of ownership; further, it examines where, when, and how network computer technology might fit in NASA's desktop computing architecture.

  13. DETECTING NETWORK ATTACKS IN COMPUTER NETWORKS BY USING DATA MINING METHODS

    OpenAIRE

    Platonov, V. V.; Semenov, P. O.

    2016-01-01

    The article describes an approach to the development of an intrusion detection system for computer networks. It is shown that the usage of several data mining methods and tools can improve the efficiency of protection computer networks against network at-tacks due to the combination of the benefits of signature detection and anomalies detection and the opportunity of adaptation the sys-tem for hardware and software structure of the computer network.

  14. Email networks and the spread of computer viruses

    Science.gov (United States)

    Newman, M. E.; Forrest, Stephanie; Balthrop, Justin

    2002-09-01

    Many computer viruses spread via electronic mail, making use of computer users' email address books as a source for email addresses of new victims. These address books form a directed social network of connections between individuals over which the virus spreads. Here we investigate empirically the structure of this network using data drawn from a large computer installation, and discuss the implications of this structure for the understanding and prevention of computer virus epidemics.

  15. An Overview of Computer Network security and Research Technology

    OpenAIRE

    Rathore, Vandana

    2016-01-01

    The rapid development in the field of computer networks and systems brings both convenience and security threats for users. Security threats include network security and data security. Network security refers to the reliability, confidentiality, integrity and availability of the information in the system. The main objective of network security is to maintain the authenticity, integrity, confidentiality, availability of the network. This paper introduces the details of the technologies used in...

  16. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  17. Network Patch Cables Demystified: A Super Activity for Computer Networking Technology

    Science.gov (United States)

    Brown, Douglas L.

    2004-01-01

    This article de-mystifies network patch cable secrets so that people can connect their computers and transfer those pesky files--without screaming at the cables. It describes a network cabling activity that can offer students a great hands-on opportunity for working with the tools, techniques, and media used in computer networking. Since the…

  18. Throughput capacity computation model for hybrid wireless networks

    African Journals Online (AJOL)

    wireless networks. We present in this paper, a computational model for obtaining throughput capacity for hybrid wireless networks. For a hybrid network with n nodes and m base stations, we observe through simulation that the throughput capacity increases linearly with the base station infrastructure connected by the wired ...

  19. Novel Ethernet Based Optical Local Area Networks for Computer Interconnection

    NARCIS (Netherlands)

    Radovanovic, Igor; van Etten, Wim; Taniman, R.O.; Kleinkiskamp, Ronny

    2003-01-01

    In this paper we present new optical local area networks for fiber-to-the-desk application. Presented networks are expected to bring a solution for having optical fibers all the way to computers. To bring the overall implementation costs down we have based our networks on short-wavelength optical

  20. 4th International Conference on Computer Engineering and Networks

    CERN Document Server

    2015-01-01

    This book aims to examine innovation in the fields of computer engineering and networking. The book covers important emerging topics in computer engineering and networking, and it will help researchers and engineers improve their knowledge of state-of-art in related areas. The book presents papers from the 4th International Conference on Computer Engineering and Networks (CENet2014) held July 19-20, 2014 in Shanghai, China.  ·       Covers emerging topics for computer engineering and networking ·       Discusses how to improve productivity by using the latest advanced technologies ·       Examines innovation in the fields of computer engineering and networking  

  1. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  2. Second International Conference on Advanced Computing, Networking and Informatics

    CERN Document Server

    Mohapatra, Durga; Konar, Amit; Chakraborty, Aruna

    2014-01-01

    Advanced Computing, Networking and Informatics are three distinct and mutually exclusive disciplines of knowledge with no apparent sharing/overlap among them. However, their convergence is observed in many real world applications, including cyber-security, internet banking, healthcare, sensor networks, cognitive radio, pervasive computing amidst many others. This two-volume proceedings explore the combined use of Advanced Computing and Informatics in the next generation wireless networks and security, signal and image processing, ontology and human-computer interfaces (HCI). The two volumes together include 148 scholarly papers, which have been accepted for presentation from over 640 submissions in the second International Conference on Advanced Computing, Networking and Informatics, 2014, held in Kolkata, India during June 24-26, 2014. The first volume includes innovative computing techniques and relevant research results in informatics with selective applications in pattern recognition, signal/image process...

  3. Network selection, Information filtering and Scalable computation

    Science.gov (United States)

    Ye, Changqing

    This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over

  4. 3rd International Conference on Advanced Computing, Networking and Informatics

    CERN Document Server

    Mohapatra, Durga; Chaki, Nabendu

    2016-01-01

    Advanced Computing, Networking and Informatics are three distinct and mutually exclusive disciplines of knowledge with no apparent sharing/overlap among them. However, their convergence is observed in many real world applications, including cyber-security, internet banking, healthcare, sensor networks, cognitive radio, pervasive computing amidst many others. This two volume proceedings explore the combined use of Advanced Computing and Informatics in the next generation wireless networks and security, signal and image processing, ontology and human-computer interfaces (HCI). The two volumes together include 132 scholarly articles, which have been accepted for presentation from over 550 submissions in the Third International Conference on Advanced Computing, Networking and Informatics, 2015, held in Bhubaneswar, India during June 23–25, 2015.

  5. HeNCE: A Heterogeneous Network Computing Environment

    Directory of Open Access Journals (Sweden)

    Adam Beguelin

    1994-01-01

    Full Text Available Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM. The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.

  6. Dynamics of Bottlebrush Networks: A Computational Study

    Science.gov (United States)

    Dobrynin, Andrey; Cao, Zhen; Sheiko, Sergei

    We study dynamics of deformation of bottlebrush networks using molecular dynamics simulations and theoretical calculations. Analysis of our simulation results show that the dynamics of bottlebrush network deformation can be described by a Rouse model for polydisperse networks with effective Rouse time of the bottlebrush network strand, τR =τ0Ns2 (Nsc + 1) where, Ns is the number-average degree of polymerization of the bottlebrush backbone strands between crosslinks, Nsc is the degree of polymerization of the side chains and τ0is a characteristic monomeric relaxation time. At time scales t smaller than the Rouse time, t crosslinks, the network response is pure elastic with shear modulus G (t) =G0 , where G0 is the equilibrium shear modulus at small deformation. The stress evolution in the bottlebrush networks can be described by a universal function of t /τR . NSF DMR-1409710.

  7. Risk, Privacy, and Security in Computer Networks

    OpenAIRE

    Årnes, Andre

    2006-01-01

    With an increasingly digitally connected society comes complexity, uncertainty, and risk. Network monitoring, incident management, and digital forensics is of increasing importance with the escalation of cybercrime and other network supported serious crimes. New laws and regulations governing electronic communications, cybercrime, and data retention are being proposed, continuously requiring new methods and tools. This thesis introduces a novel approach to real-time network risk assessmen...

  8. Computing properties of stable configurations of thermodynamic binding networks

    OpenAIRE

    Breik, Keenan; Prakash, Lakshmi; Thachuk, Chris; Heule, Marijn; Soloveichik, David

    2017-01-01

    Models of molecular computing generally embed computation in kinetics--the specific time evolution of a chemical system. However, if the desired output is not thermodynamically stable, basic physical chemistry dictates that thermodynamic forces will drive the system toward error throughout the computation. The Thermodynamic Binding Network (TBN) model was introduced to formally study how the thermodynamic equilibrium can be made consistent with the desired computation, and it idealizes bindin...

  9. Artificial Neural Network Metamodels of Stochastic Computer Simulations

    Science.gov (United States)

    1994-08-10

    23 Haddock, J. and O’Keefe, R., "Using Artificial Intelligence to Facilitate Manufacturing Systems Simulation," Computers & Industrial Engineering , Vol...Feedforward Neural Networks," Computers & Industrial Engineering , Vol. 21, No. 1- 4, (1991), pp. 247-251. 87 Proceedings of the 1992 Summer Computer...Using Simulation Experiments," Computers & Industrial Engineering , Vol. 22, No. 2 (1992), pp. 195-209. 119 Kuei, C. and Madu, C., "Polynomial

  10. Wireless Networks: New Meaning to Ubiquitous Computing.

    Science.gov (United States)

    Drew, Wilfred, Jr.

    2003-01-01

    Discusses the use of wireless technology in academic libraries. Topics include wireless networks; standards (IEEE 802.11); wired versus wireless; why libraries implement wireless technology; wireless local area networks (WLANs); WLAN security; examples of wireless use at Indiana State University and Morrisville College (New York); and useful…

  11. CFD Optimization on Network-Based Parallel Computer System

    Science.gov (United States)

    Cheung, Samson H.; VanDalsem, William (Technical Monitor)

    1994-01-01

    Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.

  12. Phoebus: Network Middleware for Next-Generation Network Computing

    Energy Technology Data Exchange (ETDEWEB)

    Martin Swany

    2012-06-16

    The Phoebus project investigated algorithms, protocols, and middleware infrastructure to improve end-to-end performance in high speed, dynamic networks. The Phoebus system essentially serves as an adaptation point for networks with disparate capabilities or provisioning. This adaptation can take a variety of forms including acting as a provisioning agent across multiple signaling domains, providing transport protocol adaptation points, and mapping between distributed resource reservation paradigms and the optical network control plane. We have successfully developed the system and demonstrated benefits. The Phoebus system was deployed in Internet2 and in ESnet, as well as in GEANT2, RNP in Brazil and over international links to Korea and Japan. Phoebus is a system that implements a new protocol and associated forwarding infrastructure for improving throughput in high-speed dynamic networks. It was developed to serve the needs of large DOE applications on high-performance networks. The idea underlying the Phoebus model is to embed Phoebus Gateways (PGs) in the network as on-ramps to dynamic circuit networks. The gateways act as protocol translators that allow legacy applications to use dedicated paths with high performance.

  13. Computationally Efficient Neural Network Intrusion Security Awareness

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Milos Manic

    2009-08-01

    An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

  14. Evolving ATLAS Computing For Today’s Networks

    CERN Document Server

    Campana, S; The ATLAS collaboration; Jezequel, S; Negri, G; Serfon, C; Ueda, I

    2012-01-01

    The ATLAS computing infrastructure was designed many years ago based on the assumption of rather limited network connectivity between computing centres. ATLAS sites have been organized in a hierarchical model, where only a static subset of all possible network links can be exploited and a static subset of well connected sites (CERN and the T1s) can cover important functional roles such as hosting master copies of the data. The pragmatic adoption of such simplified approach, in respect of a more relaxed scenario interconnecting all sites, was very beneficial during the commissioning of the ATLAS distributed computing system and essential in reducing the operational cost during the first two years of LHC data taking. In the mean time, networks evolved far beyond this initial scenario: while a few countries are still poorly connected with the rest of the WLCG infrastructure, most of the ATLAS computing centres are now efficiently interlinked. Our operational experience in running the computing infrastructure in ...

  15. Networks and Project Work: Alternative Pedagogies for Writing with Computers.

    Science.gov (United States)

    Susser, Bernard

    1993-01-01

    Describes three main uses of computers for writing as a social activity: networking, telecommunications, and project work. Examines advantages and disadvantages of teaching writing on a network. Argues that reports in the literature and the example of an English as a foreign language writing class show that project work shares most of the…

  16. Computer Networking Strategies for Building Collaboration among Science Educators.

    Science.gov (United States)

    Aust, Ronald

    The development and dissemination of science materials can be associated with technical delivery systems such as the Unified Network for Informatics in Teacher Education (UNITE). The UNITE project was designed to investigate ways for using computer networking to improve communications and collaboration among university schools of education and…

  17. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  18. Neuromorphic computing applications for network intrusion detection systems

    Science.gov (United States)

    Garcia, Raymond C.; Pino, Robinson E.

    2014-05-01

    What is presented here is a sequence of evolving concepts for network intrusion detection. These concepts start with neuromorphic structures for XOR-based signature matching and conclude with computationally based network intrusion detection system with an autonomous structuring algorithm. There is evidence that neuromorphic computation for network intrusion detection is fractal in nature under certain conditions. Specifically, the neural structure can take fractal form when simple neural structuring is autonomous. A neural structure is fractal by definition when its fractal dimension exceeds the synaptic matrix dimension. The authors introduce the use of fractal dimension of the neuromorphic structure as a factor in the autonomous restructuring feedback loop.

  19. Trajectory Based Optimal Segment Computation in Road Network Databases

    DEFF Research Database (Denmark)

    Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.

    2013-01-01

    that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....

  20. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    for traffic classification, which can be used for nearly real-time processing of big amounts of data using affordable CPU and memory resources. Other questions are related to methods for real-time estimation of the application Quality of Service (QoS) level based on the results obtained by the traffic......Traffic monitoring and analysis can be done for multiple different reasons: to investigate the usage of network resources, assess the performance of network applications, adjust Quality of Service (QoS) policies in the network, log the traffic to comply with the law, or create realistic models...... classifier. This thesis is focused on topics connected with traffic classification and analysis, while the work on methods for QoS assessment is limited to defining the connections with the traffic classification and proposing a general algorithm. We introduced the already known methods for traffic...

  1. Optical interconnection networks for high-performance computing systems.

    Science.gov (United States)

    Biberman, Aleksandr; Bergman, Keren

    2012-04-01

    Enabled by silicon photonic technology, optical interconnection networks have the potential to be a key disruptive technology in computing and communication industries. The enduring pursuit of performance gains in computing, combined with stringent power constraints, has fostered the ever-growing computational parallelism associated with chip multiprocessors, memory systems, high-performance computing systems and data centers. Sustaining these parallelism growths introduces unique challenges for on- and off-chip communications, shifting the focus toward novel and fundamentally different communication approaches. Chip-scale photonic interconnection networks, enabled by high-performance silicon photonic devices, offer unprecedented bandwidth scalability with reduced power consumption. We demonstrate that the silicon photonic platforms have already produced all the high-performance photonic devices required to realize these types of networks. Through extensive empirical characterization in much of our work, we demonstrate such feasibility of waveguides, modulators, switches and photodetectors. We also demonstrate systems that simultaneously combine many functionalities to achieve more complex building blocks. We propose novel silicon photonic devices, subsystems, network topologies and architectures to enable unprecedented performance of these photonic interconnection networks. Furthermore, the advantages of photonic interconnection networks extend far beyond the chip, offering advanced communication environments for memory systems, high-performance computing systems, and data centers.

  2. Active system area networks for data intensive computations. Final report

    Energy Technology Data Exchange (ETDEWEB)

    None

    2002-04-01

    The goal of the Active System Area Networks (ASAN) project is to develop hardware and software technologies for the implementation of active system area networks (ASANs). The use of the term ''active'' refers to the ability of the network interfaces to perform application-specific as well as system level computations in addition to their traditional role of data transfer. This project adopts the view that the network infrastructure should be an active computational entity capable of supporting certain classes of computations that would otherwise be performed on the host CPUs. The result is a unique network-wide programming model where computations are dynamically placed within the host CPUs or the NIs depending upon the quality of service demands and network/CPU resource availability. The projects seeks to demonstrate that such an approach is a better match for data intensive network-based applications and that the advent of low-cost powerful embedded processors and configurable hardware makes such an approach economically viable and desirable.

  3. Console Networks for Major Computer Systems

    Energy Technology Data Exchange (ETDEWEB)

    Ophir, D; Shepherd, B; Spinrad, R J; Stonehill, D

    1966-07-22

    A concept for interactive time-sharing of a major computer system is developed in which satellite computers mediate between the central computing complex and the various individual user terminals. These techniques allow the development of a satellite system substantially independent of the details of the central computer and its operating system. Although the user terminals' roles may be rich and varied, the demands on the central facility are merely those of a tape drive or similar batched information transfer device. The particular system under development provides service for eleven visual display and communication consoles, sixteen general purpose, low rate data sources, and up to thirty-one typewriters. Each visual display provides a flicker-free image of up to 4000 alphanumeric characters or tens of thousands of points by employing a swept raster picture generating technique directly compatible with that of commercial television. Users communicate either by typewriter or a manually positioned light pointer.

  4. Electromagnetic field computation by network methods

    CERN Document Server

    Felsen, Leopold B; Russer, Peter

    2009-01-01

    This monograph proposes a systematic and rigorous treatment of electromagnetic field representations in complex structures. The book presents new strong models by combining important computational methods. This is the last book of the late Leopold Felsen.

  5. Realistic computer network simulation for network intrusion detection dataset generation

    Science.gov (United States)

    Payer, Garrett

    2015-05-01

    The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup; its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

  6. 1st International Conference on Signal, Networks, Computing, and Systems

    CERN Document Server

    Mohapatra, Durga; Nagar, Atulya; Sahoo, Manmath

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

  7. Dynamical Systems Theory for Transparent Symbolic Computation in Neuronal Networks

    OpenAIRE

    Carmantini, Giovanni Sirio

    2017-01-01

    In this thesis, we explore the interface between symbolic and dynamical system computation, with particular regard to dynamical system models of neuronal networks. In doing so, we adhere to a definition of computation as the physical realization of a formal system, where we say that a dynamical system performs a computation if a correspondence can be found between its dynamics on a vectorial space and the formal system’s dynamics on a symbolic space. Guided by this definition, we characterize...

  8. CX: A Scalable, Robust Network for Parallel Computing

    Directory of Open Access Journals (Sweden)

    Peter Cappello

    2002-01-01

    Full Text Available CX, a network-based computational exchange, is presented. The system's design integrates variations of ideas from other researchers, such as work stealing, non-blocking tasks, eager scheduling, and space-based coordination. The object-oriented API is simple, compact, and cleanly separates application logic from the logic that supports interprocess communication and fault tolerance. Computations, of course, run to completion in the presence of computational hosts that join and leave the ongoing computation. Such hosts, or producers, use task caching and prefetching to overlap computation with interprocessor communication. To break a potential task server bottleneck, a network of task servers is presented. Even though task servers are envisioned as reliable, the self-organizing, scalable network of n- servers, described as a sibling-connected height-balanced fat tree, tolerates a sequence of n-1 server failures. Tasks are distributed throughout the server network via a simple "diffusion" process. CX is intended as a test bed for research on automated silent auctions, reputation services, authentication services, and bonding services. CX also provides a test bed for algorithm research into network-based parallel computation.

  9. Signaling networks: information flow, computation, and decision making.

    Science.gov (United States)

    Azeloglu, Evren U; Iyengar, Ravi

    2015-04-01

    Signaling pathways come together to form networks that connect receptors to many different cellular machines. Such networks not only receive and transmit signals but also process information. The complexity of these networks requires the use of computational models to understand how information is processed and how input-output relationships are determined. Two major computational approaches used to study signaling networks are graph theory and dynamical modeling. Both approaches are useful; network analysis (application of graph theory) helps us understand how the signaling network is organized and what its information-processing capabilities are, whereas dynamical modeling helps us determine how the system changes in time and space upon receiving stimuli. Computational models have helped us identify a number of emergent properties that signaling networks possess. Such properties include ultrasensitivity, bistability, robustness, and noise-filtering capabilities. These properties endow cell-signaling networks with the ability to ignore small or transient signals and/or amplify signals to drive cellular machines that spawn numerous physiological functions associated with different cell states. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.

  10. Integrating Network Management for Cloud Computing Services

    Science.gov (United States)

    2015-06-01

    DeviceConfigIsControl- lable is calculated based on whether the device is powered up, whether the device can be reachable via SSH /Telnet from the management network...lines of C# and C++ code, plus a number of internal libraries . At its core, it is a highly-available RESTful web service with persistent storage. Below

  11. Propagation models for computing biochemical reaction networks

    OpenAIRE

    Henzinger, Thomas A; Mateescu, Maria

    2011-01-01

    We introduce propagation models, a formalism designed to support general and efficient data structures for the transient analysis of biochemical reaction networks. We give two use cases for propagation abstract data types: the uniformization method and numerical integration. We also sketch an implementation of a propagation abstract data type, which uses abstraction to approximate states.

  12. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    Science.gov (United States)

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  13. Low Computational Complexity Network Coding For Mobile Networks

    DEFF Research Database (Denmark)

    Heide, Janus

    2012-01-01

    -flow coding technique. One of the key challenges of this technique is its inherent computational complexity which can lead to high computational load and energy consumption in particular on the mobile platforms that are the target platform in this work. To increase the coding throughput several...... library and will be available for researchers and students in the future. Chapter 1 introduces motivating examples and the state of art when this work commenced. In Chapter 2 selected publications are presented and how their content is related. Chapter 3 presents the main outcome of the work and briefly...

  14. Development of Computer Science Disciplines - A Social Network Analysis Approach

    CERN Document Server

    Pham, Manh Cuong; Jarke, Matthias

    2011-01-01

    In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investiga...

  15. FY 1999 Blue Book: Computing, Information, and Communications: Networked Computing for the 21st Century

    Data.gov (United States)

    Networking and Information Technology Research and Development, Executive Office of the President — U.S.research and development R and D in computing, communications, and information technologies has enabled unprecedented scientific and engineering advances,...

  16. Dynamic Defensive Posture for Computer Network Defence

    Science.gov (United States)

    2006-12-01

    des algorithmes pour le classement de la sévérité des attaques sur le réseau et des mécanismes permettant d’attribuer une valeur aux éléments...power outages and social engineering attacks. Because it has such a large knowledge base on which to draw, it can reason very thoroughly about network...service attacks, eavesdropping and sniffing attacks on data in transit, or data tampering; more complex still would be models of social engineering

  17. Characterization and Planning for Computer Network Operations

    Science.gov (United States)

    2010-07-01

    Cell phones, personal computers, laptops, and personal digital assistants represent a small number of the technology-based devices used around the...C. Simpson, editors. Assistive Technol- ogy and Artificial Intelligence, Applications in Robotics, User Interfaces and Natural Language Processing...retrieval agents: Experiments with automated web browsing. pages 13–18, 1995. [206] V. A. Siris and F. Papagalou. Application of anomaly detection

  18. Wirelessly powered sensor networks and computational RFID

    CERN Document Server

    2013-01-01

    The Wireless Identification and Sensing Platform (WISP) is the first of a new class of RF-powered sensing and computing systems.  Rather than being powered by batteries, these sensor systems are powered by radio waves that are either deliberately broadcast or ambient.  Enabled by ongoing exponential improvements in the energy efficiency of microelectronics, RF-powered sensing and computing is rapidly moving along a trajectory from impossible (in the recent past), to feasible (today), toward practical and commonplace (in the near future). This book is a collection of key papers on RF-powered sensing and computing systems including the WISP.  Several of the papers grew out of the WISP Challenge, a program in which Intel Corporation donated WISPs to academic applicants who proposed compelling WISP-based projects.  The book also includes papers presented at the first WISP Summit, a workshop held in Berkeley, CA in association with the ACM Sensys conference, as well as other relevant papers. The book provides ...

  19. Six Networks on a Universal Neuromorphic Computing Substrate

    Science.gov (United States)

    Pfeil, Thomas; Grübl, Andreas; Jeltsch, Sebastian; Müller, Eric; Müller, Paul; Petrovici, Mihai A.; Schmuker, Michael; Brüderle, Daniel; Schemmel, Johannes; Meier, Karlheinz

    2013-01-01

    In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality. PMID:23423583

  20. Computing Path Tables for Quickest Multipaths In Computer Networks

    Energy Technology Data Exchange (ETDEWEB)

    Grimmell, W.C.

    2004-12-21

    We consider the transmission of a message from a source node to a terminal node in a network with n nodes and m links where the message is divided into parts and each part is transmitted over a different path in a set of paths from the source node to the terminal node. Here each link is characterized by a bandwidth and delay. The set of paths together with their transmission rates used for the message is referred to as a multipath. We present two algorithms that produce a minimum-end-to-end message delay multipath path table that, for every message length, specifies a multipath that will achieve the minimum end-to-end delay. The algorithms also generate a function that maps the minimum end-to-end message delay to the message length. The time complexities of the algorithms are O(n{sup 2}((n{sup 2}/logn) + m)min(D{sub max}, C{sub max})) and O(nm(C{sub max} + nmin(D{sub max}, C{sub max}))) when the link delays and bandwidths are non-negative integers. Here D{sub max} and C{sub max} are respectively the maximum link delay and maximum link bandwidth and C{sub max} and D{sub max} are greater than zero.

  1. The Poor Man's Guide to Computer Networks and their Applications

    DEFF Research Database (Denmark)

    Sharp, Robin

    2003-01-01

    These notes for DTU course 02220, Concurrent Programming, give an introduction to computer networks, with focus on the modern Internet. Basic Internet protocols such as IP, TCP and UDP are presented, and two Internet application protocols, SMTP and HTTP, are described in some detail. Techniques f...... for network programming are described, with concrete examples in Java. Techniques considered include simple socket programming, RMI, Corba, and Web services with SOAP....

  2. Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

    OpenAIRE

    Desell, Travis

    2017-01-01

    This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been ...

  3. Efficient Capacity Computation and Power Optimization for Relay Networks

    CERN Document Server

    Parvaresh, Farzad

    2011-01-01

    The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power o...

  4. Building Social Networks with Computer Networks: A New Deal for Teaching and Learning.

    Science.gov (United States)

    Thurston, Thomas

    2001-01-01

    Discusses the role of computer technology and Web sites in expanding social networks. Focuses on the New Deal Network using two examples: (1) uniting a Julia C. Lathrop Housing (Chicago, Illinois) resident with a university professor; and (2) saving the Hugo Gellert art murals at the Seward Park Coop Apartments (New York). (CMK)

  5. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

    Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

    2017-05-01

    In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

  6. Service-oriented Software Defined Optical Networks for Cloud Computing

    Science.gov (United States)

    Liu, Yuze; Li, Hui; Ji, Yuefeng

    2017-10-01

    With the development of big data and cloud computing technology, the traditional software-defined network is facing new challenges (e.g., ubiquitous accessibility, higher bandwidth, more flexible management and greater security). This paper proposes a new service-oriented software defined optical network architecture, including a resource layer, a service abstract layer, a control layer and an application layer. We then dwell on the corresponding service providing method. Different service ID is used to identify the service a device can offer. Finally, we experimentally evaluate that proposed service providing method can be applied to transmit different services based on the service ID in the service-oriented software defined optical network.

  7. A local area computer network expert system framework

    Science.gov (United States)

    Dominy, Robert

    1987-01-01

    Over the past years an expert system called LANES designed to detect and isolate faults in the Goddard-wide Hybrid Local Area Computer Network (LACN) was developed. As a result, the need for developing a more generic LACN fault isolation expert system has become apparent. An object oriented approach was explored to create a set of generic classes, objects, rules, and methods that would be necessary to meet this need. The object classes provide a convenient mechanism for separating high level information from low level network specific information. This approach yeilds a framework which can be applied to different network configurations and be easily expanded to meet new needs.

  8. Test experience on an ultrareliable computer communication network

    Science.gov (United States)

    Abbott, L. W.

    1984-01-01

    The dispersed sensor processing mesh (DSPM) is an experimental, ultra-reliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology.

  9. Analytical Computation of the Epidemic Threshold on Temporal Networks

    Directory of Open Access Journals (Sweden)

    Eugenio Valdano

    2015-04-01

    Full Text Available The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

  10. Propagation of computer virus both across the Internet and external computers: A complex-network approach

    Science.gov (United States)

    Gan, Chenquan; Yang, Xiaofan; Liu, Wanping; Zhu, Qingyi; Jin, Jian; He, Li

    2014-08-01

    Based on the assumption that external computers (particularly, infected external computers) are connected to the Internet, and by considering the influence of the Internet topology on computer virus spreading, this paper establishes a novel computer virus propagation model with a complex-network approach. This model possesses a unique (viral) equilibrium which is globally attractive. Some numerical simulations are also given to illustrate this result. Further study shows that the computers with higher node degrees are more susceptible to infection than those with lower node degrees. In this regard, some appropriate protective measures are suggested.

  11. Identifying failure in a tree network of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Pinnow, Kurt W.; Wallenfelt, Brian P.

    2010-08-24

    Methods, parallel computers, and products are provided for identifying failure in a tree network of a parallel computer. The parallel computer includes one or more processing sets including an I/O node and a plurality of compute nodes. For each processing set embodiments include selecting a set of test compute nodes, the test compute nodes being a subset of the compute nodes of the processing set; measuring the performance of the I/O node of the processing set; measuring the performance of the selected set of test compute nodes; calculating a current test value in dependence upon the measured performance of the I/O node of the processing set, the measured performance of the set of test compute nodes, and a predetermined value for I/O node performance; and comparing the current test value with a predetermined tree performance threshold. If the current test value is below the predetermined tree performance threshold, embodiments include selecting another set of test compute nodes. If the current test value is not below the predetermined tree performance threshold, embodiments include selecting from the test compute nodes one or more potential problem nodes and testing individually potential problem nodes and links to potential problem nodes.

  12. Regional Computation of TEC Using a Neural Network Model

    Science.gov (United States)

    Leandro, R. F.; Santos, M. C.

    2004-05-01

    One of the main sources of errors of GPS measurements is the ionosphere refraction. As a dispersive medium, the ionosphere allow its influence to be computed by using dual frequency receivers. In the case of single frequency receivers it is necessary to use models that tell us how big the ionospheric refraction is. The GPS broadcast message carries parameters of this model, namely Klobuchar model. Dual frequency receivers allow to estimate the influence of ionosphere in the GPS signal by the computation of TEC (Total Electron Content) values, that have a direct relationship with the magnitude of the delay caused by the ionosphere. One alternative is to create a regional model based on a network of dual frequency receivers. In this case, the regional behaviour of ionosphere is modelled in a way that it is possible to estimate the TEC values into or near this region. This regional model can be based on polynomials, for example. In this work we will present a Neural Network-based model to the regional computation of TEC. The advantage of using a Neural Network is that it is not necessary to have a great knowledge on the behaviour of the modelled surface due to the adaptation capability of neural networks training process, that is an iterative adjust of the synaptic weights in function of residuals, using the training parameters. Therefore, the previous knowledge of the modelled phenomena is important to define what kind of and how many parameters are needed to train the neural network so that reasonable results are obtained from the estimations. We have used data from the GPS tracking network in Brazil, and we have tested the accuracy of the new model to all locations where there is a station, accessing the efficiency of the model everywhere. TEC values were computed for each station of the network. After that the training parameters data set for the test station was formed, with the TEC values of all others (all stations, except the test one). The Neural Network was

  13. Design, Implementation and Optimization of Innovative Internet Access Networks, based on Fog Computing and Software Defined Networking

    OpenAIRE

    Iotti, Nicola

    2017-01-01

    1. DESIGN In this dissertation we introduce a new approach to Internet access networks in public spaces, such as Wi-Fi network commonly known as Hotspot, based on Fog Computing (or Edge Computing), Software Defined Networking (SDN) and the deployment of Virtual Machines (VM) and Linux containers, on the edge of the network. In this vision we deploy specialized network elements, called Fog Nodes, on the edge of the network, able to virtualize the physical infrastructure and expose APIs to e...

  14. Small-world networks in neuronal populations: a computational perspective.

    Science.gov (United States)

    Zippo, Antonio G; Gelsomino, Giuliana; Van Duin, Pieter; Nencini, Sara; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

    2013-08-01

    The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. A computational study of routing algorithms for realistic transportation networks

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, R.; Marathe, M.V.; Nagel, K.

    1998-12-01

    The authors carry out an experimental analysis of a number of shortest path (routing) algorithms investigated in the context of the TRANSIMS (Transportation Analysis and Simulation System) project. The main focus of the paper is to study how various heuristic and exact solutions, associated data structures affected the computational performance of the software developed especially for realistic transportation networks. For this purpose the authors have used Dallas Fort-Worth road network with very high degree of resolution. The following general results are obtained: (1) they discuss and experimentally analyze various one-one shortest path algorithms, which include classical exact algorithms studied in the literature as well as heuristic solutions that are designed to take into account the geometric structure of the input instances; (2) they describe a number of extensions to the basic shortest path algorithm. These extensions were primarily motivated by practical problems arising in TRANSIMS and ITS (Intelligent Transportation Systems) related technologies. Extensions discussed include--(i) time dependent networks, (ii) multi-modal networks, (iii) networks with public transportation and associated schedules. Computational results are provided to empirically compare the efficiency of various algorithms. The studies indicate that a modified Dijkstra`s algorithm is computationally fast and an excellent candidate for use in various transportation planning applications as well as ITS related technologies.

  16. Improving a Computer Networks Course Using the Partov Simulation Engine

    Science.gov (United States)

    Momeni, B.; Kharrazi, M.

    2012-01-01

    Computer networks courses are hard to teach as there are many details in the protocols and techniques involved that are difficult to grasp. Employing programming assignments as part of the course helps students to obtain a better understanding and gain further insight into the theoretical lectures. In this paper, the Partov simulation engine and…

  17. Biological networks 101: computational modeling for molecular biologists

    NARCIS (Netherlands)

    Scholma, Jetse; Schivo, Stefano; Urquidi Camacho, Ricardo A.; van de Pol, Jan Cornelis; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole

    2014-01-01

    Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that

  18. System/360 Computer Assisted Network Scheduling (CANS) System

    Science.gov (United States)

    Brewer, A. C.

    1972-01-01

    Computer assisted scheduling techniques that produce conflict-free and efficient schedules have been developed and implemented to meet needs of the Manned Space Flight Network. CANS system provides effective management of resources in complex scheduling environment. System is automated resource scheduling, controlling, planning, information storage and retrieval tool.

  19. Fish species recognition using computer vision and a neural network

    NARCIS (Netherlands)

    Storbeck, F.; Daan, B.

    2001-01-01

    A system is described to recognize fish species by computer vision and a neural network program. The vision system measures a number of features of fish as seen by a camera perpendicular to a conveyor belt. The features used here are the widths and heights at various locations along the fish. First

  20. Computing Nash Equilibrium in Wireless Ad Hoc Networks

    DEFF Research Database (Denmark)

    Bulychev, Peter E.; David, Alexandre; Larsen, Kim G.

    2012-01-01

    This paper studies the problem of computing Nash equilibrium in wireless networks modeled by Weighted Timed Automata. Such formalism comes together with a logic that can be used to describe complex features such as timed energy constraints. Our contribution is a method for solving this problem...

  1. High Performance Computing and Networking for Science--Background Paper.

    Science.gov (United States)

    Congress of the U.S., Washington, DC. Office of Technology Assessment.

    The Office of Technology Assessment is conducting an assessment of the effects of new information technologies--including high performance computing, data networking, and mass data archiving--on research and development. This paper offers a view of the issues and their implications for current discussions about Federal supercomputer initiatives…

  2. An Analysis of Attitudes toward Computer Networks and Internet Addiction.

    Science.gov (United States)

    Tsai, Chin-Chung; Lin, Sunny S. J.

    The purpose of this study was to explore the interplay between young people's attitudes toward computer networks and Internet addiction. After analyzing questionnaire responses of an initial sample of 615 Taiwanese high school students, 78 subjects, viewed as possible Internet addicts, were selected for further explorations. It was found that…

  3. A Three-Dimensional Computational Model of Collagen Network Mechanics

    Science.gov (United States)

    Lee, Byoungkoo; Zhou, Xin; Riching, Kristin; Eliceiri, Kevin W.; Keely, Patricia J.; Guelcher, Scott A.; Weaver, Alissa M.; Jiang, Yi

    2014-01-01

    Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions. PMID:25386649

  4. Computer-Supported Modelling of Multi modal Transportation Networks Rationalization

    Directory of Open Access Journals (Sweden)

    Ratko Zelenika

    2007-09-01

    Full Text Available This paper deals with issues of shaping and functioning ofcomputer programs in the modelling and solving of multimoda Itransportation network problems. A methodology of an integrateduse of a programming language for mathematical modellingis defined, as well as spreadsheets for the solving of complexmultimodal transportation network problems. The papercontains a comparison of the partial and integral methods ofsolving multimodal transportation networks. The basic hypothesisset forth in this paper is that the integral method results inbetter multimodal transportation network rationalization effects,whereas a multimodal transportation network modelbased on the integral method, once built, can be used as the basisfor all kinds of transportation problems within multimodaltransport. As opposed to linear transport problems, multimodaltransport network can assume very complex shapes. This papercontains a comparison of the partial and integral approach totransp01tation network solving. In the partial approach, astraightforward model of a transp01tation network, which canbe solved through the use of the Solver computer tool within theExcel spreadsheet inteiface, is quite sufficient. In the solving ofa multimodal transportation problem through the integralmethod, it is necessmy to apply sophisticated mathematicalmodelling programming languages which supp01t the use ofcomplex matrix functions and the processing of a vast amountof variables and limitations. The LINGO programming languageis more abstract than the Excel spreadsheet, and it requiresa certain programming knowledge. The definition andpresentation of a problem logic within Excel, in a manner whichis acceptable to computer software, is an ideal basis for modellingin the LINGO programming language, as well as a fasterand more effective implementation of the mathematical model.This paper provides proof for the fact that it is more rational tosolve the problem of multimodal transportation networks by

  5. Computer network time synchronization the network time protocol on earth and in space

    CERN Document Server

    Mills, David L

    2010-01-01

    Carefully coordinated, reliable, and accurate time synchronization is vital to a wide spectrum of fields-from air and ground traffic control, to buying and selling goods and services, to TV network programming. Ill-gotten time could even lead to the unimaginable and cause DNS caches to expire, leaving the entire Internet to implode on the root servers.Written by the original developer of the Network Time Protocol (NTP), Computer Network Time Synchronization: The Network Time Protocol on Earth and in Space, Second Edition addresses the technological infrastructure of time dissemination, distrib

  6. Computation emerges from adaptive synchronization of networking neurons.

    Directory of Open Access Journals (Sweden)

    Massimiliano Zanin

    Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.

  7. Synchronization-based computation through networks of coupled oscillators

    Directory of Open Access Journals (Sweden)

    Daniel eMalagarriga

    2015-08-01

    Full Text Available The mesoscopic activity of the brain is strongly dynamical, while at the sametime exhibiting remarkable computational capabilities. In order to examinehow these two features coexist, here we show that the patterns of synchronizedoscillations displayed by networks of neural mass models, representing cortical columns, can be usedas substrates for Boolean computation. Our results reveal that different logicaloperations can be implemented by the same neural mass network at different timesfollowing the dynamics of the input. The results are reproduced experimentallywith electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the oscillators responsible for the functioning of the gates. We also show that theinformation-processing capabilities of coupled oscillations go beyond thesimple juxtaposition of logic gates.

  8. Advances in neural networks computational and theoretical issues

    CERN Document Server

    Esposito, Anna; Morabito, Francesco

    2015-01-01

    This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and  bio-inspired memristor-based networks.  Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive, and context-aware Information Communication Technologies.

  9. Connect the dot: Computing feed-links for network extension

    Directory of Open Access Journals (Sweden)

    Boris Aronov

    2011-12-01

    Full Text Available Road network analysis can require distance from points that are not on the network themselves. We study the algorithmic problem of connecting a point inside a face (region of the road network to its boundary while minimizing the detour factor of that point to any point on the boundary of the face. We show that the optimal single connection (feed-link can be computed in O(lambda_7(n log n time, where n is the number of vertices that bounds the face and lambda_7(n is the slightly superlinear maximum length of a Davenport-Schinzel sequence of order 7 on n symbols. We also present approximation results for placing more feed-links, deal with the case that there are obstacles in the face of the road network that contains the point to be connected, and present various related results.

  10. Computational modeling of signal transduction networks: a pedagogical exposition.

    Science.gov (United States)

    Prasad, Ashok

    2012-01-01

    We give a pedagogical introduction to computational modeling of signal transduction networks, starting from explaining the representations of chemical reactions by differential equations via the law of mass action. We discuss elementary biochemical reactions such as Michaelis-Menten enzyme kinetics and cooperative binding, and show how these allow the representation of large networks as systems of differential equations. We discuss the importance of looking for simpler or reduced models, such as network motifs or dynamical motifs within the larger network, and describe methods to obtain qualitative behavior by bifurcation analysis, using freely available continuation software. We then discuss stochastic kinetics and show how to implement easy-to-use methods of rule-based modeling for stochastic simulations. We finally suggest some methods for comprehensive parameter sensitivity analysis, and discuss the insights that it could yield. Examples, including code to try out, are provided based on a paper that modeled Ras kinetics in thymocytes.

  11. A computational method based on CVSS for quantifying the vulnerabilities in computer network

    Directory of Open Access Journals (Sweden)

    Shahriyar Mohammadi

    2014-10-01

    Full Text Available Network vulnerability taxonomy has become increasingly important in the area of information and data exchange not only for its potential use in identification of vulnerabilities but also in their assessment and prioritization. Computer networks play an important role in information and communication infrastructure. However, they are constantly exposed to a variety of vulnerability risks. In their attempts to create secure information exchange systems, scientists have concentrated on understanding the nature and typology of these vulnerabilities. Their efforts aimed at establishing secure networks have led to the development of a variety of methods and techniques for quantifying vulnerability. The objective of the present paper is developing a method based on the second edition of common vulnerability scoring system (CVSS for the quantification of Computer Network vulnerabilities. It is expected that the proposed model will help in the identification and effective management of vulnerabilities by their quantification.

  12. Applying DNA computation to intractable problems in social network analysis.

    Science.gov (United States)

    Chen, Rick C S; Yang, Stephen J H

    2010-09-01

    From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

  13. A modular architecture for transparent computation in recurrent neural networks.

    Science.gov (United States)

    Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

    2017-01-01

    Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Review On Applications Of Neural Network To Computer Vision

    Science.gov (United States)

    Li, Wei; Nasrabadi, Nasser M.

    1989-03-01

    Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

  15. Analysis of Intrusion Detection and Attack Proliferation in Computer Networks

    Science.gov (United States)

    Rangan, Prahalad; Knuth, Kevin H.

    2007-11-01

    One of the popular models to describe computer worm propagation is the Susceptible-Infected (SI) model [1]. This model of worm propagation has been implemented on the simulation toolkit Network Simulator v2 (ns-2) [2]. The ns-2 toolkit has the capability to simulate networks of different topologies. The topology studied in this work, however, is that of a simple star-topology. This work introduces our initial efforts to learn the relevant quantities describing an infection given synthetic data obtained from running the ns-2 worm model. We aim to use Bayesian methods to gain a predictive understanding of how computer infections spread in real world network topologies. This understanding would greatly reinforce dissemination of targeted immunization strategies, which may prevent real-world epidemics. The data consist of reports of infection from a subset of nodes in a large network during an attack. The infection equation obtained from [1] enables us to derive a likelihood function for the infection reports. This prior information can be used in the Bayesian framework to obtain the posterior probabilities for network properties of interest, such as the rate at which nodes contact one another (also referred to as contact rate or scan rate). Our preliminary analyses indicate an effective spread rate of only 1/5th the actual scan rate used for a star-type of topology. This implies that as the population becomes saturated with infected nodes the actual spread rate will become much less than the scan rate used in the simulation.

  16. Computers and networks in the age of globalization

    DEFF Research Database (Denmark)

    Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

    In modernity, an individual identity was constituted from civil society, while in a globalized network society, human identity, if it develops at all, must grow from communal resistance. A communal resistance to an abstract conceptualized world, where there is no possibility for perception...... in a network society; the individual and knowledge-based organizations; human responsibility and technology; and exclusion and regeneration. This volume contains the edited proceedings of the Fifth World Conference on Human Choice and Computers (HCC-5), which was sponsored by the International Federation...

  17. Spatial Analysis Along Networks Statistical and Computational Methods

    CERN Document Server

    Okabe, Atsuyuki

    2012-01-01

    In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Process

  18. Smart photonic networks and computer security for image data

    Science.gov (United States)

    Campello, Jorge; Gill, John T.; Morf, Martin; Flynn, Michael J.

    1998-02-01

    Work reported here is part of a larger project on 'Smart Photonic Networks and Computer Security for Image Data', studying the interactions of coding and security, switching architecture simulations, and basic technologies. Coding and security: coding methods that are appropriate for data security in data fusion networks were investigated. These networks have several characteristics that distinguish them form other currently employed networks, such as Ethernet LANs or the Internet. The most significant characteristics are very high maximum data rates; predominance of image data; narrowcasting - transmission of data form one source to a designated set of receivers; data fusion - combining related data from several sources; simple sensor nodes with limited buffering. These characteristics affect both the lower level network design and the higher level coding methods.Data security encompasses privacy, integrity, reliability, and availability. Privacy, integrity, and reliability can be provided through encryption and coding for error detection and correction. Availability is primarily a network issue; network nodes must be protected against failure or routed around in the case of failure. One of the more promising techniques is the use of 'secret sharing'. We consider this method as a special case of our new space-time code diversity based algorithms for secure communication. These algorithms enable us to exploit parallelism and scalable multiplexing schemes to build photonic network architectures. A number of very high-speed switching and routing architectures and their relationships with very high performance processor architectures were studied. Indications are that routers for very high speed photonic networks can be designed using the very robust and distributed TCP/IP protocol, if suitable processor architecture support is available.

  19. Computers and networks in the age of globalization

    DEFF Research Database (Denmark)

    Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

    in a network society; the individual and knowledge-based organizations; human responsibility and technology; and exclusion and regeneration. This volume contains the edited proceedings of the Fifth World Conference on Human Choice and Computers (HCC-5), which was sponsored by the International Federation...... for Information Processing (IFIP) and held in Geneva, Switzerland in August 1998. Since the first HCC conference in 1974, IFIP's Technical Committee 9 has endeavoured to set the agenda for human choices and human actions vis-a-vis computers....

  20. Computer, Network, Software, and Hardware Engineering with Applications

    CERN Document Server

    Schneidewind, Norman F

    2012-01-01

    There are many books on computers, networks, and software engineering but none that integrate the three with applications. Integration is important because, increasingly, software dominates the performance, reliability, maintainability, and availability of complex computer and systems. Books on software engineering typically portray software as if it exists in a vacuum with no relationship to the wider system. This is wrong because a system is more than software. It is comprised of people, organizations, processes, hardware, and software. All of these components must be considered in an integr

  1. Multi-objective optimization in computer networks using metaheuristics

    CERN Document Server

    Donoso, Yezid

    2007-01-01

    Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

  2. Advances in neural networks computational intelligence for ICT

    CERN Document Server

    Esposito, Anna; Morabito, Francesco; Pasero, Eros

    2016-01-01

    This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

  3. Computers and networks in the age of globalization

    DEFF Research Database (Denmark)

    Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

    their lives in a diversity of social and cultural contexts. In so doing, the book tries to imagine in what kind of networks humans may choose and act based on the knowledge and empirical evidence presented in the papers. The topics covered in the book include: people and their changing values; citizens...... in a network society; the individual and knowledge-based organizations; human responsibility and technology; and exclusion and regeneration. This volume contains the edited proceedings of the Fifth World Conference on Human Choice and Computers (HCC-5), which was sponsored by the International Federation...... for Information Processing (IFIP) and held in Geneva, Switzerland in August 1998. Since the first HCC conference in 1974, IFIP's Technical Committee 9 has endeavoured to set the agenda for human choices and human actions vis-a-vis computers....

  4. CONCEPTUAL GENERALIZATION OF STRUCTURAL ORGANIZATION OF COMPUTER NETWORKS MEDICAL SCHOOL

    Directory of Open Access Journals (Sweden)

    O. P. Mintser

    2014-01-01

    Full Text Available The basic principles of the structural organization of computer networks in schools are presented. The questions of universities integration’s in the modern infrastructure of the information society are justified. Details the structural organizations of computer networks are presented. The effectiveness of implementing automated library information systems is shown. The big dynamical growths of technical and personal readiness of students to use virtual educational space are presented. In this regard, universities are required to provide advance information on filling the educational environment of modern virtual university, including multimedia resources for industry professional education programs. Based on information and educational environments virtual representations of universities should be formed distributed resource centers that will avoid duplication of effort on the development of innovative educational technologies, will provide a mutual exchange of results and further development of an open continuous professional education, providing accessibility, modularity and mobility training and retraining specialists.

  5. Biological networks 101: computational modeling for molecular biologists.

    Science.gov (United States)

    Scholma, Jetse; Schivo, Stefano; Urquidi Camacho, Ricardo A; van de Pol, Jaco; Karperien, Marcel; Post, Janine N

    2014-01-01

    Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression. © 2013 Elsevier B.V. All rights reserved.

  6. Computational study of noise in a large signal transduction network

    Directory of Open Access Journals (Sweden)

    Ruohonen Keijo

    2011-06-01

    Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

  7. Enhancing the Understanding of Computer Networking Courses through Software Tools

    OpenAIRE

    Dafalla, Z. I.; Balaji, R. D.

    2015-01-01

    Computer networking is an important specialization in Information and Communication Technologies. However imparting the right knowledge to students can be a challenging task due to the fact that there is not enough time to deliver lengthy labs during normal lecture hours. Augmenting the use of physical machines with software tools help the students to learn beyond the limited lab sessions within the environment of higher Institutions of learning throughout the world. The Institutions focus mo...

  8. Computational tools for large-scale biological network analysis

    OpenAIRE

    Pinto, José Pedro Basto Gouveia Pereira

    2012-01-01

    Tese de doutoramento em Informática The surge of the field of Bioinformatics, among other contributions, provided biological researchers with powerful computational methods for processing and analysing the large amount of data coming from recent biological experimental techniques such as genome sequencing and other omics. Naturally, this led to the opening of new avenues of biological research among which is included the analysis of large-scale biological networks. The an...

  9. Computers and networks in the age of globalization

    DEFF Research Database (Denmark)

    Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

    In modernity, an individual identity was constituted from civil society, while in a globalized network society, human identity, if it develops at all, must grow from communal resistance. A communal resistance to an abstract conceptualized world, where there is no possibility for perception...... their lives in a diversity of social and cultural contexts. In so doing, the book tries to imagine in what kind of networks humans may choose and act based on the knowledge and empirical evidence presented in the papers. The topics covered in the book include: people and their changing values; citizens...... in a network society; the individual and knowledge-based organizations; human responsibility and technology; and exclusion and regeneration. This volume contains the edited proceedings of the Fifth World Conference on Human Choice and Computers (HCC-5), which was sponsored by the International Federation...

  10. Computer simulation of randomly cross-linked polymer networks

    CERN Document Server

    Williams, T P

    2002-01-01

    In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneiti...

  11. An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking

    Directory of Open Access Journals (Sweden)

    Hyunhun Cho

    2015-05-01

    Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.

  12. Deep Space Network (DSN), Network Operations Control Center (NOCC) computer-human interfaces

    Science.gov (United States)

    Ellman, Alvin; Carlton, Magdi

    1993-01-01

    The Network Operations Control Center (NOCC) of the DSN is responsible for scheduling the resources of DSN, and monitoring all multi-mission spacecraft tracking activities in real-time. Operations performs this job with computer systems at JPL connected to over 100 computers at Goldstone, Australia and Spain. The old computer system became obsolete, and the first version of the new system was installed in 1991. Significant improvements for the computer-human interfaces became the dominant theme for the replacement project. Major issues required innovating problem solving. Among these issues were: How to present several thousand data elements on displays without overloading the operator? What is the best graphical representation of DSN end-to-end data flow? How to operate the system without memorizing mnemonics of hundreds of operator directives? Which computing environment will meet the competing performance requirements? This paper presents the technical challenges, engineering solutions, and results of the NOCC computer-human interface design.

  13. A Study of the Impact of Virtualization on the Computer Networks

    OpenAIRE

    Timalsena, Pratik

    2013-01-01

    Virtualization is an imminent sector of the Information and Technology in the peresent world. It is advancing and being popuraly implemented world wide. Computer network is not isolated from the global impact of the virtualization. The virtualization is being deployed on the computer networks in a great extent. In general, virtualization is an inevitable tool for computer networks. This report presents a surfacial idea about the impact of the virtualization on the computer network. The report...

  14. Application of artificial neural networks in computer-aided diagnosis.

    Science.gov (United States)

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  15. Open Problems in Network-aware Data Management in Exa-scale Computing and Terabit Networking Era

    Energy Technology Data Exchange (ETDEWEB)

    Balman, Mehmet; Byna, Surendra

    2011-12-06

    Accessing and managing large amounts of data is a great challenge in collaborative computing environments where resources and users are geographically distributed. Recent advances in network technology led to next-generation high-performance networks, allowing high-bandwidth connectivity. Efficient use of the network infrastructure is necessary in order to address the increasing data and compute requirements of large-scale applications. We discuss several open problems, evaluate emerging trends, and articulate our perspectives in network-aware data management.

  16. Line-plane broadcasting in a data communications network of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Berg, Jeremy E.; Blocksome, Michael A.; Smith, Brian E.

    2010-06-08

    Methods, apparatus, and products are disclosed for line-plane broadcasting in a data communications network of a parallel computer, the parallel computer comprising a plurality of compute nodes connected together through the network, the network optimized for point to point data communications and characterized by at least a first dimension, a second dimension, and a third dimension, that include: initiating, by a broadcasting compute node, a broadcast operation, including sending a message to all of the compute nodes along an axis of the first dimension for the network; sending, by each compute node along the axis of the first dimension, the message to all of the compute nodes along an axis of the second dimension for the network; and sending, by each compute node along the axis of the second dimension, the message to all of the compute nodes along an axis of the third dimension for the network.

  17. Advanced Scientific Computing Research Network Requirements: ASCR Network Requirements Review Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Bacon, Charles [Argonne National Lab. (ANL), Argonne, IL (United States); Bell, Greg [ESnet, Berkeley, CA (United States); Canon, Shane [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Dart, Eli [ESnet, Berkeley, CA (United States); Dattoria, Vince [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Goodwin, Dave [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Lee, Jason [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Hicks, Susan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Holohan, Ed [Argonne National Lab. (ANL), Argonne, IL (United States); Klasky, Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lauzon, Carolyn [Dept. of Energy (DOE), Washington DC (United States). Office of Science. Advanced Scientific Computing Research (ASCR); Rogers, Jim [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Skinner, David [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Tierney, Brian [ESnet, Berkeley, CA (United States)

    2013-03-08

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In October 2012, ESnet and the Office of Advanced Scientific Computing Research (ASCR) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the ASCR program office. The requirements identified at the review are summarized in the Findings section, and are described in more detail in the body of the report.

  18. [Forensic evidence-based medicine in computer communication networks].

    Science.gov (United States)

    Qiu, Yun-Liang; Peng, Ming-Qi

    2013-12-01

    As an important component of judicial expertise, forensic science is broad and highly specialized. With development of network technology, increasement of information resources, and improvement of people's legal consciousness, forensic scientists encounter many new problems, and have been required to meet higher evidentiary standards in litigation. In view of this, evidence-based concept should be established in forensic medicine. We should find the most suitable method in forensic science field and other related area to solve specific problems in the evidence-based mode. Evidence-based practice can solve the problems in legal medical field, and it will play a great role in promoting the progress and development of forensic science. This article reviews the basic theory of evidence-based medicine and its effect, way, method, and evaluation in the forensic medicine in order to discuss the application value of forensic evidence-based medicine in computer communication networks.

  19. Computational analysis of protein interaction networks for infectious diseases.

    Science.gov (United States)

    Pan, Archana; Lahiri, Chandrajit; Rajendiran, Anjana; Shanmugham, Buvaneswari

    2016-05-01

    Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. Reducing Computational Overhead of Network Coding with Intrinsic Information Conveying

    DEFF Research Database (Denmark)

    Heide, Janus; Zhang, Qi; Pedersen, Morten V.

    This paper investigated the possibility of intrinsic information conveying in network coding systems. The information is embedded into the coding vector by constructing the vector based on a set of predefined rules. This information can subsequently be retrieved by any receiver. The starting point...... is RLNC (Random Linear Network Coding) and the goal is to reduce the amount of coding operations both at the coding and decoding node, and at the same time remove the need for dedicated signaling messages. In a traditional RLNC system, coding operation takes up significant computational resources and adds...... to the overall energy consumption, which is particular problematic for mobile battery-driven devices. In RLNC coding is performed over a FF (Finite Field). We propose to divide this field into sub fields, and let each sub field signify some information or state. In order to embed the information correctly...

  1. Symbolic dynamics and computation in model gene networks.

    Science.gov (United States)

    Edwards, R.; Siegelmann, H. T.; Aziza, K.; Glass, L.

    2001-03-01

    We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time. Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet. A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary. The union of the words defines the language. Letters and words correspond to analytically computable Poincare maps of the equation. This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language. Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics. This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation. (c) 2001 American Institute of Physics.

  2. Trajectory Based Optimal Segment Computation in Road Network Databases

    DEFF Research Database (Denmark)

    Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.

    Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions tha...... that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....

  3. NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Kontkanen Petri

    2007-01-01

    Full Text Available Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks.

  4. Combining MLP and Using Decision Tree in Order to Detect the Intrusion into Computer Networks

    OpenAIRE

    Saba Sedigh Rad; Alireza Zebarjad

    2013-01-01

    The security of computer networks has an important role in computer systems. The increasing use of computer networks results in penetration and destruction of systems by system operations. So, in order to keep the systems away from these hazards, it is essential to use the intrusion detection system (IDS). This intrusion detection is done in order to detect the illicit use and misuse and to avoid damages to the systems and computer networks by both the external and internal intruders. Intrusi...

  5. Complex network problems in physics, computer science and biology

    Science.gov (United States)

    Cojocaru, Radu Ionut

    There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe

  6. Implications of computer networking and the Internet for nurse education.

    Science.gov (United States)

    Ward, R

    1997-06-01

    This paper sets out the history of computer networking and its use in nursing and health care education, and places this in its wider historical and social context. The increasing availability and use of computer networks and the internet are producing a changing climate in education as well as in health care. Moves away from traditional face-to-face teaching with a campus institution to widely distributed interactive multimedia learning will affect the roles of students and teachers. The use of electronic mail, mailing lists and the World Wide Web are specifically considered, along with changes to library and information management skills, research methods, journal publication and the like. Issues about the quality, as well as quantity, of information available, are considered. As more and more organizations and institutions begin to use electronic communication methods, it becomes an increasingly important part of the curriculum at all levels, and may lead to fundamental changes in geographical and professional boundaries. A glossary of terms is provided for those not familiar with the technology, along with the contact details for mailing lists and World Wide Web pages mentioned.

  7. Optimization of stochastic discrete systems and control on complex networks computational networks

    CERN Document Server

    Lozovanu, Dmitrii

    2014-01-01

    This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...

  8. Applied and computational harmonic analysis on graphs and networks

    Science.gov (United States)

    Irion, Jeff; Saito, Naoki

    2015-09-01

    In recent years, the advent of new sensor technologies and social network infrastructure has provided huge opportunities and challenges for analyzing data recorded on such networks. In the case of data on regular lattices, computational harmonic analysis tools such as the Fourier and wavelet transforms have well-developed theories and proven track records of success. It is therefore quite important to extend such tools from the classical setting of regular lattices to the more general setting of graphs and networks. In this article, we first review basics of graph Laplacian matrices, whose eigenpairs are often interpreted as the frequencies and the Fourier basis vectors on a given graph. We point out, however, that such an interpretation is misleading unless the underlying graph is either an unweighted path or cycle. We then discuss our recent effort of constructing multiscale basis dictionaries on a graph, including the Hierarchical Graph Laplacian Eigenbasis Dictionary and the Generalized Haar-Walsh Wavelet Packet Dictionary, which are viewed as generalizations of the classical hierarchical block DCTs and the Haar-Walsh wavelet packets, respectively, to the graph setting. Finally, we demonstrate the usefulness of our dictionaries by using them to simultaneously segment and denoise 1-D noisy signals sampled on regular lattices, a problem where classical tools have difficulty.

  9. Eye tracking using artificial neural networks for human computer interaction.

    Science.gov (United States)

    Demjén, E; Aboši, V; Tomori, Z

    2011-01-01

    This paper describes an ongoing project that has the aim to develop a low cost application to replace a computer mouse for people with physical impairment. The application is based on an eye tracking algorithm and assumes that the camera and the head position are fixed. Color tracking and template matching methods are used for pupil detection. Calibration is provided by neural networks as well as by parametric interpolation methods. Neural networks use back-propagation for learning and bipolar sigmoid function is chosen as the activation function. The user's eye is scanned with a simple web camera with backlight compensation which is attached to a head fixation device. Neural networks significantly outperform parametric interpolation techniques: 1) the calibration procedure is faster as they require less calibration marks and 2) cursor control is more precise. The system in its current stage of development is able to distinguish regions at least on the level of desktop icons. The main limitation of the proposed method is the lack of head-pose invariance and its relative sensitivity to illumination (especially to incidental pupil reflections).

  10. Teaching Advanced Concepts in Computer Networks: VNUML-UM Virtualization Tool

    Science.gov (United States)

    Ruiz-Martinez, A.; Pereniguez-Garcia, F.; Marin-Lopez, R.; Ruiz-Martinez, P. M.; Skarmeta-Gomez, A. F.

    2013-01-01

    In the teaching of computer networks the main problem that arises is the high price and limited number of network devices the students can work with in the laboratories. Nowadays, with virtualization we can overcome this limitation. In this paper, we present a methodology that allows students to learn advanced computer network concepts through…

  11. High-performance computing and networking as tools for accurate emission computed tomography reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Passeri, A. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Formiconi, A.R. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); De Cristofaro, M.T.E.R. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Pupi, A. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Meldolesi, U. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy)

    1997-04-01

    It is well known that the quantitative potential of emission computed tomography (ECT) relies on the ability to compensate for resolution, attenuation and scatter effects. Reconstruction algorithms which are able to take these effects into account are highly demanding in terms of computing resources. The reported work aimed to investigate the use of a parallel high-performance computing platform for ECT reconstruction taking into account an accurate model of the acquisition of single-photon emission tomographic (SPET) data. An iterative algorithm with an accurate model of the variable system response was ported on the MIMD (Multiple Instruction Multiple Data) parallel architecture of a 64-node Cray T3D massively parallel computer. The system was organized to make it easily accessible even from low-cost PC-based workstations through standard TCP/IP networking. A complete brain study of 30 (64 x 64) slices could be reconstructed from a set of 90 (64 x 64) projections with ten iterations of the conjugate gradients algorithm in 9 s, corresponding to an actual speed-up factor of 135. This work demonstrated the possibility of exploiting remote high-performance computing and networking resources from hospital sites by means of low-cost workstations using standard communication protocols without particular problems for routine use. The achievable speed-up factors allow the assessment of the clinical benefit of advanced reconstruction techniques which require a heavy computational burden for the compensation effects such as variable spatial resolution, scatter and attenuation. The possibility of using the same software on the same hardware platform with data acquired in different laboratories with various kinds of SPET instrumentation is appealing for software quality control and for the evaluation of the clinical impact of the reconstruction methods. (orig.). With 4 figs., 1 tab.

  12. Computationally efficient measure of topological redundancy of biological and social networks

    Science.gov (United States)

    Albert, Réka; Dasgupta, Bhaskar; Hegde, Rashmi; Sivanathan, Gowri Sangeetha; Gitter, Anthony; Gürsoy, Gamze; Paul, Pradyut; Sontag, Eduardo

    2011-09-01

    It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

  13. Electricity market price forecasting by grid computing optimizing artificial neural networks

    OpenAIRE

    Niimura, T.; Ozawa, K.; Sakamoto, N.

    2007-01-01

    This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using re...

  14. 10 CFR 73.54 - Protection of digital computer and communication systems and networks.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 2 2010-01-01 2010-01-01 false Protection of digital computer and communication systems... computer and communication systems and networks. By November 23, 2009 each licensee currently licensed to... provide high assurance that digital computer and communication systems and networks are adequately...

  15. DIMACS Workshop on Interconnection Networks and Mapping, and Scheduling Parallel Computations

    CERN Document Server

    Rosenberg, Arnold L; Sotteau, Dominique; NSF Science and Technology Center in Discrete Mathematics and Theoretical Computer Science; Interconnection networks and mapping and scheduling parallel computations

    1995-01-01

    The interconnection network is one of the most basic components of a massively parallel computer system. Such systems consist of hundreds or thousands of processors interconnected to work cooperatively on computations. One of the central problems in parallel computing is the task of mapping a collection of processes onto the processors and routing network of a parallel machine. Once this mapping is done, it is critical to schedule computations within and communication among processor from universities and laboratories, as well as practitioners involved in the design, implementation, and application of massively parallel systems. Focusing on interconnection networks of parallel architectures of today and of the near future , the book includes topics such as network topologies,network properties, message routing, network embeddings, network emulation, mappings, and efficient scheduling. inputs for a process are available where and when the process is scheduled to be computed. This book contains the refereed pro...

  16. Dynamic Security Assessment Of Computer Networks In Siem-Systems

    Directory of Open Access Journals (Sweden)

    Elena Vladimirovna Doynikova

    2015-10-01

    Full Text Available The paper suggests an approach to the security assessment of computer networks. The approach is based on attack graphs and intended for Security Information and Events Management systems (SIEM-systems. Key feature of the approach consists in the application of the multilevel security metrics taxonomy. The taxonomy allows definition of the system profile according to the input data used for the metrics calculation and techniques of security metrics calculation. This allows specification of the security assessment in near real time, identification of previous and future attacker steps, identification of attackers goals and characteristics. A security assessment system prototype is implemented for the suggested approach. Analysis of its operation is conducted for several attack scenarios.

  17. Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

    CERN Document Server

    Knabe, Johannes F

    2013-01-01

    Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

  18. Computers and networks in the age of globalization

    DEFF Research Database (Denmark)

    Bloch Rasmussen, Leif; Beardon, Colin; Munari, Silvio

    In modernity, an individual identity was constituted from civil society, while in a globalized network society, human identity, if it develops at all, must grow from communal resistance. A communal resistance to an abstract conceptualized world, where there is no possibility for perception...... and experience of power and therefore no possibility for human choice and action, is of utmost importance for the constituting of human choosers and actors. This book therefore sets focus on those human choosers and actors wishing to read and enjoy the papers as they are actually perceiving and experiencing...... for Information Processing (IFIP) and held in Geneva, Switzerland in August 1998. Since the first HCC conference in 1974, IFIP's Technical Committee 9 has endeavoured to set the agenda for human choices and human actions vis-a-vis computers....

  19. WaveJava: Wavelet-based network computing

    Science.gov (United States)

    Ma, Kun; Jiao, Licheng; Shi, Zhuoer

    1997-04-01

    Wavelet is a powerful theory, but its successful application still needs suitable programming tools. Java is a simple, object-oriented, distributed, interpreted, robust, secure, architecture-neutral, portable, high-performance, multi- threaded, dynamic language. This paper addresses the design and development of a cross-platform software environment for experimenting and applying wavelet theory. WaveJava, a wavelet class library designed by the object-orient programming, is developed to take advantage of the wavelets features, such as multi-resolution analysis and parallel processing in the networking computing. A new application architecture is designed for the net-wide distributed client-server environment. The data are transmitted with multi-resolution packets. At the distributed sites around the net, these data packets are done the matching or recognition processing in parallel. The results are fed back to determine the next operation. So, the more robust results can be arrived quickly. The WaveJava is easy to use and expand for special application. This paper gives a solution for the distributed fingerprint information processing system. It also fits for some other net-base multimedia information processing, such as network library, remote teaching and filmless picture archiving and communications.

  20. Integration of a network aware traffic generation device into a computer network emulation platform

    CSIR Research Space (South Africa)

    Von Solms, S

    2014-07-01

    Full Text Available Flexible, open source network emulation tools can provide network researchers with significant benefits regarding network behaviour and performance. The evaluation of these networks can benefit greatly from the integration of realistic, network...

  1. Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

    Science.gov (United States)

    Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

    2017-11-01

    Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

  2. Providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

    Energy Technology Data Exchange (ETDEWEB)

    Archer, Charles J.; Faraj, Daniel A.; Inglett, Todd A.; Ratterman, Joseph D.

    2018-01-30

    Methods, apparatus, and products are disclosed for providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: receiving a network packet in a compute node, the network packet specifying a destination compute node; selecting, in dependence upon the destination compute node, at least one of the links for the compute node along which to forward the network packet toward the destination compute node; and forwarding the network packet along the selected link to the adjacent compute node connected to the compute node through the selected link.

  3. A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

    Energy Technology Data Exchange (ETDEWEB)

    Potok, Thomas E [ORNL; Schuman, Catherine D [ORNL; Young, Steven R [ORNL; Patton, Robert M [ORNL; Spedalieri, Federico [University of Southern California, Information Sciences Institute; Liu, Jeremy [University of Southern California, Information Sciences Institute; Yao, Ke-Thia [University of Southern California, Information Sciences Institute; Rose, Garrett [University of Tennessee (UT); Chakma, Gangotree [University of Tennessee (UT)

    2016-01-01

    Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

  4. Locating hardware faults in a data communications network of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Megerian, Mark G.; Ratterman, Joseph D.; Smith, Brian E.

    2010-01-12

    Hardware faults location in a data communications network of a parallel computer. Such a parallel computer includes a plurality of compute nodes and a data communications network that couples the compute nodes for data communications and organizes the compute node as a tree. Locating hardware faults includes identifying a next compute node as a parent node and a root of a parent test tree, identifying for each child compute node of the parent node a child test tree having the child compute node as root, running a same test suite on the parent test tree and each child test tree, and identifying the parent compute node as having a defective link connected from the parent compute node to a child compute node if the test suite fails on the parent test tree and succeeds on all the child test trees.

  5. Network Intelligence Based on Network State Information for Connected Vehicles Utilizing Fog Computing

    Directory of Open Access Journals (Sweden)

    Seongjin Park

    2017-01-01

    Full Text Available This paper proposes a method to take advantage of fog computing and SDN in the connected vehicle environment, where communication channels are unstable and the topology changes frequently. A controller knows the current state of the network by maintaining the most recent network topology. Of all the information collected by the controller in the mobile environment, node mobility information is particularly important. Thus, we divide nodes into three classes according to their mobility types and use their related attributes to efficiently manage the mobile connections. Our approach utilizes mobility information to reduce control message overhead by adjusting the period of beacon messages and to support efficient failure recovery. One is to recover the connection failures using only mobility information, and the other is to suggest a real-time scheduling algorithm to recover the services for the vehicles that lost connection in the case of a fog server failure. A real-time scheduling method is first described and then evaluated. The results show that our scheme is effective in the connected vehicle environment. We then demonstrate the reduction of control overhead and the connection recovery by using a network simulator. The simulation results show that control message overhead and failure recovery time are decreased by approximately 55% and 5%, respectively.

  6. Energy Research and Development Administration Ad Hoc Computer Networking Group: experimental program

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, I.

    1975-03-19

    The Ad Hoc Computer Networking Group was established to investigate the potential advantages and costs of newer forms of remote resource sharing and computer networking. The areas of research and investigation that are within the scope of the ERDA CNG are described. (GHT)

  7. Toward a Practical Technique to Halt Multiple Virus Outbreaks on Computer Networks

    OpenAIRE

    Hole, Kjell Jørgen

    2012-01-01

    The author analyzes a technique to prevent multiple simultaneous virus epidemics on any vulnerable computer network with inhomogeneous topology. The technique immunizes a small fraction of the computers and utilizes diverse software platforms to halt the virus outbreaks. The halting technique is of practical interest since a network's detailed topology need not be known.

  8. Synchronized Pair Configuration in Virtualization-Based Lab for Learning Computer Networks

    Science.gov (United States)

    Kongcharoen, Chaknarin; Hwang, Wu-Yuin; Ghinea, Gheorghita

    2017-01-01

    More studies are concentrating on using virtualization-based labs to facilitate computer or network learning concepts. Some benefits are lower hardware costs and greater flexibility in reconfiguring computer and network environments. However, few studies have investigated effective mechanisms for using virtualization fully for collaboration.…

  9. Finding Multi-step Attacks in Computer Networks using Heuristic Search and Mobile Ambients

    NARCIS (Netherlands)

    Nunes Leal Franqueira, V.

    2009-01-01

    An important aspect of IT security governance is the proactive and continuous identification of possible attacks in computer networks. This is complicated due to the complexity and size of networks, and due to the fact that usually network attacks are performed in several steps. This thesis proposes

  10. Mechanism Aligning (Gateway) Between Operating System Based on Computer Networks in Unocal

    OpenAIRE

    Vera Morina Oktavia Carla; Drs.Ida Ayu Wiastiti, M.KOM Drs.Ida Ayu Wiastiti, M.KOM

    1997-01-01

    Computer network is a system that allows for the sharing of information among users.Use of operating systems in the network settings is an absolute must. Replacement ofthe network operating system should really consider the possibility of impacts.Aligning is one solution in order to connect two different systems.

  11. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    Science.gov (United States)

    1987-10-01

    include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

  12. Discussion on the Technology and Method of Computer Network Security Management

    Science.gov (United States)

    Zhou, Jianlei

    2017-09-01

    With the rapid development of information technology, the application of computer network technology has penetrated all aspects of society, changed people's way of life work to a certain extent, brought great convenience to people. But computer network technology is not a panacea, it can promote the function of social development, but also can cause damage to the community and the country. Due to computer network’ openness, easiness of sharing and other characteristics, it had a very negative impact on the computer network security, especially the loopholes in the technical aspects can cause damage on the network information. Based on this, this paper will do a brief analysis on the computer network security management problems and security measures.

  13. S-TSP: a novel routing algorithm for In-network processing of recursive computation in wireless sensor networks

    Science.gov (United States)

    Tang, Tingfang; Guo, Peng; Liu, Xuefeng

    2016-10-01

    In-network processing is an efficient way to reduce the transmission cost in wireless sensor networks (WSNs). The in-network processing of many domain-specific computation tasks in WSNs usually requires to losslessly distribute the computation of the tasks into the sensor nodes, which is however usually not easy. In this paper we are concerned with such kind of tasks whose computation can only be partitioned into recursive computation mode. To distribute the recursive computations into WSNs, it is required to design an appropriate single in-network processing path, along which the intermediate data is forwarded and updated in the WSNs. We address the recursive computation with constant size of computation result, e.g., distributed least square estimation (D-LSE). Finding the optimal in-network processing path to minimize the total transmission cost in WSNs, is a new problem and seldom studied before. To solve it, we propose a novel routing algorithm called as S-TSP, and compare it with some other greedy algorithms. Extensive simulations are conducted, and the results show the good performance of the proposed S-TSP algorithm.

  14. Algorithm-structured computer arrays and networks architectures and processes for images, percepts, models, information

    CERN Document Server

    Uhr, Leonard

    1984-01-01

    Computer Science and Applied Mathematics: Algorithm-Structured Computer Arrays and Networks: Architectures and Processes for Images, Percepts, Models, Information examines the parallel-array, pipeline, and other network multi-computers.This book describes and explores arrays and networks, those built, being designed, or proposed. The problems of developing higher-level languages for systems and designing algorithm, program, data flow, and computer structure are also discussed. This text likewise describes several sequences of successively more general attempts to combine the power of arrays wi

  15. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Adaptive Management of Computing and Network Resources for Spacecraft Systems

    Science.gov (United States)

    Pfarr, Barbara; Welch, Lonnie R.; Detter, Ryan; Tjaden, Brett; Huh, Eui-Nam; Szczur, Martha R. (Technical Monitor)

    2000-01-01

    It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.

  17. Computational approach in estimating the need of ditch network maintenance

    Science.gov (United States)

    Lauren, Ari; Hökkä, Hannu; Launiainen, Samuli; Palviainen, Marjo; Repo, Tapani; Leena, Finer; Piirainen, Sirpa

    2015-04-01

    Ditch network maintenance (DNM), implemented annually in 70 000 ha area in Finland, is the most controversial of all forest management practices. Nationwide, it is estimated to increase the forest growth by 1…3 million m3 per year, but simultaneously to cause 65 000 tons export of suspended solids and 71 tons of phosphorus (P) to water courses. A systematic approach that allows simultaneous quantification of the positive and negative effects of DNM is required. Excess water in the rooting zone slows the gas exchange and decreases biological activity interfering with the forest growth in boreal forested peatlands. DNM is needed when: 1) the excess water in the rooting zone restricts the forest growth before the DNM, and 2) after the DNM the growth restriction ceases or decreases, and 3) the benefits of DNM are greater than the caused adverse effects. Aeration in the rooting zone can be used as a drainage criterion. Aeration is affected by several factors such as meteorological conditions, tree stand properties, hydraulic properties of peat, ditch depth, and ditch spacing. We developed a 2-dimensional DNM simulator that allows the user to adjust these factors and to evaluate their effect on the soil aeration at different distance from the drainage ditch. DNM simulator computes hydrological processes and soil aeration along a water flowpath between two ditches. Applying daily time step it calculates evapotranspiration, snow accumulation and melt, infiltration, soil water storage, ground water level, soil water content, air-filled porosity and runoff. The model performance in hydrology has been tested against independent high frequency field monitoring data. Soil aeration at different distance from the ditch is computed under steady-state assumption using an empirical oxygen consumption model, simulated air-filled porosity, and diffusion coefficient at different depths in soil. Aeration is adequate and forest growth rate is not limited by poor aeration if the

  18. Automatic detection of emerging threats to computer networks

    CSIR Research Space (South Africa)

    McDonald, A

    2015-10-01

    Full Text Available intrusion detection technology is to detect threats to networked information systems and networking infrastructure in an automated fashion, thereby providing an opportunity to deploy countermeasures. This presentation showcases the research and development...

  19. Use of medical information by computer networks raises major concerns about privacy.

    OpenAIRE

    OReilly, M.

    1995-01-01

    The development of computer data-bases and long-distance computer networks is leading to improvements in Canada's health care system. However, these developments come at a cost and require a balancing act between access and confidentiality. Columnist Michael OReilly, who in this article explores the security of computer networks, notes that respect for patients' privacy must be given as high a priority as the ability to see their records in the first place.

  20. Integrating Network Awareness in ATLAS Distributed Computing Using the ANSE Project

    CERN Document Server

    Klimentov, Alexei; The ATLAS collaboration; Petrosyan, Artem; Batista, Jorge Horacio; Mc Kee, Shawn Patrick

    2015-01-01

    A crucial contributor to the success of the massively scaled global computing system that delivers the analysis needs of the LHC experiments is the networking infrastructure upon which the system is built. The experiments have been able to exploit excellent high-bandwidth networking in adapting their computing models for the most efficient utilization of resources. New advanced networking technologies now becoming available such as software defined networking hold the potential of further leveraging the network to optimize workflows and dataflows, through proactive control of the network fabric on the part of high level applications such as experiment workload management and data management systems. End to end monitoring of networks using perfSONAR combined with data flow performance metrics further allows applications to adapt based on real time conditions. We will describe efforts underway in ATLAS on integrating network awareness at the application level, particularly in workload management, building upon ...

  1. Calculating a checksum with inactive networking components in a computing system

    Science.gov (United States)

    Aho, Michael E; Chen, Dong; Eisley, Noel A; Gooding, Thomas M; Heidelberger, Philip; Tauferner, Andrew T

    2015-01-27

    Calculating a checksum utilizing inactive networking components in a computing system, including: identifying, by a checksum distribution manager, an inactive networking component, wherein the inactive networking component includes a checksum calculation engine for computing a checksum; sending, to the inactive networking component by the checksum distribution manager, metadata describing a block of data to be transmitted by an active networking component; calculating, by the inactive networking component, a checksum for the block of data; transmitting, to the checksum distribution manager from the inactive networking component, the checksum for the block of data; and sending, by the active networking component, a data communications message that includes the block of data and the checksum for the block of data.

  2. Managing computer networks using peer-to-peer technologies

    OpenAIRE

    Granville, Lisandro Zambenedetti; Rosa, Diego Moreira da; Panisson, André; Melchiors, Cristina; Almeida, Maria Janilce Bosquiroli; Tarouco,Liane Margarida Rockenbach

    2005-01-01

    Peer-to-peer systems and network management are usually related to each other because the traffic loads of P2P systems have to be controlled to avoid regular network services becoming unavailable due to network congestion. In this context, from a network operation point of view, P2P systems often mean problems. In this article we take a different perspective and look at P2P technologies as an alternative to improve current network management solutions. We introduce an approach where P2P netwo...

  3. Spacelab data analysis using the space plasma computer analysis network (SCAN) system

    Science.gov (United States)

    Green, J. L.

    1984-01-01

    The Space-plasma Computer Analysis Network (SCAN) currently connects a large number of U.S. Spacelab investigators into a common computer network. Used primarily by plasma physics researchers at present, SCAN provides access to Spacelab investigators in other areas of space science, to Spacelab and non-Spacelab correlative data bases, and to large Class VI computational facilities for modeling. SCAN links computers together at remote institutions used by space researchers, utilizing commercially available software for computer-to-computer communications. Started by the NASA's Office of Space Science in mid 1980, SCAN presently contains ten system nodes located at major universities and space research laboratories, with fourteen new nodes projected for the near future. The Stanford University computer gateways allow SCAN users to connect onto the ARPANET and TELENET overseas networks.

  4. Syntactic computations in the language network: Characterising dynamic network properties using representational similarity analysis

    Directory of Open Access Journals (Sweden)

    Lorraine Komisarjevsky Tyler

    2013-05-01

    Full Text Available The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG and posterior middle temporal gyrus (LMTG and the anatomical connections between them. Here we use MEG to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g. …landing planes…, at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA to characterize syntactic information represented in the LIFG and LpMTG over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity.

  5. Computing of network tenacity based on modified binary particle swarm optimization algorithm

    Science.gov (United States)

    Shen, Maoxing; Sun, Chengyu

    2017-05-01

    For rapid calculation of network node tenacity, which can depict the invulnerability performance of network, this paper designs a computational method based on modified binary particle swarm optimization (BPSO) algorithm. Firstly, to improve the astringency of the BPSO algorithm, the algorithm adopted an improved bit transfer probability function and location updating formula. Secondly, algorithm for fitness function value of BPSO based on the breadth-first search is designed. Thirdly, the computing method for network tenacity based on the modified BPSO algorithm is presented. Results of experiment conducted in the Advanced Research Project Agency (ARPA) network and Tactical Support Communication (TCS) network illustrate that the computing method is impactful and high-performance to calculate network tenacity.

  6. In-Network Computation is a Dumb Idea Whose Time Has Come

    KAUST Repository

    Sapio, Amedeo

    2017-11-27

    Programmable data plane hardware creates new opportunities for infusing intelligence into the network. This raises a fundamental question: what kinds of computation should be delegated to the network? In this paper, we discuss the opportunities and challenges for co-designing data center distributed systems with their network layer. We believe that the time has finally come for offloading part of their computation to execute in-network. However, in-network computation tasks must be judiciously crafted to match the limitations of the network machine architecture of programmable devices. With the help of our experiments on machine learning and graph analytics workloads, we identify that aggregation functions raise opportunities to exploit the limited computation power of networking hardware to lessen network congestion and improve the overall application performance. Moreover, as a proof-of-concept, we propose DAIET, a system that performs in-network data aggregation. Experimental results with an initial prototype show a large data reduction ratio (86.9%-89.3%) and a similar decrease in the workers\\' computation time.

  7. ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

    Science.gov (United States)

    Rusakov, Dmitri A.; Savtchenko, Leonid P.

    2017-01-01

    Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT). PMID:28362877

  8. Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

    Science.gov (United States)

    2014-01-01

    Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel

  9. Experimental free-space optical network for massively parallel computers.

    Science.gov (United States)

    Araki, S; Kajita, M; Kasahara, K; Kubota, K; Kurihara, K; Redmond, I; Schenfeld, E; Suzaki, T

    1996-03-10

    A free-space optical interconnection scheme is described for massively parallel processors based on the interconnection-cached network architecture. The optical network operates in a circuit-switching mode. Combined with a packet-switching operation among the circuit-switched optical channels, a high-bandwidth, low-latency network for massively parallel processing results. The design and assembly of a 64-channel experimental prototype is discussed, and operational results are presented.

  10. Effect of anti-virus software on infectious nodes in computer network: A mathematical model

    Science.gov (United States)

    Mishra, Bimal Kumar; Pandey, Samir Kumar

    2012-07-01

    An e-epidemic model of malicious codes in the computer network through vertical transmission is formulated. We have observed that if the basic reproduction number is less than unity, the infected proportion of computer nodes disappear and malicious codes die out and also the malicious codes-free equilibrium is globally asymptotically stable which leads to its eradication. Effect of anti-virus software on the removal of the malicious codes from the computer network is critically analyzed. Analysis and simulation results show some managerial insights that are helpful for the practice of anti-virus in information sharing networks.

  11. Distinguishing humans from computers in the game of go: A complex network approach

    Science.gov (United States)

    Coquidé, C.; Georgeot, B.; Giraud, O.

    2017-08-01

    We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as a tool to implement a Turing-like test for go simulators.

  12. Fair Secure Computation with Reputation Assumptions in the Mobile Social Networks

    Directory of Open Access Journals (Sweden)

    Yilei Wang

    2015-01-01

    Full Text Available With the rapid development of mobile devices and wireless technologies, mobile social networks become increasingly available. People can implement many applications on the basis of mobile social networks. Secure computation, like exchanging information and file sharing, is one of such applications. Fairness in secure computation, which means that either all parties implement the application or none of them does, is deemed as an impossible task in traditional secure computation without mobile social networks. Here we regard the applications in mobile social networks as specific functions and stress on the achievement of fairness on these functions within mobile social networks in the presence of two rational parties. Rational parties value their utilities when they participate in secure computation protocol in mobile social networks. Therefore, we introduce reputation derived from mobile social networks into the utility definition such that rational parties have incentives to implement the applications for a higher utility. To the best of our knowledge, the protocol is the first fair secure computation in mobile social networks. Furthermore, it finishes within constant rounds and allows both parties to know the terminal round.

  13. A Social Network Approach to Provisioning and Management of Cloud Computing Services for Enterprises

    OpenAIRE

    Kuada, Eric; Olesen, Henning

    2011-01-01

    This paper proposes a social network approach to the provisioning and management of cloud computing services termed Opportunistic Cloud Computing Services (OCCS), for enterprises; and presents the research issues that need to be addressed for its implementation. We hypothesise that OCCS will facilitate the adoption process of cloud computing services by enterprises. OCCS deals with the concept of enterprises taking advantage of cloud computing services to meet their business needs without hav...

  14. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    Science.gov (United States)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  15. The development of computer networks: First results from a microeconomic model

    Science.gov (United States)

    Maier, Gunther; Kaufmann, Alexander

    Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model - based on a microeconomic calculus - that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms.

  16. Experimental realization of an entanglement access network and secure multi-party computation

    Science.gov (United States)

    Chang, Xiuying; Deng, Donglin; Yuan, Xinxing; Hou, Panyu; Huang, Yuanyuan; Duan, Luming; Department of Physics, University of Michigan Collaboration; Center for Quantum Information in Tsinghua University Team

    2017-04-01

    To construct a quantum network with many end users, it is critical to have a cost-efficient way to distribute entanglement over different network ends. We demonstrate an entanglement access network, where the expensive resource, the entangled photon source at the telecom wavelength and the core communication channel, is shared by many end users. Using this cost-efficient entanglement access network, we report experimental demonstration of a secure multiparty computation protocol, the privacy-preserving secure sum problem, based on the network quantum cryptography.

  17. Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

    Directory of Open Access Journals (Sweden)

    Taimoor Khan

    2014-01-01

    Full Text Available In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA- based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

  18. Main control computer security model of closed network systems protection against cyber attacks

    Science.gov (United States)

    Seymen, Bilal

    2014-06-01

    The model that brings the data input/output under control in closed network systems, that maintains the system securely, and that controls the flow of information through the Main Control Computer which also brings the network traffic under control against cyber-attacks. The network, which can be controlled single-handedly thanks to the system designed to enable the network users to make data entry into the system or to extract data from the system securely, intends to minimize the security gaps. Moreover, data input/output record can be kept by means of the user account assigned for each user, and it is also possible to carry out retroactive tracking, if requested. Because the measures that need to be taken for each computer on the network regarding cyber security, do require high cost; it has been intended to provide a cost-effective working environment with this model, only if the Main Control Computer has the updated hardware.

  19. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  20. Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

    National Research Council Canada - National Science Library

    Zhaohui Luo; Minghui LiWang; Zhijian Lin; Lianfen Huang; Xiaojiang Du; Mohsen Guizani

    2017-01-01

    Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive...

  1. A computational model of hemodynamic parameters in cortical capillary networks.

    Science.gov (United States)

    Safaeian, Navid; Sellier, Mathieu; David, Tim

    2011-02-21

    The analysis of hemodynamic parameters and functional reactivity of cerebral capillaries is still controversial. To assess the hemodynamic parameters in the cortical capillary network, a generic model was created using 2D voronoi tessellation in which each edge represents a capillary segment. This method is capable of creating an appropriate generic model of cerebral capillary network relating to each part of the brain cortex because the geometric model is able to vary the capillary density. The modeling presented here is based on morphometric parameters extracted from physiological data of the human cortex. The pertinent hemodynamic parameters were obtained by numerical simulation based on effective blood viscosity as a function of hematocrit and microvessel diameter, phase separation and plasma skimming effects. The hemodynamic parameters of capillary networks with two different densities (consistent with the variation of the morphometric data in the human cortical capillary network) were analyzed. The results show pertinent hemodynamic parameters for each model. The heterogeneity (coefficient variation) and the mean value of hematocrits, flow rates and velocities of the both network models were specified. The distributions of blood flow throughout the both models seem to confirm the hypothesis in which all capillaries in a cortical network are recruited at rest (normal condition). The results also demonstrate a discrepancy of the network resistance between two models, which are derived from the difference in the number density of capillary segments between the models. Copyright © 2010 Elsevier Ltd. All rights reserved.

  2. Computational Data Modeling for Network-Constrained Moving Objects

    DEFF Research Database (Denmark)

    Jensen, Christian Søndergaard; Speicys, L.; Kligys, A.

    2003-01-01

    Advances in wireless communications, positioning technology, and other hardware technologies combine to enable a range of applications that use a mobile user’s geo-spatial data to deliver online, location-enhanced services, often referred to as location-based services. Assuming that the service...... users are constrained to a transportation network, this paper develops data structures that model road networks, the mobile users, and stationary objects of interest. The proposed framework encompasses two supplementary road network representations, namely a two-dimensional representation and a graph...

  3. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    OpenAIRE

    Sadik Kamel Gharghan; Rosdiadee Nordin; Mahamod Ismail

    2016-01-01

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the...

  4. THE BLENDED LEARNING ACCOMPLISHMENT OF COMPUTER AND NETWORK ENGINEERING EXPERTISE PROGRAM IN VOCATIONAL SCHOOLS

    OpenAIRE

    Aries Alfian Prasetyo; Setiadi Cahyono Putro; I Made Wirawan

    2016-01-01

    This study aims to (1) describe supporting and inhibiting factors in blended learning implementation for the students of computer and network engineering expertise program and (2) describe the accomplishment level of the implementation. This study is designed as a descriptive study with quantitative approach. The research object is the blended learning implementation in computer and network engineering expertise program in SMK N 1 Baureno Bojonegoro. The research subjects consist of teachers,...

  5. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  6. Change Detection Algorithms for Information Assurance of Computer Networks

    National Research Council Canada - National Science Library

    Cardenas, Alvaro A

    2002-01-01

    .... In this thesis, the author will focus on the detection of three attack scenarios: the spreading of active worms throughout the Internet, distributed denial of service attacks, and routing attacks to wireless ad hoc networks...

  7. Using new edges for anomaly detection in computer networks

    Science.gov (United States)

    Neil, Joshua Charles

    2015-05-19

    Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

  8. Using new edges for anomaly detection in computer networks

    Energy Technology Data Exchange (ETDEWEB)

    Neil, Joshua Charles

    2017-07-04

    Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

  9. A Social Network Approach to Provisioning and Management of Cloud Computing Services for Enterprises

    DEFF Research Database (Denmark)

    Kuada, Eric; Olesen, Henning

    2011-01-01

    This paper proposes a social network approach to the provisioning and management of cloud computing services termed Opportunistic Cloud Computing Services (OCCS), for enterprises; and presents the research issues that need to be addressed for its implementation. We hypothesise that OCCS...... will facilitate the adoption process of cloud computing services by enterprises. OCCS deals with the concept of enterprises taking advantage of cloud computing services to meet their business needs without having to pay or paying a minimal fee for the services. The OCCS network will be modelled and implemented...... as a social network of enterprises collaborating strategically for the provisioning and consumption of cloud computing services without entering into any business agreements. We conclude that it is possible to configure current cloud service technologies and management tools for OCCS but there is a need...

  10. Transport capacity of wireless networks: benefits from multi-access computation coding

    NARCIS (Netherlands)

    Goseling, Jasper; Gastpar, Michael; Weber, Jos H.

    2009-01-01

    We consider the effect on the transport capacity of wireless networks of different physical layer coding mechanisms. We compare the performance of traditional channel coding techniques, turning the wireless network in reliable point-to-point channels, with multi-access computation coding, in which

  11. A Computational Investigation of Cohesion and Lexical Network Density in L2 Writing

    Science.gov (United States)

    Green, Clarence

    2012-01-01

    This study used a new computational linguistics tool, the Coh-Metrix, to investigate and measure the differences in cohesion and lexical network density between native speaker and non-native speaker writing, as well as to investigate L2 proficiency level differences in cohesion and lexical network density. This study analyzed data from three…

  12. Factors Impacting Adult Learner Achievement in a Technology Certificate Program on Computer Networks

    Science.gov (United States)

    Delialioglu, Omer; Cakir, Hasan; Bichelmeyer, Barbara A.; Dennis, Alan R.; Duffy, Thomas M.

    2010-01-01

    This study investigates the factors impacting the achievement of adult learners in a technology certificate program on computer networks. We studied 2442 participants in 256 institutions. The participants were older than age 18 and were enrolled in the Cisco Certified Network Associate (CCNA) technology training program as "non-degree" or…

  13. Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

    OpenAIRE

    Li, Chencheng; Zhou, Pan; Zhou, Yingxue; Bian, Kaigui; Jiang, Tao; Rahardja, Susanto

    2016-01-01

    With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from ...

  14. A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces

    OpenAIRE

    Johnston, Matthew D.

    2017-01-01

    Recent work of M.D. Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the prog...

  15. Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation

    Science.gov (United States)

    Aminia, A. S.

    2017-09-01

    Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.

  16. Networking and computing: From the Chip to the Web

    OpenAIRE

    Kropf, Peter; Plaice, John

    2010-01-01

    There are two fundamental trends in the development of computers: the miniaturization of components and the increase in communication capacities. The combination of these two trends is leading to a qualitatively new situation, in which the same techniques will be applicable at all scales of computing, be they at the chip level or at the level of the World Wide Web (WWW).

  17. THE IMPROVEMENT OF COMPUTER NETWORK PERFORMANCE WITH BANDWIDTH MANAGEMENT IN KEMURNIAN II SENIOR HIGH SCHOOL

    Directory of Open Access Journals (Sweden)

    Bayu Kanigoro

    2012-05-01

    Full Text Available This research describes the improvement of computer network performance with bandwidth management in Kemurnian II Senior High School. The main issue of this research is the absence of bandwidth division on computer, which makes user who is downloading data, the provided bandwidth will be absorbed by the user. It leads other users do not get the bandwidth. Besides that, it has been done IP address division on each room, such as computer, teacher and administration room for supporting learning process in Kemurnian II Senior High School, so wireless network is needed. The method is location observation and interview with related parties in Kemurnian II Senior High School, the network analysis has run and designed a new topology network including the wireless network along with its configuration and separation bandwidth on microtic router and its limitation. The result is network traffic on Kemurnian II Senior High School can be shared evenly to each user; IX and IIX traffic are separated, which improve the speed on network access at school and the implementation of wireless network.Keywords: Bandwidth Management; Wireless Network

  18. RESEARCH OF ENGINEERING TRAFFIC IN COMPUTER UZ NETWORK USING MPLS TE TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    V. M. Pakhomovа

    2014-12-01

    Full Text Available Purpose. In railway transport of Ukraine one requires the use of computer networks of different technologies: Ethernet, Token Bus, Token Ring, FDDI and others. In combined computer networks on the railway transport it is necessary to use packet switching technology in multiprotocol networks MPLS (MultiProtocol Label Switching more effectively. They are based on the use of tags. Packet network must transmit different types of traffic with a given quality of service. The purpose of the research is development a methodology for determining the sequence of destination flows for the considered fragment of computer network of UZ. Methodology. When optimizing traffic management in MPLS networks has the important role of technology traffic engineering (Traffic Engineering, TE. The main mechanism of TE in MPLS is the use of unidirectional tunnels (MPLS TE tunnel to specify the path of the specified traffic. The mathematical model of the problem of traffic engineering in computer network of UZ technology MPLS TE was made. Computer UZ network is represented with the directed graph, their vertices are routers of computer network, and each arc simulates communication between nodes. As an optimization criterion serves the minimum value of the maximum utilization of the TE-tunnel. Findings. The six options destination flows were determined; rational sequence of flows was found, at which the maximum utilization of TE-tunnels considered a simplified fragment of a computer UZ network does not exceed 0.5. Originality. The method of solving the problem of traffic engineering in Multiprotocol network UZ technology MPLS TE was proposed; for different classes its own way is laid, depending on the bandwidth and channel loading. Practical value. Ability to determine the values of the maximum coefficient of use of TE-tunnels in computer UZ networks based on developed software model «TraffEng». The input parameters of the model: number of routers, channel capacity, the

  19. MetaNetwork : A computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, Jingyuan; Swertz, Morris A.; Keurentjes, Joost J. B.; Jansen, Ritsert C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

  20. MetaNetwork: a computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, J.; Swertz, M.A.; Keurentjes, J.J.B.; Jansen, R.C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

  1. Creating and Using a Computer Networking and Systems Administration Laboratory Built under Relaxed Financial Constraints

    Science.gov (United States)

    Conlon, Michael P.; Mullins, Paul

    2011-01-01

    The Computer Science Department at Slippery Rock University created a laboratory for its Computer Networks and System Administration and Security courses under relaxed financial constraints. This paper describes the department's experience designing and using this laboratory, including lessons learned and descriptions of some student projects…

  2. Meeting report from the fourth meeting of computational modeling in biology network (COMBINE).

    NARCIS (Netherlands)

    Waltemath, D.; Bergmann, F.T.; Chaouiya, C.; Czauderna, T.; Gleeson, P.; Goble, C.A.; Golebiewski, M.; Hucka, M.; Juty, N.; Krebs, O.; Le Novere, N.; Mi, H.; Moraru, I.I.; Myers, C.J.; Nickerson, D.; Olivier, B.G.; Rodriguez, N.; Schreiber, F.; Smith, L.; Zhang, F.; Bonnet, E.

    2014-01-01

    The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in

  3. Cloud and fog computing in 5G mobile networks emerging advances and applications

    CERN Document Server

    Markakis, Evangelos; Mavromoustakis, Constandinos X; Pallis, Evangelos

    2017-01-01

    This book focuses on the challenges and solutions related to cloud and fog computing for 5G mobile networks, and presents novel approaches to the frameworks and schemes that carry out storage, communication, computation and control in the fog/cloud paradigm.

  4. Effects of maximum node degree on computer virus spreading in scale-free networks

    Science.gov (United States)

    Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

    2017-10-01

    The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

  5. Asymptotic Behavior of the Maximum Entropy Routing in Computer Networks

    Directory of Open Access Journals (Sweden)

    Milan Tuba

    2013-01-01

    Full Text Available Maximum entropy method has been successfully used for underdetermined systems. Network design problem, with routing and topology subproblems, is an underdetermined system and a good candidate for maximum entropy method application. Wireless ad-hoc networks with rapidly changing topology and link quality, where the speed of recalculation is of crucial importance, have been recently successfully investigated by maximum entropy method application. In this paper we prove a theorem that establishes asymptotic properties of the maximum entropy routing solution. This result, besides being theoretically interesting, can be used to direct initial approximation for iterative optimization algorithms and to speed up their convergence.

  6. An efficient algorithm for computing attractors of synchronous and asynchronous Boolean networks.

    Science.gov (United States)

    Zheng, Desheng; Yang, Guowu; Li, Xiaoyu; Wang, Zhicai; Liu, Feng; He, Lei

    2013-01-01

    Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly [Formula: see text] faster in computing attractors for empirical experimental systems. The software package is available at https://sites.google.com/site/desheng619/download.

  7. Picoradio: Communication/Computation Piconodes for Sensor Networks

    National Research Council Canada - National Science Library

    Rabaey, Jan

    2003-01-01

    This project addressed the 'system-on-a-chip' implementation of a PicoNode, which can provide all the communication, computation, and geolocation functions necessary for an adaptive distributed sensor...

  8. The Implications of Pervasive Computing on Network Design

    Science.gov (United States)

    Briscoe, R.

    Mark Weiser's late-1980s vision of an age of calm technology with pervasive computing disappearing into the fabric of the world [1] has been tempered by an industry-driven vision with more of a feel of conspicuous consumption. In the modified version, everyone carries around consumer electronics to provide natural, seamless interactions both with other people and with the information world, particularly for eCommerce, but still through a pervasive computing fabric.

  9. Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ruchi D. Chande

    2017-01-01

    Full Text Available Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

  10. Computing distance-based topological descriptors of complex chemical networks: New theoretical techniques

    Science.gov (United States)

    Hayat, Sakander

    2017-11-01

    Structure-based topological descriptors/indices of complex chemical networks enable prediction of physico-chemical properties and the bioactivities of these compounds through QSAR/QSPR methods. In this paper, we have developed a rigorous computational and theoretical technique to compute various distance-based topological indices of complex chemical networks. A fullerene is called the IPR (Isolated-Pentagon-Rule) fullerene, if every pentagon in it is surrounded by hexagons only. To ensure the applicability of our technique, we compute certain distance-based indices of an infinite family of IPR fullerenes. Our results show that the proposed technique is more diverse and bears less algorithmic and combinatorial complexity.

  11. Russian students face new Estonian-language classes / Joel Alas

    Index Scriptorium Estoniae

    Alas, Joel

    2007-01-01

    Vene koolide üleminekust eestikeelsele õppele, Tallinna Ülikoolis selleks õpetajatele korraldatud kursustest. Haridusministeeriumi kommentaar. President Toomas Hendrik Ilvese kõnest teadmiste päeval

  12. Condor-COPASI: high-throughput computing for biochemical networks

    Directory of Open Access Journals (Sweden)

    Kent Edward

    2012-07-01

    Full Text Available Abstract Background Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise. Results We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays. Conclusions Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at http://code.google.com/p/condor-copasi/, along with full instructions on deployment and usage.

  13. AN EVALUATION AND IMPLEMENTATION OF COLLABORATIVE AND SOCIAL NETWORKING TECHNOLOGIES FOR COMPUTER EDUCATION

    Directory of Open Access Journals (Sweden)

    Ronnie Cheung

    2011-06-01

    Full Text Available We have developed a collaborative and social networking environment that integrates the knowledge and skills in communication and computing studies with a multimedia development project. The outcomes of the students’ projects show that computer literacy can be enhanced through a cluster of communication, social, and digital skills. Experience in implementing a web-based social networking environment shows that the new media is an effective means of enriching knowledge by sharing in computer literacy projects. The completed assignments, projects, and self-reflection reports demonstrate that the students were able to achieve the learning outcomes of a computer literacy course in multimedia development. The students were able to assess the effectiveness of a variety of media through the development of media presentations in a web-based, social-networking environment. In the collaborative and social-networking environment, students were able to collaborate and communicate with their team members to solve problems, resolve conflicts, make decisions, and work as a team to complete tasks. Our experience has shown that social networking environments are effective for computer literacy education, and the development of the new media is emerging as the core knowledge for computer literacy education.

  14. Online Social Networks and Computer Skills of University Students

    Science.gov (United States)

    Barbas, Maria Potes; Valerio, Gabriel; Rodríguez-Martínez, María del Carmen; Herrera-Murillo, Dagoberto José; Belmonte-Jiménez, Ana María

    2014-01-01

    Currently a large number of college students belong to social networks and spend several hours a week on them. Some sectors of society, like parents and teachers, are concerned about the negative impact on their academic work and in their personal lives. However, because the potential positive impacts have not been explored enough, this research…

  15. Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

    DEFF Research Database (Denmark)

    Schmidt, R.; Waschina, S.; Boettger-Schmidt, D.

    2015-01-01

    by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass...

  16. Computational analyses of synergism in small molecular network motifs.

    Directory of Open Access Journals (Sweden)

    Yili Zhang

    2014-03-01

    Full Text Available Cellular functions and responses to stimuli are controlled by complex regulatory networks that comprise a large diversity of molecular components and their interactions. However, achieving an intuitive understanding of the dynamical properties and responses to stimuli of these networks is hampered by their large scale and complexity. To address this issue, analyses of regulatory networks often focus on reduced models that depict distinct, reoccurring connectivity patterns referred to as motifs. Previous modeling studies have begun to characterize the dynamics of small motifs, and to describe ways in which variations in parameters affect their responses to stimuli. The present study investigates how variations in pairs of parameters affect responses in a series of ten common network motifs, identifying concurrent variations that act synergistically (or antagonistically to alter the responses of the motifs to stimuli. Synergism (or antagonism was quantified using degrees of nonlinear blending and additive synergism. Simulations identified concurrent variations that maximized synergism, and examined the ways in which it was affected by stimulus protocols and the architecture of a motif. Only a subset of architectures exhibited synergism following paired changes in parameters. The approach was then applied to a model describing interlocked feedback loops governing the synthesis of the CREB1 and CREB2 transcription factors. The effects of motifs on synergism for this biologically realistic model were consistent with those for the abstract models of single motifs. These results have implications for the rational design of combination drug therapies with the potential for synergistic interactions.

  17. Computer-mediated-communication and social networking tools at work

    NARCIS (Netherlands)

    Ou, C.X.J.; Sia, C.L.; Hui, C.K.

    2013-01-01

    Purpose – Advances in information technology (IT) have resulted in the development of various computer‐mediated communication (CMC) and social networking tools. However, quantifying the benefits of utilizing these tools in the organizational context remains a challenge. In this study, the authors

  18. Emulation of the Active Immune Response in a Computer Network

    Science.gov (United States)

    2009-01-15

    there exist a number of methods connected to processes of optimization intended to solve several problems including immunotherapy and immuno ...researchers and security analysts to respond faster in order to keep up with these attacks. New approaches for network security analysis, reactive and

  19. Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

    Science.gov (United States)

    Krajíček, Jiří

    This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

  20. APINetworks: A general API for the treatment of complex networks in arbitrary computational environments

    Science.gov (United States)

    Niño, Alfonso; Muñoz-Caro, Camelia; Reyes, Sebastián

    2015-11-01

    The last decade witnessed a great development of the structural and dynamic study of complex systems described as a network of elements. Therefore, systems can be described as a set of, possibly, heterogeneous entities or agents (the network nodes) interacting in, possibly, different ways (defining the network edges). In this context, it is of practical interest to model and handle not only static and homogeneous networks but also dynamic, heterogeneous ones. Depending on the size and type of the problem, these networks may require different computational approaches involving sequential, parallel or distributed systems with or without the use of disk-based data structures. In this work, we develop an Application Programming Interface (APINetworks) for the modeling and treatment of general networks in arbitrary computational environments. To minimize dependency between components, we decouple the network structure from its function using different packages for grouping sets of related tasks. The structural package, the one in charge of building and handling the network structure, is the core element of the system. In this work, we focus in this API structural component. We apply an object-oriented approach that makes use of inheritance and polymorphism. In this way, we can model static and dynamic networks with heterogeneous elements in the nodes and heterogeneous interactions in the edges. In addition, this approach permits a unified treatment of different computational environments. Tests performed on a C++11 version of the structural package show that, on current standard computers, the system can handle, in main memory, directed and undirected linear networks formed by tens of millions of nodes and edges. Our results compare favorably to those of existing tools.

  1. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

    Science.gov (United States)

    Tamada, Yoshinori; Imoto, Seiya; Araki, Hiromitsu; Nagasaki, Masao; Print, Cristin; Charnock-Jones, D Stephen; Miyano, Satoru

    2011-01-01

    We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

  2. NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 47: The value of computer networks in aerospace

    Science.gov (United States)

    Bishop, Ann Peterson; Pinelli, Thomas E.

    1995-01-01

    This paper presents data on the value of computer networks that were obtained from a national survey of 2000 aerospace engineers that was conducted in 1993. Survey respondents reported the extent to which they used computer networks in their work and communication and offered their assessments of the value of various network types and applications. They also provided information about the positive impacts of networks on their work, which presents another perspective on value. Finally, aerospace engineers' recommendations on network implementation present suggestions for increasing the value of computer networks within aerospace organizations.

  3. A Study on Parallel Computation Tools on Networked PCs

    Directory of Open Access Journals (Sweden)

    Heru Suhartanto

    2010-10-01

    Full Text Available Many models for natural phenomena, engineering applications and industries need powerfull computing resources to solve their problems. High Performance Computing resources were introduced by many researchers. This comes in the form of Supercomputers and with operating systems and tools for development such as parallel compiler and its library. However, these resources are  expensive for the investation and maintenance, hence people need some alternatives. Many people then introduced parallel distributed computing by using available computing resource such as PCs. Each of these PCs is treated  s a processors, hence the cluster of the PC behaves as Multiprocessors Computer. Many tools are developed for such purposes. This paper studies the peformance of the currently popular tools such as Parallel Virta\\ual Machine (PVM, Message Passing Interface (MPI, Java Remote Method Invocation (RMI and Java Common Object Request Broker Architecture (CORBA. Some experiments were conducted on a cluster of PCs, the results show significant speed up. Each of those tools are identified suitable for a certain implementation and programming purposes.

  4. Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

    Directory of Open Access Journals (Sweden)

    Kaznessis Yiannis N

    2007-01-01

    Full Text Available Abstract Background Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA and Tet-On (rtTA. Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. Results In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. Conclusion The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory

  5. Personal Computer Local Area Network Security in an Academic Environment

    Science.gov (United States)

    1989-12-01

    AND JUSTIFICATION The San Francisco Examiner ran an article by John Dvorak on Sunday August 6th titled "Viruses Make Me Sick". The author speaks of the...humidity or foreign object destruction (e.g. a drink spilled into the keyboard ). Unfortunately, these areas can be tough to guard against. User training is...inserted into a floppy drive or a favorite soft drink is placed two inches from a keyboard . Instead, upon introduc- tion to the network labs

  6. PRiFi Networking for Tracking-Resistant Mobile Computing

    Science.gov (United States)

    2017-11-01

    of an organization to access the Internet anonymously while they are on- site, via privacy-preserving WiFi networking, or off-site, via privacy...client messages based on the history of all messages they have received so far. As a result, any equivocation attempt makes the communication...model, threat models, and goals of the protocol. Our system model comprises a set of n clients (or users) who want to access the Internet anonymously

  7. Computer Simulations of Bottlebrush Melts and Soft Networks

    Science.gov (United States)

    Cao, Zhen; Carrillo, Jan-Michael; Sheiko, Sergei; Dobrynin, Andrey

    We have studied dense bottlebrush systems in a melt and network state using a combination of the molecular dynamics simulations and analytical calculations. Our simulations show that the bottlebrush macromolecules in a melt behave as ideal chains with the effective Kuhn length bK. The bottlebrush induced bending rigidity is due to redistribution of the side chains upon backbone bending. Kuhn length of the bottlebrushes increases with increasing the side-chain degree of polymerization nsc as bK ~nsc0 . 46 . This model of bottlebrush macromolecules is extended to describe mechanical properties of bottlebrush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G0 ~nsc + 1 - 1 as long as the ratio of the Kuhn length to the size of the fully extended bottlebrush backbone between crosslinks, Rmax, is smaller than unity, bK /Rmax crosslinks. Nsf DMR-1409710 DMR-1436201.

  8. Data identification for improving gene network inference using computational algebra.

    Science.gov (United States)

    Dimitrova, Elena; Stigler, Brandilyn

    2014-11-01

    Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

  9. Design, implementation and security of a typical educational laboratory computer network

    Directory of Open Access Journals (Sweden)

    Martin Pokorný

    2013-01-01

    Full Text Available Computer network used for laboratory training and for different types of network and security experiments represents a special environment where hazardous activities take place, which may not affect any production system or network. It is common that students need to have administrator privileges in this case which makes the overall security and maintenance of such a network a difficult task. We present our solution which has proved its usability for more than three years. First of all, four user requirements on the laboratory network are defined (access to educational network devices, to laboratory services, to the Internet, and administrator privileges of the end hosts, and four essential security rules are stipulated (enforceable end host security, controlled network access, level of network access according to the user privilege level, and rules for hazardous experiments, which protect the rest of the laboratory infrastructure as well as the outer university network and the Internet. The main part of the paper is dedicated to a design and implementation of these usability and security rules. We present a physical diagram of a typical laboratory network based on multiple circuits connecting end hosts to different networks, and a layout of rack devices. After that, a topological diagram of the network is described which is based on different VLANs and port-based access control using the IEEE 802.1x/EAP-TLS/RADIUS authentication to achieve defined level of network access. In the second part of the paper, the latest innovation of our network is presented that covers a transition to the system virtualization at the end host devices – inspiration came from a similar solution deployed at the Department of Telecommunications at Brno University of Technology. This improvement enables a greater flexibility in the end hosts maintenance and a simultaneous network access to the educational devices as well as to the Internet. In the end, a vision of a

  10. Innovations and advances in computing, informatics, systems sciences, networking and engineering

    CERN Document Server

    Elleithy, Khaled

    2015-01-01

    Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering  This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers from the conference proceedings of the Eighth and some selected papers of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2012 & CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.  ·       Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; ·       Includes chapters in the most a...

  11. Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network.

    Science.gov (United States)

    Goto, Hayato

    2016-02-22

    The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.

  12. Simulation and Noise Analysis of Multimedia Transmission in Optical CDMA Computer Networks

    Directory of Open Access Journals (Sweden)

    Nasaruddin Nasaruddin

    2013-09-01

    Full Text Available This paper simulates and analyzes noise of multimedia transmission in a flexible optical code division multiple access (OCDMA computer network with different quality of service (QoS requirements. To achieve multimedia transmission in OCDMA, we have proposed strict variable-weight optical orthogonal codes (VW-OOCs, which can guarantee the smallest correlation value of one by the optimal design. In developing multimedia transmission for computer network, a simulation tool is essential in analyzing the effectiveness of various transmissions of services. In this paper, implementation models are proposed to analyze the multimedia transmission in the representative of OCDMA computer networks by using MATLAB simulink tools. Simulation results of the models are discussed including spectrums outputs of transmitted signals, superimposed signals, received signals, and eye diagrams with and without noise. Using the proposed models, multimedia OCDMA computer network using the strict VW-OOC is practically evaluated. Furthermore, system performance is also evaluated by considering avalanche photodiode (APD noise and thermal noise. The results show that the system performance depends on code weight, received laser power, APD noise, and thermal noise which should be considered as important parameters to design and implement multimedia transmission in OCDMA computer networks.

  13. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  14. Simulation and Noise Analysis of Multimedia Transmission in Optical CDMA Computer Networks

    Directory of Open Access Journals (Sweden)

    Nasaruddin

    2009-11-01

    Full Text Available This paper simulates and analyzes noise of multimedia transmission in a flexible optical code division multiple access (OCDMA computer network with different quality of service (QoS requirements. To achieve multimedia transmission in OCDMA, we have proposed strict variable-weight optical orthogonal codes (VW-OOCs, which can guarantee the smallest correlation value of one by the optimal design. In developing multimedia transmission for computer network, a simulation tool is essential in analyzing the effectiveness of various transmissions of services. In this paper, implementation models are proposed to analyze the multimedia transmission in the representative of OCDMA computer networks by using MATLAB simulink tools. Simulation results of the models are discussed including spectrums outputs of transmitted signals, superimposed signals, received signals, and eye diagrams with and without noise. Using the proposed models, multimedia OCDMA computer network using the strict VW-OOC is practically evaluated. Furthermore, system performance is also evaluated by considering avalanche photodiode (APD noise and thermal noise. The results show that the system performance depends on code weight, received laser power, APD noise, and thermal noise which should be considered as important parameters to design and implement multimedia transmission in OCDMA computer networks.

  15. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Lars Buesing

    2011-11-01

    Full Text Available The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  16. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Science.gov (United States)

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-11-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  17. Computation of loss allocation in electric power networks using loss ...

    African Journals Online (AJOL)

    This paper presents the computation of loss allocation that can be applied to sellers and buyers participating in electric power trade in a deregulated power market. The approach is based on the Jacobian and Hessian matrices of the power flow equations. The losses to be allocated are derived from load flow of a specified ...

  18. Computational Fluids Domain Reduction to a Simplified Fluid Network

    Science.gov (United States)

    2012-04-19

    best suited algorithm for a specific task. There are no theoretical guidelines to assist on proper features selection for a specific situation. The...UNCLASSIFIED Kumar, Parvesh, and Siri Krishan Wasan. "Comparative Analysis of k-mean Based Algorithms." IJCSNS International Journal of Computer

  19. An Analysis of the Computer and Network Attack Taxonomy

    Science.gov (United States)

    2001-03-01

    method that the Air Force can use to help it classify Internet security attacks and incidents. This researcher concluded that the computer and...organizations responsible for the collection and distribution of Internet Security information, do explicitly collect some, not all, information useful as input into the taxonomy.

  20. Contention Bounds for Combinations of Computation Graphs and Network Topologies

    Science.gov (United States)

    2014-08-08

    Google, Nokia , NVIDIA, Oracle, MathWorks and Samsung. Also funded by U.S. DOE Office of Science, Office of Advanced Scientific Computing Research...program sponsored by MARCO and DARPA, and ASPIRE Lab industrial sponsors and affiliates Intel, Google, Nokia , NVIDIA, Oracle, MathWorks and Samsung

  1. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  2. Can computational efficiency alone drive the evolution of modularity in neural networks?

    Science.gov (United States)

    Tosh, Colin R

    2016-08-30

    Some biologists have abandoned the idea that computational efficiency in processing multipart tasks or input sets alone drives the evolution of modularity in biological networks. A recent study confirmed that small modular (neural) networks are relatively computationally-inefficient but large modular networks are slightly more efficient than non-modular ones. The present study determines whether these efficiency advantages with network size can drive the evolution of modularity in networks whose connective architecture can evolve. The answer is no, but the reason why is interesting. All simulations (run in a wide variety of parameter states) involving gradualistic connective evolution end in non-modular local attractors. Thus while a high performance modular attractor exists, such regions cannot be reached by gradualistic evolution. Non-gradualistic evolutionary simulations in which multi-modularity is obtained through duplication of existing architecture appear viable. Fundamentally, this study indicates that computational efficiency alone does not drive the evolution of modularity, even in large biological networks, but it may still be a viable mechanism when networks evolve by non-gradualistic means.

  3. State of the Art of Network Security Perspectives in Cloud Computing

    Science.gov (United States)

    Oh, Tae Hwan; Lim, Shinyoung; Choi, Young B.; Park, Kwang-Roh; Lee, Heejo; Choi, Hyunsang

    Cloud computing is now regarded as one of social phenomenon that satisfy customers' needs. It is possible that the customers' needs and the primary principle of economy - gain maximum benefits from minimum investment - reflects realization of cloud computing. We are living in the connected society with flood of information and without connected computers to the Internet, our activities and work of daily living will be impossible. Cloud computing is able to provide customers with custom-tailored features of application software and user's environment based on the customer's needs by adopting on-demand outsourcing of computing resources through the Internet. It also provides cloud computing users with high-end computing power and expensive application software package, and accordingly the users will access their data and the application software where they are located at the remote system. As the cloud computing system is connected to the Internet, network security issues of cloud computing are considered as mandatory prior to real world service. In this paper, survey and issues on the network security in cloud computing are discussed from the perspective of real world service environments.

  4. The super-Turing computational power of plastic recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2014-12-01

    We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

  5. Computation and evaluation of scheduled waiting time for railway networks

    DEFF Research Database (Denmark)

    Landex, Alex

    2010-01-01

    Timetables are affected by scheduled waiting time (SWT) that prolongs the travel times for trains and thereby passengers. SWT occurs when a train hinders another train to run with the wanted speed. The SWT affects both the trains and the passengers in the trains. The passengers may be further...... affected due to longer transfer times to other trains. SWT can be estimated analytically for a given timetable or by simulation of timetables and/or plans of operation. The simulation of SWT has the benefit that it is possible to examine the entire network. This makes it possible to improve the future...

  6. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  7. A new framework to integrate wireless sensor networks with cloud computing

    Science.gov (United States)

    Shah, Sajjad Hussain; Khan, Fazle Kabeer; Ali, Wajid; Khan, Jamshed

    Wireless sensors networks have several applications of their own. These applications can further enhanced by integrating a local wireless sensor network to internet, which can be used in real time applications where the results of sensors are stored on the cloud. We propose an architecture that integrates a wireless sensor network to the internet using cloud technology. The resultant system is proved to be reliable, available and extensible. In this paper a new framework is proposed for WSN integration with Cloud computing model, existing WSN will be connected to the proposed framework. Three deployment layer are used to serve user request (IaaS, PaaS, SaaS) either from the library which is made from data collected from data centric DC by WSN periodically. The integration controller unit of the proposed framework integrates the sensor network and cloud computing technology which offers reliability, availability and extensibility.

  8. Quantum perceptron over a field and neural network architecture selection in a quantum computer.

    Science.gov (United States)

    da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

    2016-04-01

    In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Analysis of Various Computer System Monitoring and LCD Projector through the Network TCP/IP

    Directory of Open Access Journals (Sweden)

    Santoso Budijono

    2015-09-01

    Full Text Available Many electronic devices have a network connection facility. Projectors today have network facilities to bolster its customer satisfaction in everyday use. By using a device that can be controlled, the expected availability and reliability of the presentation system (computer and projector can be maintained to keep itscondition ready to use for presentation. Nevertheless, there is still a projector device that has no network facilities so that the necessary additional equipment with expensive price. Besides, control equipment in large quantities has problems in timing and the number of technicians in performing controls. This study began with study of literature, from searching for the projectors that has LAN and software to control and finding a number of computer control softwares where the focus is easy to use and affordable. Result of this research is creating asystem which contains suggestions of procurement of computer hardware, hardware and software projectors each of which can be controlled centrally from a distance.

  10. Renal Arterial Network Structure by Computed Tomography, and Nephron-Arterial Interactions

    DEFF Research Database (Denmark)

    Postnov, Dmitry; von Holstein-Rathlou, Niels-Henrik; Sosnovtseva, Olga

    2015-01-01

    Our goal is to predict interactions that develop among nephrons and between nephrons and the arterial network that supports them. We have developed a computationally simple but physiologically-based mathematical model of the kidney vascular tree to study renal autoregulation in ensembles of inter......Our goal is to predict interactions that develop among nephrons and between nephrons and the arterial network that supports them. We have developed a computationally simple but physiologically-based mathematical model of the kidney vascular tree to study renal autoregulation in ensembles...... of interacting nephrons not directly available for experimentation. The study combines computed tomography (CT) of a renal vascular cast at 2 micrometer resolution with simulation. The CT scan showed a bifurcating branching structure with as many as 7 bifurcations between arcuate arteries and the renal surface....... The network model predicts dynamical aspects of vascular pressure drops and nephron self-sustained cooperative dynamics....

  11. The propagation approach for computing biochemical reaction networks.

    Science.gov (United States)

    Henzinger, Thomas A; Mateescu, Maria

    2013-01-01

    We introduce propagation models (PMs), a formalism able to express several kinds of equations that describe the behavior of biochemical reaction networks. Furthermore, we introduce the propagation abstract data type (PADT), which separates concerns regarding different numerical algorithms for the transient analysis of biochemical reaction networks from concerns regarding their implementation, thus allowing for portable and efficient solutions. The state of a propagation abstract data type is given by a vector that assigns mass values to a set of nodes, and its next operator propagates mass values through this set of nodes. We propose an approximate implementation of the next operator, based on threshold abstraction, which propagates only "significant" mass values and thus achieves a compromise between efficiency and accuracy. Finally, we give three use cases for propagation models: the chemical master equation (CME), the reaction rate equation (RRE), and a hybrid method that combines these two equations. These three applications use propagation models in order to propagate probabilities and/or expected values and variances of the model's variables.

  12. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  13. Conceptual Considerations for Reducing the Computational Complexity in Software Defined Radio using Cooperative Wireless Networks

    DEFF Research Database (Denmark)

    Kristensen, Jesper Michael; Fitzek, Frank H. P.; Koch, Peter

    2005-01-01

    This paper motivates the application of Software defined radio as the enabling technology in the implementation of future wireless terminals for 4G. It outlines the advantages and disadvantages of SDR in terms of Flexibility and reconfigurability versus computational complexity. To mitigate...... the expected increase in complexity leading to a decrease in energy efficiency, cooperative wireless networks are introduced. Cooperative wireless networks enables the concept of resource sharing. Resource sharing is interpreted as collaborative signal processing. This interpretation leads to the concept...

  14. Aspects of healthcare computer networks security in the education of students of medicine and healthcare management

    OpenAIRE

    Mircheva, Iskra

    2001-01-01

    Preserving privacy and confidentiality of medical data has always been a fundamental question in medicine and healthcare. Information technologies state even greater requirements to medical data security, especially when, as expected, medical data should be transferred between the different healthcare providers using specialized or public computer networks. The presented paper outlines some basic requirements to healthcare networks security, with which the future medical specialists should ...

  15. Sign: large-scale gene network estimation environment for high performance computing.

    Science.gov (United States)

    Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

    2011-01-01

    Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

  16. Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation.

    Science.gov (United States)

    Hambli, Ridha; Katerchi, Houda; Benhamou, Claude-Laurent

    2011-02-01

    The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate the process of bone remodelling. As whole bone simulation, including the 3D reconstruction of trabecular level bone, is time consuming, finite element calculation is only performed at the macroscopic level, whilst trained neural networks are employed as numerical substitutes for the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at the macroscopic scale depending on the morphological and mechanical adaptation at the mesoscopic scale computed by the trained neural network. The digital image-based modelling technique using μ-CT and voxel finite element analysis is used to capture volume elements representative of 2 mm³ at the mesoscale level of the femoral head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied stress. The output data are the updated bone properties and some trabecular bone factors. The current approach is the first model, to our knowledge, that incorporates both finite element analysis and neural network computation to rapidly simulate multilevel bone adaptation.

  17. Computation and Communication Evaluation of an Authentication Mechanism for Time-Triggered Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Goncalo Martins

    2016-07-01

    Full Text Available In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications. Many networked control systems employ Time-Triggered (TT architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC to TT networked control systems. The contributions of the paper include (1 the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2 an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems.

  18. Computation and Communication Evaluation of an Authentication Mechanism for Time-Triggered Networked Control Systems.

    Science.gov (United States)

    Martins, Goncalo; Moondra, Arul; Dubey, Abhishek; Bhattacharjee, Anirban; Koutsoukos, Xenofon D

    2016-07-25

    In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Triggered (TT) architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC) to TT networked control systems. The contributions of the paper include (1) the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2) an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems.

  19. Ptychographic X-ray computed tomography of extended colloidal networks in food emulsions

    DEFF Research Database (Denmark)

    Schou Nielsen, Mikkel; Bøgelund Munk, Merete; Diaz, Ana

    2016-01-01

    of suitable non-destructive 3D imaging techniques with submicron resolution. We present results of quantitative ptychographic X-ray computed tomography applied to a palm kernel oil based oil-in-water emulsion. The measurements were carried out at ambient pressure and temperature. The 3D structure...... of the extended colloidal network of fat globules was obtained with a resolution of around 300 nm. Through image analysis of the network structure, the fat globule size distribution was computed and compared to previous findings. In further support, the reconstructed electron density values were within 4...

  20. Computational modeling of the quorum-sensing network in bacteria

    Science.gov (United States)

    Fenley, Andrew; Banik, Suman; Kulkarni, Rahul

    2007-03-01

    Certain species of bacteria are able produce and sense the concentration of small molecules called autodinducers in order to coordinate gene regulation in response to population density, a process known as ``quorum-sensing''. The resulting regulation of gene expression involves both transcriptional and post-transcriptional regulators. In particular, the species of bacteria in the Vibrio genus use small RNAs to regulate the master protein controlling the quorum-sensing response (luminescence, biofilm formation, virulence...). We model the network of interactions using a modular approach which provides a quantitative understanding of how signal transduction occurs. The parameters of the input-module are fit to current experimental results allowing for testable predictions to be made for future experiments. The results of our analysis offer a revised perspective on quorum-sensing based regulation.

  1. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  2. Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

    Science.gov (United States)

    Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

    2017-01-01

    The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

  3. On the relevance of efficient, integrated computer and network monitoring in HEP distributed online environment

    CERN Document Server

    Carvalho, D F; Delgado, V; Albert, J N; Bellas, N; Javello, J; Miere, Y; Ruffinoni, D; Smith, G

    1996-01-01

    Large Scientific Equipments are controlled by Computer System whose complexity is growing driven, on the one hand by the volume and variety of the information, its distributed nature, thhe sophistication of its trearment and, on the over hand by the fast evolution of the computer and network market. Some people call them generically Large-Scale Distributed Data Intensive Information Systems or Distributed Computer Control Systems (DCCS) for those systems dealing more with real time control. Taking advantage of (or forced by) the distributed architecture, the tasks are more and more often implemented as Client-Server applications. In this frame- work the monitoring of the computer nodes, the communications network and the applications becomes of primary importance for ensuring the safe running and guaranteed performance of the system. With the future generation of HEP experiments, such as those at the LHC in view, it is to integrate the various functions of DCCS monitoring into one general purpose Multi-layer ...

  4. Gammon - A load balancing strategy for local computer systems with multiaccess networks

    Science.gov (United States)

    Baumgartner, Katherine M.; Wah, Benjamin W.

    1989-01-01

    Consideration is given to an efficient load-balancing strategy, Gammon (global allocation from maximum to minimum in constant time), for distributed computing systems connected by multiaccess local area networks. The broadcast capability of these networks is utilized to implement an identification procedure at the applications level for the maximally and the minimally loaded processors. The search technique has an average overhead which is independent of the number of participating stations. An implementation of Gammon on a network of Sun workstations is described. Its performance is found to be better than that of other known methods.

  5. A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

    Science.gov (United States)

    Abdul Wahab, Ainuddin Wahid; Han, Qi; Bin Abdul Rahman, Zulkanain

    2014-01-01

    Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC. PMID:25097880

  6. Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach

    Directory of Open Access Journals (Sweden)

    Qing Sun

    2015-01-01

    Full Text Available Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.

  7. A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing.

    Science.gov (United States)

    Khan, Suleman; Shiraz, Muhammad; Wahab, Ainuddin Wahid Abdul; Gani, Abdullah; Han, Qi; Rahman, Zulkanain Bin Abdul

    2014-01-01

    Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.

  8. On Using Home Networks and Cloud Computing for a Future Internet of Things

    Science.gov (United States)

    Niedermayer, Heiko; Holz, Ralph; Pahl, Marc-Oliver; Carle, Georg

    In this position paper we state four requirements for a Future Internet and sketch our initial concept. The requirements: (1) more comfort, (2) integration of home networks, (3) resources like service clouds in the network, and (4) access anywhere on any machine. Future Internet needs future quality and future comfort. There need to be new possiblities for everyone. Our focus is on higher layers and related to the many overlay proposals. We consider them to run on top of a basic Future Internet core. A new user experience means to include all user devices. Home networks and services should be a fundamental part of the Future Internet. Home networks extend access and allow interaction with the environment. Cloud Computing can provide reliable resources beyond local boundaries. For access anywhere, we also need secure storage for data and profiles in the network, in particular for access with non-personal devices (Internet terminal, ticket machine, ...).

  9. A Newly Developed Method for Computing Reliability Measures in a Water Supply Network

    Directory of Open Access Journals (Sweden)

    Jacek Malinowski

    2016-01-01

    Full Text Available A reliability model of a water supply network has beens examined. Its main features are: a topology that can be decomposed by the so-called state factorization into a (relativelysmall number of derivative networks, each having a series-parallel structure (1, binary-state components (either operative or failed with given flow capacities (2, a multi-state character of the whole network and its sub-networks - a network state is defined as the maximal flow between a source (sources and a sink (sinks (3, all capacities (component, network, and sub-network have integer values (4. As the network operates, its state changes due to component failures, repairs, and replacements. A newly developed method of computing the inter-state transition intensities has been presented. It is based on the so-called state factorization and series-parallel aggregation. The analysis of these intensities shows that the failure-repair process of the considered system is an asymptotically homogenous Markov process. It is also demonstrated how certain reliability parameters useful for the network maintenance planning can be determined on the basis of the asymptotic intensities. For better understanding of the presented method, an illustrative example is given. (original abstract

  10. Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings

    Directory of Open Access Journals (Sweden)

    Guessasma Sofiane

    2017-01-01

    Full Text Available In this work, neural network computation is attempted to relate alumina and titania phase changes of a coating microstructure with respect to energetic parameters of atmospheric plasma straying (APS process. Experimental results were analysed using standard fitting routines and neural computation to quantify the effect of arc current, hydrogen ratio and total plasma flow rate. For a large parameter domain, phase changes were 10% for alumina and 8% for titania with a significant control of titania phase.

  11. Computation and analysis of temporal betweenness in a knowledge mobilization network.

    Science.gov (United States)

    Afrasiabi Rad, Amir; Flocchini, Paola; Gaudet, Joanne

    2017-01-01

    Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goals, is one of the many examples of dynamic social networks. Despite the wide use and extensive study of dynamic networks, their temporal component is often neglected in social network analysis, and statistical measures are usually performed on static network representations. As a result, measures of importance (like betweenness centrality) typically do not reveal the temporal role of the entities involved. Our goal is to contribute to fill this limitation by proposing a form of temporal betweenness measure (foremost betweenness). Our method is analytical as well as experimental: we design an algorithm to compute foremost betweenness, and we apply it to a case study to analyze a knowledge mobilization network. We propose a form of temporal betweenness measure (foremost betweenness) to analyze a knowledge mobilization network and we introduce, for the first time, an algorithm to compute exact foremost betweenness. We then show that this measure, which explicitly takes time into account, allows us to detect centrality roles that were completely hidden in the classical statistical analysis. In particular, we uncover nodes whose static centrality was negligible, but whose temporal role might instead be important to accelerate mobilization flow in the network. We also observe the reverse behavior by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. In this paper, we focus on a form of temporal betweenness designed to detect accelerators in dynamic networks. By revealing potentially important temporal roles, this study is a first step toward a better understanding of the impact of time in social networks and opens the road to further investigation.

  12. Representing spatial information in a computational model for network management

    Science.gov (United States)

    Blaisdell, James H.; Brownfield, Thomas F.

    1994-01-01

    While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.

  13. Steady state analysis of Boolean molecular network models via model reduction and computational algebra.

    Science.gov (United States)

    Veliz-Cuba, Alan; Aguilar, Boris; Hinkelmann, Franziska; Laubenbacher, Reinhard

    2014-06-26

    A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for

  14. Computing the viscosity of supercooled liquids: Markov Network model.

    Directory of Open Access Journals (Sweden)

    Ju Li

    Full Text Available The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of deeply supercooled liquids based on numerical sampling of an atomistic energy landscape, which sheds some light on this transition. Shear stress relaxation is calculated from a master-equation description in which the system follows a transition-state pathway trajectory of hopping among local energy minima separated by activation barriers, which is in turn sampled by a metadynamics-based algorithm. Quantitative connection is established between the temperature variation of the calculated viscosity and the underlying potential energy and inherent stress landscape, showing a different landscape topography or "terrain" is needed for low-temperature viscosity (of order 10(7 Pa·s from that associated with high-temperature viscosity (10(-5 Pa·s. Within this range our results clearly indicate the crossover from an essentially Arrhenius scaling behavior at high temperatures to a low-temperature behavior that is clearly super-Arrhenius (fragile for a Kob-Andersen model of binary liquid. Experimentally the manifestation of this crossover in atomic dynamics continues to raise questions concerning its fundamental origin. In this context this work explicitly demonstrates that a temperature-dependent "terrain" characterizing different parts of the same potential energy surface is sufficient to explain the signature behavior of vitrification, at the same time the notion of a temperature-dependent effective activation barrier is quantified.

  15. Abstracting massive data for lightweight intrusion detection in computer networks

    KAUST Repository

    Wang, Wei

    2016-10-15

    Anomaly intrusion detection in big data environments calls for lightweight models that are able to achieve real-time performance during detection. Abstracting audit data provides a solution to improve the efficiency of data processing in intrusion detection. Data abstraction refers to abstract or extract the most relevant information from the massive dataset. In this work, we propose three strategies of data abstraction, namely, exemplar extraction, attribute selection and attribute abstraction. We first propose an effective method called exemplar extraction to extract representative subsets from the original massive data prior to building the detection models. Two clustering algorithms, Affinity Propagation (AP) and traditional . k-means, are employed to find the exemplars from the audit data. . k-Nearest Neighbor (k-NN), Principal Component Analysis (PCA) and one-class Support Vector Machine (SVM) are used for the detection. We then employ another two strategies, attribute selection and attribute extraction, to abstract audit data for anomaly intrusion detection. Two http streams collected from a real computing environment as well as the KDD\\'99 benchmark data set are used to validate these three strategies of data abstraction. The comprehensive experimental results show that while all the three strategies improve the detection efficiency, the AP-based exemplar extraction achieves the best performance of data abstraction.

  16. Medical applications for high-performance computers in SKIF-GRID network.

    Science.gov (United States)

    Zhuchkov, Alexey; Tverdokhlebov, Nikolay

    2009-01-01

    The paper presents a set of software services for massive mammography image processing by using high-performance parallel computers of SKIF-family which are linked into a service-oriented grid-network. An experience of a prototype system implementation in two medical institutions is also described.

  17. Adolescents, Health Education, and Computers: The Body Awareness Resource Network (BARN).

    Science.gov (United States)

    Bosworth, Kris; And Others

    1983-01-01

    The Body Awareness Resource Network (BARN) is a computer-based system designed as a confidential, nonjudgmental source of health information for adolescents. Topics include alcohol and other drugs, diet and activity, family communication, human sexuality, smoking, and stress management; programs are available for high school and middle school…

  18. POSTER: Privacy-Preserving Profile Similarity Computation in Online Social Networks

    NARCIS (Netherlands)

    Jeckmans, Arjan; Tang, Qiang; Hartel, Pieter H.

    2011-01-01

    Currently, none of the existing online social networks (OSNs) enables its users to make new friends without revealing their private information. This leaves the users in a vulnerable position when searching for new friends. We propose a solution which enables a user to compute her profile similarity

  19. 2nd FTRA International Conference on Ubiquitous Computing Application and Wireless Sensor Network

    CERN Document Server

    Pan, Yi; Chao, Han-Chieh; Yi, Gangman

    2015-01-01

    IT changes everyday’s life, especially in education and medicine. The goal of ITME 2014 is to further explore the theoretical and practical issues of Ubiquitous Computing Application and Wireless Sensor Network. It also aims to foster new ideas and collaboration between researchers and practitioners. The organizing committee is soliciting unpublished papers for the main conference and its special tracks.

  20. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  1. The Use of Computer Networks in Data Gathering and Data Analysis.

    Science.gov (United States)

    Yost, Michael; Bremner, Fred

    This document describes the review, analysis, and decision-making process that Trinity University, Texas, went through to develop the three-part computer network that they use to gather and analyze EEG (electroencephalography) and EKG (electrocardiogram) data. The data are gathered in the laboratory on a PDP-1124, an analog minicomputer. Once…

  2. Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

    Science.gov (United States)

    Chen, Siyue; Leung, Henry; Dondo, Maxwell

    2014-05-01

    As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the Expectation-Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

  3. Portable Operating Systems for Network Computers: Distributed Operating Systems Support for Group Communications.

    Science.gov (United States)

    1985-10-31

    Modula-2 MICROS/ MICRONET " (5) William S. Holmes, May 1983, "Version and Source Code Support Environment" (6) Michael J. Palumbo. May 1983, "Stand...Dist. Comp. Sys., Denver, CO, May 1985, 386- 393. 7. A. van Tilborg and L. D. Wittie, "Operating Systems for the Micronet Network Computer", IEEE Micro

  4. Improving airway segmentation in computed tomography using leak detection with convolutional networks

    NARCIS (Netherlands)

    Charbonnier, J.P.; Rikxoort, E.M. van; Setio, A.A.A.; Schaefer-Prokop, C.M.; Ginneken, B. van; Ciompi, F.

    2017-01-01

    We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task.

  5. Data systems and computer science space data systems: Onboard networking and testbeds

    Science.gov (United States)

    Dalton, Dan

    1991-01-01

    The technical objectives are to develop high-performance, space-qualifiable, onboard computing, storage, and networking technologies. The topics are presented in viewgraph form and include the following: justification; technology challenges; program description; and state-of-the-art assessment.

  6. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  7. Computing Assortative Mixing by Degree with the s-Metric in Networks Using Linear Programming

    Directory of Open Access Journals (Sweden)

    Lourens J. Waldorp

    2015-01-01

    Full Text Available Calculation of assortative mixing by degree in networks indicates whether nodes with similar degree are connected to each other. In networks with scale-free distribution high values of assortative mixing by degree can be an indication of a hub-like core in networks. Degree correlation has generally been used to measure assortative mixing of a network. But it has been shown that degree correlation cannot always distinguish properly between different networks with nodes that have the same degrees. The so-called s-metric has been shown to be a better choice to calculate assortative mixing. The s-metric is normalized with respect to the class of networks without self-loops, multiple edges, and multiple components, while degree correlation is always normalized with respect to unrestricted networks, where self-loops, multiple edges, and multiple components are allowed. The challenge in computing the normalized s-metric is in obtaining the minimum and maximum value within a specific class of networks. We show that this can be solved by using linear programming. We use Lagrangian relaxation and the subgradient algorithm to obtain a solution to the s-metric problem. Several examples are given to illustrate the principles and some simulations indicate that the solutions are generally accurate.

  8. Computing the Local Field Potential (LFP from Integrate-and-Fire Network Models.

    Directory of Open Access Journals (Sweden)

    Alberto Mazzoni

    2015-12-01

    Full Text Available Leaky integrate-and-fire (LIF network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP. Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

  9. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

    Science.gov (United States)

    Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T

    2015-12-01

    Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

  10. Optimal control strategy for a novel computer virus propagation model on scale-free networks

    Science.gov (United States)

    Zhang, Chunming; Huang, Haitao

    2016-06-01

    This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

  11. A Study of Quality of Service Communication for High-Speed Packet-Switching Computer Sub-Networks

    Science.gov (United States)

    Cui, Zhenqian

    1999-01-01

    With the development of high-speed networking technology, computer networks, including local-area networks (LANs), wide-area networks (WANs) and the Internet, are extending their traditional roles of carrying computer data. They are being used for Internet telephony, multimedia applications such as conferencing and video on demand, distributed simulations, and other real-time applications. LANs are even used for distributed real-time process control and computing as a cost-effective approach. Differing from traditional data transfer, these new classes of high-speed network applications (video, audio, real-time process control, and others) are delay sensitive. The usefulness of data depends not only on the correctness of received data, but also the time that data are received. In other words, these new classes of applications require networks to provide guaranteed services or quality of service (QoS). Quality of service can be defined by a set of parameters and reflects a user's expectation about the underlying network's behavior. Traditionally, distinct services are provided by different kinds of networks. Voice services are provided by telephone networks, video services are provided by cable networks, and data transfer services are provided by computer networks. A single network providing different services is called an integrated-services network.

  12. Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks

    NARCIS (Netherlands)

    Greff, Klaus; van Damme, Rudolf M.J.; Koutnik, Jan; Broersma, Haitze J.; Mikhal, Julia Olegivna; Lawrence, Celestine Preetham; van der Wiel, Wilfred Gerard; Schmidhuber, Jürgen

    2016-01-01

    Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experiments were performed on disordered nano-particle networks that have no predefined design. The material has been treated as a blackbox,

  13. Mobile cloud networking: mobile network, compute, and storage as one service on-demand

    NARCIS (Netherlands)

    Jamakovic, Almerima; Bohnert, Thomas Michael; Karagiannis, Georgios; Galis, A.; Gavras, A.

    2013-01-01

    The Future Communication Architecture for Mobile Cloud Services: Mobile Cloud Networking (MCN)1 is a EU FP7 Large scale Integrating Project (IP) funded by the European Commission. MCN project was launched in November 2012 for the period of 36 month. In total top-tier 19 partners from industry and

  14. The spread of computer viruses over a reduced scale-free network

    Science.gov (United States)

    Yang, Lu-Xing; Yang, Xiaofan

    2014-02-01

    Due to the high dimensionality of an epidemic model of computer viruses over a general scale-free network, it is difficult to make a close study of its dynamics. In particular, it is extremely difficult, if not impossible, to prove the global stability of its viral equilibrium, if any. To overcome this difficulty, we suggest to simplify a general scale-free network by partitioning all of its nodes into two classes: higher-degree nodes and lower-degree nodes, and then equating the degrees of all higher-degree nodes and all lower-degree nodes, respectively, yielding a reduced scale-free network. We then propose an epidemic model of computer viruses over a reduced scale-free network. A theoretical analysis reveals that the proposed model is bound to have a globally stable viral equilibrium, implying that any attempt to eradicate network viruses would prove unavailing. As a result, the next best thing we can do is to restrain virus prevalence. Based on an analysis of the impact of different model parameters on virus prevalence, some practicable measures are recommended to contain virus spreading. The work in this paper adequately justifies the idea of reduced scale-free networks.

  15. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  16. Computing and Network Systems Administration, Operations Research, and System Dynamics Modeling: A Proposed Research Framework

    Directory of Open Access Journals (Sweden)

    Michael W. Totaro

    2016-12-01

    Full Text Available Information and computing infrastructures (ICT involve levels of complexity that are highly dynamic in nature. This is due in no small measure to the proliferation of technologies, such as: cloud computing and distributed systems architectures, data mining and multidimensional analysis, and large scale enterprise systems, to name a few. Effective computing and network systems administration is integral to the stability and scalability of these complex software, hardware and communication systems. Systems administration involves the design, analysis, and continuous improvement of the performance or operation of information and computing systems. Additionally, social and administrative responsibilities have become nearly as integral for the systems administrator as are the technical demands that have been imposed for decades. The areas of operations research (OR and system dynamics (SD modeling offer system administrators a rich array of analytical and optimization tools that have been developed from diverse disciplines, which include: industrial, scientific, engineering, economic and financial, to name a few. This paper proposes a research framework by which OR and SD modeling techniques may prove useful to computing and network systems administration, which include: linear programming, network analysis, integer programming, nonlinear optimization, Markov processes, queueing modeling, simulation, decision analysis, heuristic techniques, and system dynamics modeling.

  17. New trends in networking, computing, e-learning, systems sciences, and engineering

    CERN Document Server

    Sobh, Tarek

    2015-01-01

    This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers form the conference proceedings of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.  • Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; • Includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences, and Engineering; • Accessible to a wide range of readership, including professors, researchers, practitioners and...

  18. Computational intelligence in wireless sensor networks recent advances and future challenges

    CERN Document Server

    Falcon, Rafael; Koeppen, Mario

    2017-01-01

    This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from the spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors. USP: Presents recent advances and fu...

  19. MEDUSA - An overset grid flow solver for network-based parallel computer systems

    Science.gov (United States)

    Smith, Merritt H.; Pallis, Jani M.

    1993-01-01

    Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.

  20. Year 1 Progress Report Computational Materials and Chemical Sciences Network Administration

    Energy Technology Data Exchange (ETDEWEB)

    Rehr, John J.

    2012-08-02

    This document reports progress on the project “Computational Materials and Chemical Sciences Network Administration,” which is supported by DOE BES Grant DE-FG02-02ER45990 MOD 08. As stated in the original proposal, the primary goal of this project is to carry out the scientific administrative responsibilities for the Computational Materials and Chemical Sciences Network (CMCSN) of the U.S. Department of Energy, Office of Basic Energy Sciences. These responsibilities include organizing meetings, publishing and maintaining CMCSN’s website, publishing a periodic newsletter, writing original material for both the website and the newsletter, maintaining CMCSN documentation, editing scientific documents, as needed, serving as liaison for the entire Network, facilitating information exchange across the network, communicating CMCSN’s success stories to the larger community and numerous other tasks outside the purview of the scientists in the CMCSN. Given the dramatic increase in computational power, advances in computational materials science can have an enormous impact in science and technology. For many of the questions that can be addressed by computation there is a choice of theoretical techniques available, yet often there is no accepted understanding of the relative strengths and effectiveness of the competing approaches. The CMCSN fosters progress in this understanding by providing modest additional funding to research groups which engage in collaborative activities to develop, compare, and test novel computational techniques. Thus, the CMCSN provides the “glue” money which enables different groups to work together, building on their existing programs and expertise while avoiding unnecessary duplication of effort. This includes travel funding, partial postdoc salaries, and funding for periodic scientific meetings. The activities supported by this grant are briefly summarized below.

  1. Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study.

    Science.gov (United States)

    Szkoła, Jarosław; Pancerz, Krzysztof; Warchoł, Jan

    2011-01-01

    The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks (ENs) are a classical representative of RNNs. To improve learning ability of ENs, we may modify and combine them with another kind of RNNs, namely, with the Jordan networks. The modified Elman-Jordan networks (EJNs) manifest a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients from the control group and with two kinds of laryngopathies.

  2. Formal Methods for Information Protection Technology. Task 1: Formal Grammar-Based Approach and Tool for Simulation Attacks against Computer Network. Part 1

    National Research Council Canada - National Science Library

    Karsayev, O

    2004-01-01

    .... Integrity, confidentiality and availability of the network resources must be assured. To detect and suppress different types of computer unauthorized intrusions, modern network security systems (NSS...

  3. Designing optimal transportation networks: a knowledge-based computer-aided multicriteria approach

    Energy Technology Data Exchange (ETDEWEB)

    Tung, S.I.

    1986-01-01

    The dissertation investigates the applicability of using knowledge-based expert systems (KBES) approach to solve the single-mode (automobile), fixed-demand, discrete, multicriteria, equilibrium transportation-network-design problem. Previous works on this problem has found that mathematical programming method perform well on small networks with only one objective. Needed is a solution technique that can be used on large networks having multiple, conflicting criteria with different relative importance weights. The KBES approach developed in this dissertation represents a new way to solve network design problems. The development of an expert system involves three major tasks: knowledge acquisition, knowledge representation, and testing. For knowledge acquisition, a computer aided network design/evaluation model (UFOS) was developed to explore the design space. This study is limited to the problem of designing an optimal transportation network by adding and deleting capacity increments to/from any link in the network. Three weighted criteria were adopted for use in evaluating each design alternative: cost, average V/C ratio, and average travel time.

  4. Benchmarking selected computational gene network growing tools in context of virus-host interactions.

    Science.gov (United States)

    Taye, Biruhalem; Vaz, Candida; Tanavde, Vivek; Kuznetsov, Vladimir A; Eisenhaber, Frank; Sugrue, Richard J; Maurer-Stroh, Sebastian

    2017-07-19

    Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.

  5. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Combining Topological Hardware and Topological Software: Color-Code Quantum Computing with Topological Superconductor Networks

    Directory of Open Access Journals (Sweden)

    Daniel Litinski

    2017-09-01

    Full Text Available We present a scalable architecture for fault-tolerant topological quantum computation using networks of voltage-controlled Majorana Cooper pair boxes and topological color codes for error correction. Color codes have a set of transversal gates which coincides with the set of topologically protected gates in Majorana-based systems, namely, the Clifford gates. In this way, we establish color codes as providing a natural setting in which advantages offered by topological hardware can be combined with those arising from topological error-correcting software for full-fledged fault-tolerant quantum computing. We provide a complete description of our architecture, including the underlying physical ingredients. We start by showing that in topological superconductor networks, hexagonal cells can be employed to serve as physical qubits for universal quantum computation, and we present protocols for realizing topologically protected Clifford gates. These hexagonal-cell qubits allow for a direct implementation of open-boundary color codes with ancilla-free syndrome read-out and logical T gates via magic-state distillation. For concreteness, we describe how the necessary operations can be implemented using networks of Majorana Cooper pair boxes, and we give a feasibility estimate for error correction in this architecture. Our approach is motivated by nanowire-based networks of topological superconductors, but it could also be realized in alternative settings such as quantum-Hall–superconductor hybrids.

  7. Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network

    Directory of Open Access Journals (Sweden)

    Zhen Hong

    2017-11-01

    Full Text Available Device-to-device (D2D communication is becoming an increasingly important technology in future networks with the climbing demand for local services. For instance, resource sharing in the D2D network features ubiquitous availability, flexibility, low latency and low cost. However, these features also bring along challenges when building a satisfactory resource sharing system in the D2D network. Specifically, user mobility is one of the top concerns for designing a cooperative D2D computational resource sharing system since mutual communication may not be stably available due to user mobility. A previous endeavour has demonstrated and proven how connectivity can be incorporated into cooperative task scheduling among users in the D2D network to effectively lower average task execution time. There are doubts about whether this type of task scheduling scheme, though effective, presents fairness among users. In other words, it can be unfair for users who contribute many computational resources while receiving little when in need. In this paper, we propose a novel blockchain-based credit system that can be incorporated into the connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network. Users’ computational task cooperation will be recorded on the public blockchain ledger in the system as transactions, and each user’s credit balance can be easily accessible from the ledger. A supernode at the base station is responsible for scheduling cooperative computational tasks based on user mobility and user credit balance. We investigated the performance of the credit system, and simulation results showed that with a minor sacrifice of average task execution time, the level of fairness can obtain a major enhancement.

  8. Computing single step operators of logic programming in radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  9. Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks

    Directory of Open Access Journals (Sweden)

    Hui-Ping Chen

    2016-11-01

    Full Text Available The Space-time prism (STP is a key concept in time geography for analyzing human activity-travel behavior under various Space-time constraints. Most existing time-geographic studies use a straightforward algorithm to construct STPs in road networks by using two one-to-all shortest path searches. However, this straightforward algorithm can introduce considerable computational overhead, given the fact that accessible links in a STP are generally a small portion of the whole network. To address this issue, an efficient geo-computational algorithm, called NTP-A*, is proposed. The proposed NTP-A* algorithm employs the A* and branch-and-bound techniques to discard inaccessible links during two shortest path searches, and thereby improves the STP construction performance. Comprehensive computational experiments are carried out to demonstrate the computational advantage of the proposed algorithm. Several implementation techniques, including the label-correcting technique and the hybrid link-node labeling technique, are discussed and analyzed. Experimental results show that the proposed NTP-A* algorithm can significantly improve STP construction performance in large-scale road networks by a factor of 100, compared with existing algorithms.

  10. Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

    Science.gov (United States)

    Wei, Yawei; Venayagamoorthy, Ganesh Kumar

    2017-09-01

    To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim.

    Science.gov (United States)

    Parasuram, Harilal; Nair, Bipin; D'Angelo, Egidio; Hines, Michael; Naldi, Giovanni; Diwakar, Shyam

    2016-01-01

    Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals.

  12. Application of cloud computing in power routing for clusters of microgrids using oblivious network routing algorithm

    DEFF Research Database (Denmark)

    Amini, M. Hadi; Broojeni, Kianoosh G.; Dragicevic, Tomislav

    2017-01-01

    of microgrid while preventing congestion as well as minimizing the power loss. Then, we present a two-layer simulation platform which considers both communication layer and physical layer of the microgrids' cluster. In order to improve the security of communication network, we perform the computations...... regarding the oblivious power routing via a cloud-based network. The proposed framework can be used for further studies that deal with the real-time simulation of the clusters of microgrids. In order to validate the effectiveness of the proposed framework, we implement our proposed oblivious routing scheme...

  13. Teaching strategies applied to teaching computer networks in Engineering in Telecommunications and Electronics

    Directory of Open Access Journals (Sweden)

    Elio Manuel Castañeda-González

    2016-07-01

    Full Text Available Because of the large impact that today computer networks, their study in related fields such as Telecommunications Engineering and Electronics is presented to the student with great appeal. However, by digging in content, lacking a strong practical component, you can make this interest decreases considerably. This paper proposes the use of teaching strategies and analogies, media and interactive applications that enhance the teaching of discipline networks and encourage their study. It is part of an analysis of how the teaching of the discipline process is performed and then a description of each of these strategies is done with their respective contribution to student learning.

  14. Computer network operations and ‘rule-with-law’ in Australia

    Directory of Open Access Journals (Sweden)

    Adam Molnar

    2017-03-01

    Full Text Available Computer Network Operations (CNOs refers to government intrusion and/or interference with networked information communication infrastructures for the purposes of law enforcement and security intelligence. The following article explores how CNOs are lawfully authorised in Australia, and considers the extent to which the current use of CNOs are subject to ‘counter-law’ developments. More specifically, the article finds that the scope and application of CNOs in Australia are subject to weak legislative controls, that while such operations might be ‘lawful’, they undermine rule of law and disturb core democratic freedoms.

  15. A knowledge-based system with learning for computer communication network design

    Science.gov (United States)

    Pierre, Samuel; Hoang, Hai Hoc; Tropper-Hausen, Evelyne

    1990-01-01

    Computer communication network design is well-known as complex and hard. For that reason, the most effective methods used to solve it are heuristic. Weaknesses of these techniques are listed and a new approach based on artificial intelligence for solving this problem is presented. This approach is particularly recommended for large packet switched communication networks, in the sense that it permits a high degree of reliability and offers a very flexible environment dealing with many relevant design parameters such as link cost, link capacity, and message delay.

  16. PROBABILISTIC BEHAVIORAL MODEL FOR COMPUTER NETWORK PROTECTION BASED ON ATTACK TREES

    Directory of Open Access Journals (Sweden)

    N. A. Dorodnikov

    2016-09-01

    Full Text Available The paper deals with the results of probabilistic model development for behavioral computer network. We present a method for the system state simulation immediately after the attack. To describe the threats we have selected an appropriate set of mathematical models for processes. The authors have proposed a modification of the attack trees theory including probabilistic attack trees, describing the ways to achieve objectives by illegal intruders and calculating the probability of the various types of threats. The proposed method enables to assess the levels of risks and vulnerability of the studied networks with the aid of the system behavior simulation.

  17. A computational framework for the automated construction of glycosylation reaction networks.

    Directory of Open Access Journals (Sweden)

    Gang Liu

    Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

  18. The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

    Directory of Open Access Journals (Sweden)

    Ikhthison Mekongga

    2014-02-01

    Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

  19. Interactive granular computations in networks and systems engineering a practical perspective

    CERN Document Server

    Jankowski, Andrzej

    2017-01-01

    The book outlines selected projects conducted under the supervision of the author. Moreover, it discusses significant relations between Interactive Granular Computing (IGrC) and numerous dynamically developing scientific domains worldwide, along with features characteristic of the author’s approach to IGrC. The results presented are a continuation and elaboration of various aspects of Wisdom Technology, initiated and developed in cooperation with Professor Andrzej Skowron. Based on the empirical findings from these projects, the author explores the following areas: (a) understanding the causes of the theory and practice gap problem (TPGP) in complex systems engineering (CSE);(b) generalizing computing models of complex adaptive systems (CAS) (in particular, natural computing models) by constructing an interactive granular computing (IGrC) model of networks of interrelated interacting complex granules (c-granules), belonging to a single agent and/or to a group of agents; (c) developing methodologies based ...

  20. Performance analysis and acceleration of explicit integration for large kinetic networks using batched GPU computations

    Energy Technology Data Exchange (ETDEWEB)

    Shyles, Daniel [University of Tennessee (UT); Dongarra, Jack J. [University of Tennessee, Knoxville (UTK); Guidry, Mike W. [ORNL; Tomov, Stanimire Z. [ORNL; Billings, Jay Jay [ORNL; Brock, Benjamin A. [ORNL; Haidar Ahmad, Azzam A. [ORNL

    2016-09-01

    Abstract—We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms that solve efficiently N coupled ordinary differential equations (subject to initial conditions) on modern GPUs. We take representative test cases (Type Ia supernova explosions) and demonstrate two or more orders of magnitude increase in efficiency for solving such systems (of realistic thermonuclear networks coupled to fluid dynamics). This implies that important coupled, multiphysics problems in various scientific and technical disciplines that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible. As examples of such applications we present the computational techniques developed for our ongoing deployment of these new methods on modern GPU accelerators. We show that similarly to many other scientific applications, ranging from national security to medical advances, the computation can be split into many independent computational tasks, each of relatively small-size. As the size of each individual task does not provide sufficient parallelism for the underlying hardware, especially for accelerators, these tasks must be computed concurrently as a single routine, that we call batched routine, in order to saturate the hardware with enough work.

  1. CRYSNET manual. Informal report. [Hardware and software of crystallographic computing network

    Energy Technology Data Exchange (ETDEWEB)

    None,

    1976-07-01

    This manual describes the hardware and software which together make up the crystallographic computing network (CRYSNET). The manual is intended as a users' guide and also provides general information for persons without any experience with the system. CRYSNET is a network of intelligent remote graphics terminals that are used to communicate with the CDC Cyber 70/76 computing system at the Brookhaven National Laboratory (BNL) Central Scientific Computing Facility. Terminals are in active use by four research groups in the field of crystallography. A protein data bank has been established at BNL to store in machine-readable form atomic coordinates and other crystallographic data for macromolecules. The bank currently includes data for more than 20 proteins. This structural information can be accessed at BNL directly by the CRYSNET graphics terminals. More than two years of experience has been accumulated with CRYSNET. During this period, it has been demonstrated that the terminals, which provide access to a large, fast third-generation computer, plus stand-alone interactive graphics capability, are useful for computations in crystallography, and in a variety of other applications as well. The terminal hardware, the actual operations of the terminals, and the operations of the BNL Central Facility are described in some detail, and documentation of the terminal and central-site software is given. (RWR)

  2. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  3. Building Model for the University of Mosul Computer Network Using OPNET Simulator

    Directory of Open Access Journals (Sweden)

    Modhar Modhar A. Hammoudi

    2013-04-01

    Full Text Available This paper aims at establishing a model in OPNET (Optimized Network Engineering Tool simulator for the University of Mosul computer network. The proposed network model was made up of two routers (Cisco 2600, core switch (Cisco6509, two servers, ip 32 cloud and 37 VLANs. These VLANs were connected to the core switch using fiber optic cables (1000BaseX. Three applications were added to test the network model. These applications were FTP (File Transfer Protocol, HTTP (Hyper Text Transfer Protocol and VoIP (Voice over Internet Protocol. The results showed that the proposed model had a positive efficiency on designing and managing the targeted network and can be used to view the data flow in it. Also, the simulation results showed that the maximum number of VoIP service users could be raised upto 5000 users when working under IP Telephony. This means that the ability to utilize VoIP service in this network can be maintained and is better when subjected to IP telephony scheme.

  4. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  5. Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow.

    Science.gov (United States)

    Gruenert, Gerd; Gizynski, Konrad; Escuela, Gabi; Ibrahim, Bashar; Gorecki, Jerzy; Dittrich, Peter

    2015-11-01

    In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.

  6. Low-cost autonomous perceptron neural network inspired by quantum computation

    Science.gov (United States)

    Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

    2017-11-01

    Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

  7. Exact computation and large angular momentum asymptotics of 3nj symbols: Semiclassical disentangling of spin networks.

    Science.gov (United States)

    Anderson, Roger W; Aquilanti, Vincenzo; da Silva Ferreira, Cristiane

    2008-10-28

    Spin networks, namely, the 3nj symbols of quantum angular momentum theory and their generalizations to groups other than SU(2) and to quantum groups, permeate many areas of pure and applied science. The issues of their computation and characterization for large values of their entries are a challenge for diverse fields, such as spectroscopy and quantum chemistry, molecular and condensed matter physics, quantum computing, and the geometry of space time. Here we record progress both in their efficient calculation and in the study of the large j asymptotics. For the 9j symbol, a prototypical entangled network, we present and extensively check numerically formulas that illustrate the passage to the semiclassical limit, manifesting both the occurrence of disentangling and the discrete-continuum transition.

  8. A Social Network Approach to Provisioning and Management of Cloud Computing Services for Enterprises

    DEFF Research Database (Denmark)

    Kuada, Eric

    been extremely difficult in the past; but with the advent of cloud computing, this problem should be less difficult to solve. Opportunistic Cloud Services (OCS) is about enterprises leveraging cloud computing technologies to contribute spare IT resources to a platform so that others on the platform can...... utilize them as and when needed. The OCS network is modelled as a social network of enterprises collaborating strategically in contributing and utilizing cloud services without entering into any business agreements. Such a platform faces several problems. One of such problems is the free riding problem...... challenges that were discovered during this study, the obtained results demonstrate both the technical feasibility and the existence of enabling conditions for the implementation of opportunistic cloud services for enterprises....

  9. Efficient quantum computation in a network with probabilistic gates and logical encoding

    DEFF Research Database (Denmark)

    Borregaard, J.; Sørensen, A. S.; Cirac, J. I.

    2017-01-01

    An approach to efficient quantum computation with probabilistic gates is proposed and analyzed in both a local and nonlocal setting. It combines heralded gates previously studied for atom or atomlike qubits with logical encoding from linear optical quantum computation in order to perform high......-fidelity quantum gates across a quantum network. The error-detecting properties of the heralded operations ensure high fidelity while the encoding makes it possible to correct for failed attempts such that deterministic and high-quality gates can be achieved. Importantly, this is robust to photon loss, which...... is typically the main obstacle to photonic-based quantum information processing. Overall this approach opens a path toward quantum networks with atomic nodes and photonic links....

  10. Modeling and computing of stock index forecasting based on neural network and Markov chain.

    Science.gov (United States)

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

  11. Basic Principles of Industrial Electric Power Network Computer Aided Design and Engineering

    Directory of Open Access Journals (Sweden)

    M. I. Fursanov

    2012-01-01

    Full Text Available A conceptual model for a computer aided design and engineering system has been developed in the paper. The paper presents basic automation process principles including a graphical representation   network and calculation results, convenient user interface, automatic mode calculation, selection of transformer rated power and cross-section area of wires. The developed algorithm and program make it possible to save time and improve quality of project implementation.

  12. Stability and Hopf Bifurcation in a Delayed SEIRS Worm Model in Computer Network

    Directory of Open Access Journals (Sweden)

    Zizhen Zhang

    2013-01-01

    Full Text Available A delayed SEIRS epidemic model with vertical transmission in computer network is considered. Sufficient conditions for local stability of the positive equilibrium and existence of local Hopf bifurcation are obtained by analyzing distribution of the roots of the associated characteristic equation. Furthermore, the direction of the local Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by using the normal form theory and center manifold theorem. Finally, a numerical example is presented to verify the theoretical analysis.

  13. Computational Principle and Performance Evaluation of Coherent Ising Machine Based on Degenerate Optical Parametric Oscillator Network

    Directory of Open Access Journals (Sweden)

    Yoshitaka Haribara

    2016-04-01

    Full Text Available We present the operational principle of a coherent Ising machine (CIM based on a degenerate optical parametric oscillator (DOPO network. A quantum theory of CIM is formulated, and the computational ability of CIM is evaluated by numerical simulation based on c-number stochastic differential equations. We also discuss the advanced CIM with quantum measurement-feedback control and various problems which can be solved by CIM.

  14. PROBABILISTIC BEHAVIORAL MODEL FOR COMPUTER NETWORK PROTECTION BASED ON ATTACK TREES

    OpenAIRE

    N. A. Dorodnikov; S. A. Arustamov

    2016-01-01

    The paper deals with the results of probabilistic model development for behavioral computer network. We present a method for the system state simulation immediately after the attack. To describe the threats we have selected an appropriate set of mathematical models for processes. The authors have proposed a modification of the attack trees theory including probabilistic attack trees, describing the ways to achieve objectives by illegal intruders and calculating the probability of the various ...

  15. A FPGA-based Network Interface Card with GPUDirect enabling realtime GPU computing in HEP experiments

    OpenAIRE

    Lonardo, Alessandro; Ameli, Fabrizio; Ammendola, Roberto; Biagioni, Andrea; Cotta Ramusino, Angelo; Fiorini, Massimiliano; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Martinelli, Michele; Neri, Ilaria; Paolucci, Pier Stanislao; Pastorelli, Elena; Pontisso, Luca; Rossetti, Davide

    2015-01-01

    The capability of processing high bandwidth data streams in real-time is a computational requirement common to many High Energy Physics experiments. Keeping the latency of the data transport tasks under control is essential in order to meet this requirement. We present NaNet, a FPGA-based PCIe Network Interface Card design featuring Remote Direct Memory Access towards CPU and GPU memories plus a transport protocol offload module characterized by cycle-accurate upper-bound handling. The combin...

  16. A Computational Estimation of Cyclic Material Properties Using Artificial Neural Networks

    OpenAIRE

    Tomasella, A.; Dsoki, C. el; H. Hanselka; Kaufmann, H.

    2011-01-01

    The structural durability design of components requires the knowledge of cyclic material properties. These parameters are strongly dependent on environmental conditions and manufacturing processes, and require many experimental tests to be correctly determined. Considering time and costs, it is not possible to include in the tests all the variables that influence the material behaviour. For this reason, the computational method of the Artificial Neural Network (ANN) can be implemented to supp...

  17. Utilizing neural networks in magnetic media modeling and field computation: A review

    OpenAIRE

    Amr A. Adly; Abd-El-Hafiz, Salwa K.

    2013-01-01

    Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in ma...

  18. A computational approach to extinction events in chemical reaction networks with discrete state spaces.

    Science.gov (United States)

    Johnston, Matthew D

    2017-12-01

    Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-01-01

    Full Text Available Computer aided detection (CAD systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO. Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

  20. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  1. Computing global structural balance in large-scale signed social networks.

    Science.gov (United States)

    Facchetti, Giuseppe; Iacono, Giovanni; Altafini, Claudio

    2011-12-27

    Structural balance theory affirms that signed social networks (i.e., graphs whose signed edges represent friendly/hostile interactions among individuals) tend to be organized so as to avoid conflictual situations, corresponding to cycles of negative parity. Using an algorithm for ground-state calculation in large-scale Ising spin glasses, in this paper we compute the global level of balance of very large online social networks and verify that currently available networks are indeed extremely balanced. This property is explainable in terms of the high degree of skewness of the sign distributions on the nodes of the graph. In particular, individuals linked by a large majority of negative edges create mostly "apparent disorder," rather than true "frustration."

  2. Quantification of soil pore network complexity with X-ray computed tomography and gas transport measurements

    DEFF Research Database (Denmark)

    Katuwal, Sheela; Arthur, Emmanuel; Tuller, M.

    2015-01-01

    different soils subjected to 22 mo of field regeneration were quantified with X-ray computed tomography (CT) and compared with functional pore characteristics estimated from measurements of air permeability and gas diffusivity. Furthermore, predictive models for air permeability and gas diffusivity were......Flow and transport of gases through soils are largely controlled by pore structural attributes. The quantification of pore network characteristics is therefore essential for accurate prediction of air permeability and gas diffusivity. In this study, the pore network characteristics of seven...... developed based on CT-derived structural parameters and compared with previously proposed predictive models. Strong correlations between functional and pore geometry parameters were observed. The consideration of CT-derived air-filled porosity, pore network tortuosity and connectivity, and minimum...

  3. Error recovery to enable error-free message transfer between nodes of a computer network

    Energy Technology Data Exchange (ETDEWEB)

    Blumrich, Matthias A.; Coteus, Paul W.; Chen, Dong; Gara, Alan; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd; Steinmacher-Burow, Burkhard; Vranas, Pavlos M.

    2016-01-26

    An error-recovery method to enable error-free message transfer between nodes of a computer network. A first node of the network sends a packet to a second node of the network over a link between the nodes, and the first node keeps a copy of the packet on a sending end of the link until the first node receives acknowledgment from the second node that the packet was received without error. The second node tests the packet to determine if the packet is error free. If the packet is not error free, the second node sets a flag to mark the packet as corrupt. The second node returns acknowledgement to the first node specifying whether the packet was received with or without error. When the packet is received with error, the link is returned to a known state and the packet is sent again to the second node.

  4. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Science.gov (United States)

    Li, Wei; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods. PMID:28070212

  5. Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.

    Science.gov (United States)

    Tian, Tianhai

    2016-01-01

    The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.

  6. Identification of control targets in Boolean molecular network models via computational algebra.

    Science.gov (United States)

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

    2016-09-23

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

  7. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  8. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

    Science.gov (United States)

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

  9. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    Science.gov (United States)

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

  10. Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

    Directory of Open Access Journals (Sweden)

    Min Chen

    2016-06-01

    Full Text Available Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D caching, Small cell Base Station (SBS caching and Macrocell Base Station (MBS caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching. In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user’s quality of experience (QoE and the heterogeneity of mobile terminals in terms of caching and computing capabilities.

  11. Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks.

    Science.gov (United States)

    Chen, Min; Hao, Yixue; Qiu, Meikang; Song, Jeungeun; Wu, Di; Humar, Iztok

    2016-06-25

    Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user's quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities.

  12. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits.

    Directory of Open Access Journals (Sweden)

    Danielle S Bassett

    2010-04-01

    Full Text Available Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.

  13. A New Generation of Networks and Computing Models for High Energy Physics in the LHC Era

    Science.gov (United States)

    Newman, H.

    2011-12-01

    Wide area networks of increasing end-to-end capacity and capability are vital for every phase of high energy physicists' work. Our bandwidth usage, and the typical capacity of the major national backbones and intercontinental links used by our field have progressed by a factor of several hundred times over the past decade. With the opening of the LHC era in 2009-10 and the prospects for discoveries in the upcoming LHC run, the outlook is for a continuation or an acceleration of these trends using next generation networks over the next few years. Responding to the need to rapidly distribute and access datasets of tens to hundreds of terabytes drawn from multi-petabyte data stores, high energy physicists working with network engineers and computer scientists are learning to use long range networks effectively on an increasing scale, and aggregate flows reaching the 100 Gbps range have been observed. The progress of the LHC, and the unprecedented ability of the experiments to produce results rapidly using worldwide distributed data processing and analysis has sparked major, emerging changes in the LHC Computing Models, which are moving from the classic hierarchical model designed a decade ago to more agile peer-to-peer-like models that make more effective use of the resources at Tier2 and Tier3 sites located throughout the world. A new requirements working group has gauged the needs of Tier2 centers, and charged the LHCOPN group that runs the network interconnecting the LHC Tierls with designing a new architecture interconnecting the Tier2s. As seen from the perspective of ICFA's Standing Committee on Inter-regional Connectivity (SCIC), the Digital Divide that separates physicists in several regions of the developing world from those in the developed world remains acute, although many countries have made major advances through the rapid installation of modern network infrastructures. A case in point is Africa, where a new round of undersea cables promises to transform

  14. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits.

    Science.gov (United States)

    Bassett, Danielle S; Greenfield, Daniel L; Meyer-Lindenberg, Andreas; Weinberger, Daniel R; Moore, Simon W; Bullmore, Edward T

    2010-04-22

    Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.

  15. The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking

    Science.gov (United States)

    Ma, Zhanshan (Sam); Krings, Axel W.; Hung, Chih-Cheng

    Communication is a fundamental function of life, and it exists in almost all living things: from single-cell bacteria to human beings. Communication, together with competition and cooperation,arethree fundamental processes in nature. Computer scientists are familiar with the study of competition or 'struggle for life' through Darwin's evolutionary theory, or even evolutionary computing. They may be equally familiar with the study of cooperation or altruism through the Prisoner's Dilemma (PD) game. However, they are likely to be less familiar with the theory of animal communication. The objective of this article is three-fold: (i) To suggest that the study of animal communication, especially the honesty (reliability) of animal communication, in which some significant advances in behavioral biology have been achieved in the last three decades, should be on the verge to spawn important cross-disciplinary research similar to that generated by the study of cooperation with the PD game. One of the far-reaching advances in the field is marked by the publication of "The Handicap Principle: a Missing Piece of Darwin's Puzzle" by Zahavi (1997). The 'Handicap' principle [34][35], which states that communication signals must be costly in some proper way to be reliable (honest), is best elucidated with evolutionary games, e.g., Sir Philip Sidney (SPS) game [23]. Accordingly, we suggest that the Handicap principle may serve as a fundamental paradigm for trust research in computer science. (ii) To suggest to computer scientists that their expertise in modeling computer networks may help behavioral biologists in their study of the reliability of animal communication networks. This is largely due to the historical reason that, until the last decade, animal communication was studied with the dyadic paradigm (sender-receiver) rather than with the network paradigm. (iii) To pose several open questions, the answers to which may bear some refreshing insights to trust research in

  16. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  17. Functional network reorganization during learning in a brain-computer interface paradigm.

    Science.gov (United States)

    Jarosiewicz, Beata; Chase, Steven M; Fraser, George W; Velliste, Meel; Kass, Robert E; Schwartz, Andrew B

    2008-12-09

    Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.

  18. Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology

    Directory of Open Access Journals (Sweden)

    K. C. Okafor

    2017-01-01

    Full Text Available With the Internet of Everything (IoE paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications.

  19. The prediction in computer color matching of dentistry based on GA+BP neural network.

    Science.gov (United States)

    Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

    2015-01-01

    Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

  20. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... reduction in computational effort when performing time domain simulation of mooring lines. The hybrid method uses a classical numerical model to generate simulation data, which are then subsequently used to train the ANN. After successful training the ANN is able to take over the simulation at a speed two...

  1. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  2. A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Nurul Afsar Shaon

    2017-05-01

    Full Text Available A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities. This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN. Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection. However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against “out of band” and “in band” wormhole attacks. The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed algorithm is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR based detection models. The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.

  3. A Family of Algorithms for Computing Consensus about Node State from Network Data

    Science.gov (United States)

    Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C.

    2013-01-01

    Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which

  4. A family of algorithms for computing consensus about node state from network data.

    Directory of Open Access Journals (Sweden)

    Eleanor R Brush

    Full Text Available Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes-from ranking websites to determining critical species in ecosystems-yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth, or by measuring net flow into a target node (depth. Using data from communication, social, and biological networks we find that that how an algorithm measures consensus-through breadth or depth- impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data and are robust to errors. However, in cases where nodes "form opinions" about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which

  5. A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

    Science.gov (United States)

    Pawlicki, Ted

    1988-03-01

    Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions

  6. NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 35: The use of computer networks in aerospace engineering

    Science.gov (United States)

    Bishop, Ann P.; Pinelli, Thomas E.

    1995-01-01

    This research used survey research to explore and describe the use of computer networks by aerospace engineers. The study population included 2000 randomly selected U.S. aerospace engineers and scientists who subscribed to Aerospace Engineering. A total of 950 usable questionnaires were received by the cutoff date of July 1994. Study results contribute to existing knowledge about both computer network use and the nature of engineering work and communication. We found that 74 percent of mail survey respondents personally used computer networks. Electronic mail, file transfer, and remote login were the most widely used applications. Networks were used less often than face-to-face interactions in performing work tasks, but about equally with reading and telephone conversations, and more often than mail or fax. Network use was associated with a range of technical, organizational, and personal factors: lack of compatibility across systems, cost, inadequate access and training, and unwillingness to embrace new technologies and modes of work appear to discourage network use. The greatest positive impacts from networking appear to be increases in the amount of accurate and timely information available, better exchange of ideas across organizational boundaries, and enhanced work flexibility, efficiency, and quality. Involvement with classified or proprietary data and type of organizational structure did not distinguish network users from nonusers. The findings can be used by people involved in the design and implementation of networks in engineering communities to inform the development of more effective networking systems, services, and policies.

  7. Sustaining Employability: A Process for Introducing Cloud Computing, Big Data, Social Networks, Mobile Programming and Cybersecurity into Academic Curricula

    National Research Council Canada - National Science Library

    Razvan Bologa; Ana-Ramona Lupu; Catalin Boja; Tiberiu Marian Georgescu

    2017-01-01

    ... curricula of business students: cloud computing, big data, mobile programming, and social networks and cybersecurity (CAMSS). The results are useful for those trying to implement similar curricular reforms, or to companies that need to manage talent pipelines.

  8. Network-based Parallel Retrieval Onboard Computing Environment for Sensor Systems Deployed on NASA Unmanned Aircraft Systems Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Remote Sensing Solutions proposes to develop the Network-based Parallel Retrieval Onboard Computing Environment for Sensor Systems (nPROCESS) for deployment on...

  9. A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network

    Science.gov (United States)

    Vodenicarevic, Damir; Locatelli, Nicolas; Abreu Araujo, Flavio; Grollier, Julie; Querlioz, Damien

    2017-03-01

    With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices’ non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing.

  10. Computing network-based features from intracranial EEG time series data: Application to seizure focus localization.

    Science.gov (United States)

    Hao, Stephanie; Subramanian, Sandya; Jordan, Austin; Santaniello, Sabato; Yaffe, Robert; Jouny, Christophe C; Bergey, Gregory K; Anderson, William S; Sarma, Sridevi V

    2014-01-01

    The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures. We define each node in the network as an electrode and weight each edge connecting a pair of nodes by the gamma band cross power of the corresponding iEEG signals. The eigenvector centrality (EVC) of each node is tracked over two seizures per patient and the electrodes are ranked according to the corresponding EVC value. We hypothesize that electrodes covering the EZ have a signature EVC rank evolution during seizure that differs from electrodes outside the EZ. We tested this hypothesis on multi-channel iEEG recordings from 2 DRE patients who had successful surgery (i.e., seizures were under control with or without medications) and 1 patient who had unsuccessful surgery. In the successful cases, we assumed that the resected region contained the EZ and found that the EVC rank evolution of the electrodes within the resected region had a distinct "arc" signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.

  11. Computational connectionism within neurons: A model of cytoskeletal automata subserving neural networks

    Science.gov (United States)

    Rasmussen, Steen; Karampurwala, Hasnain; Vaidyanath, Rajesh; Jensen, Klaus S.; Hameroff, Stuart

    1990-06-01

    “Neural network” models of brain function assume neurons and their synaptic connections to be the fundamental units of information processing, somewhat like switches within computers. However, neurons and synapses are extremely complex and resemble entire computers rather than switches. The interiors of the neurons (and other eucaryotic cells) are now known to contain highly ordered parallel networks of filamentous protein polymers collectively termed the cytoskeleton. Originally assumed to provide merely structural “bone-like” support, cytoskeletal structures such as microtubules are now recognized to organize cell interiors dynamically. The cytoskeleton is the internal communication network for the eucaryotic cell, both by means of simple transport and by means of coordinating extremely complicated events like cell division, growth and differentiation. The cytoskeleton may therefore be viewed as the cell's “nervous system”. Consequently the neuronal cytoskeleton may be involved in molecular level information processing which subserves higher, collective neuronal functions ultimately relating to cognition. Numerous models of information processing within the cytoskeleton (in particular, microtubules) have been proposed. We have utilized cellular automata as a means to model and demonstrate the potential for information processing in cytoskeletal microtubules. In this paper, we extend previous work and simulate associative learning in a cytoskeletal network as well as assembly and disassembly of microtubules. We also discuss possible relevance and implications of cytoskeletal information processing to cognition.

  12. Computationally Efficient Power Allocation Algorithm in Multicarrier-Based Cognitive Radio Networks: OFDM and FBMC Systems

    Directory of Open Access Journals (Sweden)

    Shaat Musbah

    2010-01-01

    Full Text Available Cognitive Radio (CR systems have been proposed to increase the spectrum utilization by opportunistically access the unused spectrum. Multicarrier communication systems are promising candidates for CR systems. Due to its high spectral efficiency, filter bank multicarrier (FBMC can be considered as an alternative to conventional orthogonal frequency division multiplexing (OFDM for transmission over the CR networks. This paper addresses the problem of resource allocation in multicarrier-based CR networks. The objective is to maximize the downlink capacity of the network under both total power and interference introduced to the primary users (PUs constraints. The optimal solution has high computational complexity which makes it unsuitable for practical applications and hence a low complexity suboptimal solution is proposed. The proposed algorithm utilizes the spectrum holes in PUs bands as well as active PU bands. The performance of the proposed algorithm is investigated for OFDM and FBMC based CR systems. Simulation results illustrate that the proposed resource allocation algorithm with low computational complexity achieves near optimal performance and proves the efficiency of using FBMC in CR context.

  13. Network Computing Infrastructure to Share Tools and Data in Global Nuclear Energy Partnership

    Science.gov (United States)

    Kim, Guehee; Suzuki, Yoshio; Teshima, Naoya

    CCSE/JAEA (Center for Computational Science and e-Systems/Japan Atomic Energy Agency) integrated a prototype system of a network computing infrastructure for sharing tools and data to support the U.S. and Japan collaboration in GNEP (Global Nuclear Energy Partnership). We focused on three technical issues to apply our information process infrastructure, which are accessibility, security, and usability. In designing the prototype system, we integrated and improved both network and Web technologies. For the accessibility issue, we adopted SSL-VPN (Security Socket Layer-Virtual Private Network) technology for the access beyond firewalls. For the security issue, we developed an authentication gateway based on the PKI (Public Key Infrastructure) authentication mechanism to strengthen the security. Also, we set fine access control policy to shared tools and data and used shared key based encryption method to protect tools and data against leakage to third parties. For the usability issue, we chose Web browsers as user interface and developed Web application to provide functions to support sharing tools and data. By using WebDAV (Web-based Distributed Authoring and Versioning) function, users can manipulate shared tools and data through the Windows-like folder environment. We implemented the prototype system in Grid infrastructure for atomic energy research: AEGIS (Atomic Energy Grid Infrastructure) developed by CCSE/JAEA. The prototype system was applied for the trial use in the first period of GNEP.

  14. Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole J.; Roth, Dan; Wilkins, David C.

    2001-01-01

    Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that

  15. Computationally Inexpensive Incorporation of Solute Transport Physics into Pore-Network Models

    Science.gov (United States)

    Mehmani, Y.; Oostrom, M.

    2014-12-01

    Several modeling approaches have been developed in the literature for simulating solute transport at the pore scale. This includes "direct modeling" where the fundamental equations are solved directly on the actual pore-scale geometry (obtained from digital images). These methods, even though very accurate, come at a high computational cost. A pore-network representation of the pore-scale geometry is a first step in reducing the computational cost. However, the geometric simplification is typically accompanied by a secondary simplification of the physics of the problem (contributing to their inaccuracy). This is seen in the widely-used "mixed-cell method" which has simplifications in two key components: 1) intra-pore mixing, and 2) inter-pore rate expressions. Nevertheless, the method is popular because it is computationally inexpensive, allowing for examining larger and more representative computational domains. In this work, we explore two novel methods for circumventing the aforementioned limitations of the mixed-cell method (intra-pore mixing and inter-pore rate expressions); all while making an effort to keep the computational cost low. We show that while intra-pore mixing can be accurately taken into account, correcting for the inter-pore rate expressions has fundamental implications on the applicability of Eulerian pore-network models and the interpretation of the results obtained therefrom. Despite recent important progress in the development of accurate and robust direct modeling tools, there is a need in the literature for simple, accurate, and inexpensive models both from a scientific as well as a practical point of view.

  16. A new computational strategy for identifying essential proteins based on network topological properties and biological information.

    Science.gov (United States)

    Qin, Chao; Sun, Yongqi; Dong, Yadong

    2017-01-01

    Essential proteins are the proteins that are indispensable to the survival and development of an organism. Deleting a single essential protein will cause lethality or infertility. Identifying and analysing essential proteins are key to understanding the molecular mechanisms of living cells. There are two types of methods for predicting essential proteins: experimental methods, which require considerable time and resources, and computational methods, which overcome the shortcomings of experimental methods. However, the prediction accuracy of computational methods for essential proteins requires further improvement. In this paper, we propose a new computational strategy named CoTB for identifying essential proteins based on a combination of topological properties, subcellular localization information and orthologous protein information. First, we introduce several topological properties of the protein-protein interaction (PPI) network. Second, we propose new methods for measuring orthologous information and subcellular localization and a new computational strategy that uses a random forest prediction model to obtain a probability score for the proteins being essential. Finally, we conduct experiments on four different Saccharomyces cerevisiae datasets. The experimental results demonstrate that our strategy for identifying essential proteins outperforms traditional computational methods and the most recently developed method, SON. In particular, our strategy improves the prediction accuracy to 89, 78, 79, and 85 percent on the YDIP, YMIPS, YMBD and YHQ datasets at the top 100 level, respectively.

  17. A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

    Directory of Open Access Journals (Sweden)

    Ankit Gupta

    2014-06-01

    Full Text Available Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.

  18. A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

    Science.gov (United States)

    Gupta, Ankit; Briat, Corentin; Khammash, Mustafa

    2014-06-01

    Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.

  19. A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

    Science.gov (United States)

    Khammash, Mustafa

    2014-01-01

    Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191

  20. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

    Science.gov (United States)

    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.

  1. THE BLENDED LEARNING ACCOMPLISHMENT OF COMPUTER AND NETWORK ENGINEERING EXPERTISE PROGRAM IN VOCATIONAL SCHOOLS

    Directory of Open Access Journals (Sweden)

    Aries Alfian Prasetyo

    2016-10-01

    Full Text Available This study aims to (1 describe supporting and inhibiting factors in blended learning implementation for the students of computer and network engineering expertise program and (2 describe the accomplishment level of the implementation. This study is designed as a descriptive study with quantitative approach. The research object is the blended learning implementation in computer and network engineering expertise program in SMK N 1 Baureno Bojonegoro. The research subjects consist of teachers, facilities, materials and applications and students in the blended learning implementation process. The data was collected using observation, surveys and interviews. It was analyzed using percentages and classification analysis. The results reveals that the blended learning has been appropriately implemented. It is proven by the analysis result of supporting and inhibiting factors including facilities, teachers’ skill, materials and applications and blended learning accomplishment. The result is also supported by the description about blended learning activity, the use of facilities, blended learning composition and the impact of implementing blended learning. The weaknesses in the implementation process are the low quantity and quality of personal computers and inadequate internet connection. Teachers and school boards are expected to work collaboratively to solve the problems thus the implementation of blended learning can be maximized.

  2. Human Environmental Disease Network: A computational model to assess toxicology of contaminants.

    Science.gov (United States)

    Taboureau, Olivier; Audouze, Karine

    2017-01-01

    During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.

  3. Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

    OpenAIRE

    Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P.; Gerstein, Mark

    2010-01-01

    The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a cano...

  4. Dynamic mechanisms of cell rigidity sensing: insights from a computational model of actomyosin networks.

    Directory of Open Access Journals (Sweden)

    Carlos Borau

    Full Text Available Cells modulate themselves in response to the surrounding environment like substrate elasticity, exhibiting structural reorganization driven by the contractility of cytoskeleton. The cytoskeleton is the scaffolding structure of eukaryotic cells, playing a central role in many mechanical and biological functions. It is composed of a network of actins, actin cross-linking proteins (ACPs, and molecular motors. The motors generate contractile forces by sliding couples of actin filaments in a polar fashion, and the contractile response of the cytoskeleton network is known to be modulated also by external stimuli, such as substrate stiffness. This implies an important role of actomyosin contractility in the cell mechano-sensing. However, how cells sense matrix stiffness via the contractility remains an open question. Here, we present a 3-D Brownian dynamics computational model of a cross-linked actin network including the dynamics of molecular motors and ACPs. The mechano-sensing properties of this active network are investigated by evaluating contraction and stress in response to different substrate stiffness. Results demonstrate two mechanisms that act to limit internal stress: (i In stiff substrates, motors walk until they exert their maximum force, leading to a plateau stress that is independent of substrate stiffness, whereas (ii in soft substrates, motors walk until they become blocked by other motors or ACPs, leading to submaximal stress levels. Therefore, this study provides new insights into the role of molecular motors in the contraction and rigidity sensing of cells.

  5. Comparative study of computational methods to detect the correlated reaction sets in biochemical networks.

    Science.gov (United States)

    Xi, Yanping; Chen, Yi-Ping Phoebe; Qian, Chen; Wang, Fei

    2011-03-01

    Correlated reaction sets (Co-Sets) are mathematically defined modules in biochemical reaction networks which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. According to the degree of association, Co-Sets can be classified into three types: perfect, partial and directional. Five approaches have been developed to calculate Co-Sets, including network-based pathway analysis, Monte Carlo sampling, linear optimization, enzyme subsets and hard-coupled reaction sets. However, differences in design and implementation of these methods lead to discrepancies in the resulted Co-Sets as well as in their use in biotechnology which need careful interpretation. In this paper, we provide a comparative study of the methods for Co-Sets computing in detail from four aspects: (i) sensitivity, (ii) completeness and soundness, (iii) flexibility and (iv) scalability. By applying them to Escherichia coli core metabolic network, the differences and relationships among these methods are clearly articulated which may be useful for potential users.

  6. SCinet Architecture: Featured at the International Conference for High Performance Computing,Networking, Storage and Analysis 2016

    Energy Technology Data Exchange (ETDEWEB)

    Lyonnais, Marc; Smith, Matt; Mace, Kate P.

    2017-02-06

    SCinet is the purpose-built network that operates during the International Conference for High Performance Computing,Networking, Storage and Analysis (Super Computing or SC). Created each year for the conference, SCinet brings to life a high-capacity network that supports applications and experiments that are a hallmark of the SC conference. The network links the convention center to research and commercial networks around the world. This resource serves as a platform for exhibitors to demonstrate the advanced computing resources of their home institutions and elsewhere by supporting a wide variety of applications. Volunteers from academia, government and industry work together to design and deliver the SCinet infrastructure. Industry vendors and carriers donate millions of dollars in equipment and services needed to build and support the local and wide area networks. Planning begins more than a year in advance of each SC conference and culminates in a high intensity installation in the days leading up to the conference. The SCinet architecture for SC16 illustrates a dramatic increase in participation from the vendor community, particularly those that focus on network equipment. Software-Defined Networking (SDN) and Data Center Networking (DCN) are present in nearly all aspects of the design.

  7. Topological quantum computing with a very noisy network and local error rates approaching one percent.

    Science.gov (United States)

    Nickerson, Naomi H; Li, Ying; Benjamin, Simon C

    2013-01-01

    A scalable quantum computer could be built by networking together many simple processor cells, thus avoiding the need to create a single complex structure. The difficulty is that realistic quantum links are very error prone. A solution is for cells to repeatedly communicate with each other and so purify any imperfections; however prior studies suggest that the cells themselves must then have prohibitively low internal error rates. Here we describe a method by which even error-prone cells can perform purification: groups of cells generate shared resource states, which then enable stabilization of topologically encoded data. Given a realistically noisy network (≥10% error rate) we find that our protocol can succeed provided that intra-cell error rates for initialisation, state manipulation and measurement are below 0.82%. This level of fidelity is already achievable in several laboratory systems.

  8. Exact computation of the Maximum Entropy Potential of spiking neural networks models

    CERN Document Server

    Cofre, Rodrigo

    2014-01-01

    Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the ?tting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.

  9. Closed-loop neuro-robotic experiments to test computational properties of neuronal networks.

    Science.gov (United States)

    Tessadori, Jacopo; Chiappalone, Michela

    2015-03-02

    Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks.

  10. A system for routing and capacity assignment in computer communication networks

    Science.gov (United States)

    Gavish, Bezalel; Neuman, Irina

    1989-04-01

    The combined problem of selecting a primary route for each communicating pair and a capacity value for each link in computer communication networks is considered. The network topology and traffic characteristics are given: a set of candidate routes and of candidate capacities for each link are also available. The goal is to obtain the least costly feasible design where the costs include both capacity and queuing components. Lagrangean relaxation and subgradient optimization techniques were used to obtain verifiable solutions to the problem. The method was tested on several topologies, and in all cases good feasible solutions, as well as tight lower bounds, were obtained. The model can be generalized to deal with different classes of customers, characterized by different priorities, message lengths, and/or delay requirements.

  11. Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

    Science.gov (United States)

    Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

    1985-07-01

    Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

  12. A Cloud Theory-Based Trust Computing Model in Social Networks

    Directory of Open Access Journals (Sweden)

    Fengming Liu

    2016-12-01

    Full Text Available How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node’s comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved.

  13. A Hitchhiker's Guide to the Turing Galaxy: on naming the age of the networked digital computer

    Directory of Open Access Journals (Sweden)

    GRASSMUCK, Volker

    2007-12-01

    Full Text Available The most commonly used name for our era is that of the `information society, which is a rather unexpressive and, strictly speaking, tautological term. The informatics & society scholar Wolfgang Coy, following the example of McLuhan`s Gutenberg Galaxy, has introduced the concept of the Turing Galaxy. The paper retraces the pre-history of the concept, its grounding in the fundamental breakthroughs of the British mathematician Alan M. Turing, the Turing Machine and the Turing Test, analyses the reception of the concept in a variety of fields of scholarship and asks for its value in the further debate on the knowledge environment of the networked computer.

  14. Computing Nash Equilibrium in Wireless Ad Hoc Networks: A Simulation-Based Approach

    Directory of Open Access Journals (Sweden)

    Peter Bulychev

    2012-02-01

    Full Text Available This paper studies the problem of computing Nash equilibrium in wireless networks modeled by Weighted Timed Automata. Such formalism comes together with a logic that can be used to describe complex features such as timed energy constraints. Our contribution is a method for solving this problem using Statistical Model Checking. The method has been implemented in UPPAAL model checker and has been applied to the analysis of Aloha CSMA/CD and IEEE 802.15.4 CSMA/CA protocols.

  15. Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe-Based Neural Network Computing

    Science.gov (United States)

    Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko

    In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.

  16. Rough Set Soft Computing Cancer Classification and Network: One Stone, Two Birds

    Directory of Open Access Journals (Sweden)

    Yue Zhang

    2010-07-01

    Full Text Available Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

  17. An Australian Perspective On The Challenges For Computer And Network Security For Novice End-Users

    Directory of Open Access Journals (Sweden)

    Patryk Szewczyk

    2012-12-01

    Full Text Available It is common for end-users to have difficulty in using computer or network security appropriately and thus have often been ridiculed when misinterpreting instructions or procedures. This discussion paper details the outcomes of research undertaken over the past six years on why security is overly complex for end-users. The results indicate that multiple issues may render end-users vulnerable to security threats and that there is no single solution to address these problems. Studies on a small group of senior citizens has shown that educational seminars can be beneficial in ensuring that simple security aspects are understood and used appropriately.

  18. A New Model for Capturing the Spread of Computer Viruses on Complex-Networks

    Directory of Open Access Journals (Sweden)

    Chunming Zhang

    2013-01-01

    Full Text Available Based on complex network, this paper proposes a novel computer virus propagation model which is motivated by the traditional SEIRQ model. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its basic reproduction is less than one, and the viral equilibrium is globally attractive when the basic reproduction is greater than one. Some numerical simulations are finally given to illustrate the main results, implying that these results are applicable to depict the dynamics of virus propagation.

  19. Towards a Versatile Tele-Education Platform for Computer Science Educators Based on the Greek School Network

    Science.gov (United States)

    Paraskevas, Michael; Zarouchas, Thomas; Angelopoulos, Panagiotis; Perikos, Isidoros

    2013-01-01

    Now days the growing need for highly qualified computer science educators in modern educational environments is commonplace. This study examines the potential use of Greek School Network (GSN) to provide a robust and comprehensive e-training course for computer science educators in order to efficiently exploit advanced IT services and establish a…

  20. National High-Performance Computing and Networking Act. Report To Accompany S. 343, Senate, 102d Congess, 1st Session.

    Science.gov (United States)

    Congress of the U.S., Washington, DC. Senate Committee on Energy and Natural Resources.

    The purpose of the bill (S. 343), as reported by the Senate Committee on Energy and Natural Resources, is to establish a federal commitment to the advancement of high-performance computing, improve interagency planning and coordination of federal high-performance computing and networking activities, authorize a national high-speed computer…

  1. Experimental and computational methods for the analysis and modeling of signaling networks.

    Science.gov (United States)

    Gherardini, Pier Federico; Helmer-Citterich, Manuela

    2013-03-25

    External cues are processed and integrated by signal transduction networks that drive appropriate cellular responses. Characterizing these programs, as well as how their deregulation leads to disease, is crucial for our understanding of cell biology. The past ten years have witnessed a gradual increase in the number of molecular parameters that can be simultaneously measured in a sample. Moreover our capacity to handle multiple samples in parallel has expanded, thus allowing a deeper profiling of cellular states under diverse experimental conditions. These technological advances have been complemented by the development of computational methods aimed at mining, analyzing and modeling these data. In this review we give a general overview of the most important experimental and computational techniques used in the field and describe several interesting application of these methodologies. We conclude by highlighting the issues that we think will keep researchers in the field busy in the next few years. Copyright © 2012 Elsevier B.V. All rights reserved.

  2. Computer vision system for egg volume prediction using backpropagation neural network

    Science.gov (United States)

    Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

    2017-11-01

    Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

  3. Some issues related to simulation of the tracking and communications computer network

    Science.gov (United States)

    Lacovara, Robert C.

    1989-01-01

    The Communications Performance and Integration branch of the Tracking and Communications Division has an ongoing involvement in the simulation of its flight hardware for Space Station Freedom. Specifically, the communication process between central processor(s) and orbital replaceable units (ORU's) is simulated with varying degrees of fidelity. The results of investigations into three aspects of this simulation effort are given. The most general area involves the use of computer assisted software engineering (CASE) tools for this particular simulation. The second area of interest is simulation methods for systems of mixed hardware and software. The final area investigated is the application of simulation methods to one of the proposed computer network protocols for space station, specifically IEEE 802.4.

  4. Optimizing the Number of Cooperating Terminals for Energy Aware Task Computing in Wireless Networks

    DEFF Research Database (Denmark)

    Olsen, Anders Brødløs; Fitzek, Frank H. P.; Koch, Peter

    2005-01-01

    It is generally accepted that energy consumption is a significant design constraint for mobile handheld systems, therefore motivations for methods optimizing the energy consumption making better use of the restricted battery resources are evident. A novel concept of distributed task computing...... is previously proposed (D2VS), where the overall idea of selective distribution of tasks among terminals is made. In this paper the optimal number of terminals for cooperative task computing in a wireless network will be investigated. The paper presents an energy model for the proposed scheme. Energy...... consumption of the terminals with respect to their workload and the overhead of distributing tasks among terminals are taken into account. The paper shows, that the number of cooperating terminals is in general limited to a few, though alternating with respect to the various system parameters....

  5. Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks

    DEFF Research Database (Denmark)

    Tan, Chris Soon Heng; Linding, Rune

    2009-01-01

    The explosion of site- and context-specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector...... kinases. This is important for (re)constructing transient kinase-substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high-throughput protein-interaction studies due to their transient nature and strong...... dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts...

  6. Utilizing neural networks in magnetic media modeling and field computation: A review.

    Science.gov (United States)

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2014-11-01

    Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

  7. Utilizing neural networks in magnetic media modeling and field computation: A review

    Directory of Open Access Journals (Sweden)

    Amr A. Adly

    2014-11-01

    Full Text Available Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

  8. Motivating students' participation in a computer networks course by means of magic, drama and games.

    Science.gov (United States)

    Hilas, Constantinos S; Politis, Anastasios

    2014-01-01

    The recent economic crisis has forced many universities to cut down expenses by packing students into large lecture groups. The problem with large auditoria is that they discourage dialogue between students and faculty and they burden participation. Adding to this, students in computer science courses usually find the field to be full of theoretical and technical concepts. Lack of understanding leads them to lose interest and / or motivation. Classroom experience shows that the lecturer could employ alternative teaching methods, especially for early-year undergraduate students, in order to grasp their interest and introduce basic concepts. This paper describes some of the approaches that may be used to keep students interested and make them feel comfortable as they comprehend basic concepts in computer networks. The lecturing procedure was enriched with games, magic tricks and dramatic representations. This approach was used experimentally for two semesters and the results were more than encouraging.

  9. An integrated geometric modelling framework for patient-specific computational haemodynamic study on wide-ranged vascular network.

    Science.gov (United States)

    Torii, Ryo; Oshima, Marie

    2012-01-01

    Patient-specific haemodynamic computations have been used as an effective tool in researches on cardiovascular disease associated with haemodynamics such as atherosclerosis and aneurysm. Recent development of computer resource has enabled 3D haemodynamic computations in wide-spread arterial network but there are still difficulties in modelling vascular geometry because of noise and limited resolution in medical images. In this paper, an integrated framework to model an arterial network tree for patient-specific computational haemodynamic study is developed. With this framework, 3D vascular geometry reconstruction of an arterial network and quantification of its geometric feature are aimed. The combination of 3D haemodynamic computation and vascular morphology quantification helps better understand the relationship between vascular morphology and haemodynamic force behind 'geometric risk factor' for cardiovascular diseases. The proposed method is applied to an intracranial arterial network to demonstrate its accuracy and effectiveness. The results are compared with the marching-cubes (MC) method. The comparison shows that the present modelling method can reconstruct a wide-ranged vascular network anatomically more accurate than the MC method, particularly in peripheral circulation where the image resolution is low in comparison to the vessel diameter, because of the recognition of an arterial network connectivity based on its centreline.

  10. Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

    KAUST Repository

    Mora Cordova, Angel

    2018-01-30

    One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite\\'s conductivity based on these parameters.

  11. Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

    2010-01-01

    For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

  12. NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 39: The role of computer networks in aerospace engineering

    Science.gov (United States)

    Bishop, Ann P.; Pinelli, Thomas E.

    1994-01-01

    This paper presents selected results from an empirical investigation into the use of computer networks in aerospace engineering. Such networks allow aerospace engineers to communicate with people and access remote resources through electronic mail, file transfer, and remote log-in. The study drew its subjects from private sector, government and academic organizations in the U.S. aerospace industry. Data presented here were gathered in a mail survey, conducted in Spring 1993, that was distributed to aerospace engineers performing a wide variety of jobs. Results from the mail survey provide a snapshot of the current use of computer networks in the aerospace industry, suggest factors associated with the use of networks, and identify perceived impacts of networks on aerospace engineering work and communication.

  13. Modeling, Simulation and Analysis of Complex Networked Systems: A Program Plan for DOE Office of Advanced Scientific Computing Research

    Energy Technology Data Exchange (ETDEWEB)

    Brown, D L

    2009-05-01

    Many complex systems of importance to the U.S. Department of Energy consist of networks of discrete components. Examples are cyber networks, such as the internet and local area networks over which nearly all DOE scientific, technical and administrative data must travel, the electric power grid, social networks whose behavior can drive energy demand, and biological networks such as genetic regulatory networks and metabolic networks. In spite of the importance of these complex networked systems to all aspects of DOE's operations, the scientific basis for understanding these systems lags seriously behind the strong foundations that exist for the 'physically-based' systems usually associated with DOE research programs that focus on such areas as climate modeling, fusion energy, high-energy and nuclear physics, nano-science, combustion, and astrophysics. DOE has a clear opportunity to develop a similarly strong scientific basis for understanding the structure and dynamics of networked systems by supporting a strong basic research program in this area. Such knowledge will provide a broad basis for, e.g., understanding and quantifying the efficacy of new security approaches for computer networks, improving the design of computer or communication networks to be more robust against failures or attacks, detecting potential catastrophic failure on the power grid and preventing or mitigating its effects, understanding how populations will respond to the availability of new energy sources or changes in energy policy, and detecting subtle vulnerabilities in large software systems to intentional attack. This white paper outlines plans for an aggressive new research program designed to accelerate the advancement of the scientific basis for complex networked systems of importance to the DOE. It will focus principally on four research areas: (1) understanding network structure, (2) understanding network dynamics, (3) predictive modeling and simulation for complex

  14. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-08-01

    Full Text Available In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs. The two techniques, Neural Fuzzy Inference System (ANFIS and Artificial Neural Network (ANN, focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO, Gravitational Search Algorithm (GSA, and Backtracking Search Algorithm (BSA. The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

  15. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

    Science.gov (United States)

    Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

    2016-08-06

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

  16. An Object-Oriented Network-Centric Software Architecture for Physical Computing

    Science.gov (United States)

    Palmer, Richard

    1997-08-01

    Recent developments in object-oriented computer languages and infrastructure such as the Internet, Web browsers, and the like provide an opportunity to define a more productive computational environment for scientific programming that is based more closely on the underlying mathematics describing physics than traditional programming languages such as FORTRAN or C++. In this talk I describe an object-oriented software architecture for representing physical problems that includes classes for such common mathematical objects as geometry, boundary conditions, partial differential and integral equations, discretization and numerical solution methods, etc. In practice, a scientific program written using this architecture looks remarkably like the mathematics used to understand the problem, is typically an order of magnitude smaller than traditional FORTRAN or C++ codes, and hence easier to understand, debug, describe, etc. All objects in this architecture are ``network-enabled,'' which means that components of a software solution to a physical problem can be transparently loaded from anywhere on the Internet or other global network. The architecture is expressed as an ``API,'' or application programmers interface specification, with reference embeddings in Java, Python, and C++. A C++ class library for an early version of this API has been implemented for machines ranging from PC's to the IBM SP2, meaning that phidentical codes run on all architectures.

  17. Trust in social computing. The case of peer-to-peer file sharing networks

    Directory of Open Access Journals (Sweden)

    Heng Xu

    2011-09-01

    Full Text Available Social computing and online communities are changing the fundamental way people share information and communicate with each other. Social computing focuses on how users may have more autonomy to express their ideas and participate in social exchanges in various ways, one of which may be peer-to-peer (P2P file sharing. Given the greater risk of opportunistic behavior by malicious or criminal communities in P2P networks, it is crucial to understand the factors that affect individual’s use of P2P file sharing software. In this paper, we develop and empirically test a research model that includes trust beliefs and perceived risks as two major antecedent beliefs to the usage intention. Six trust antecedents are assessed including knowledge-based trust, cognitive trust, and both organizational and peer-network factors of institutional trust. Our preliminary results show general support for the model and offer some important implications for software vendors in P2P sharing industry and regulatory bodies.

  18. Using the electrocorticographic speech network to control a brain-computer interface in humans

    Science.gov (United States)

    Leuthardt, Eric C.; Gaona, Charles; Sharma, Mohit; Szrama, Nicholas; Roland, Jarod; Freudenberg, Zac; Solis, Jamie; Breshears, Jonathan; Schalk, Gerwin

    2011-06-01

    Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

  19. Using the Electrocorticographic Speech Network to Control a Brain-Computer Interface in Humans

    Science.gov (United States)

    Leuthardt, Eric C.; Gaona, Charles; Sharma, Mohit; Szrama, Nicholas; Roland, Jarod; Freudenberg, Zac; Solis, Jamie; Breshears, Jonathan; Schalk, Gerwin

    2013-01-01

    Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68 and 91% within 15 minutes. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive. PMID:21471638

  20. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

    Directory of Open Access Journals (Sweden)

    Yonghui Dai

    2014-01-01

    Full Text Available The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.