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1
Scardoni, Giovanni, Gabriele Tosadori, Mohammed Faizan, Fausto Spoto, Franco Fabbri, and Carlo Laudanna. "Biological network analysis with CentiScaPe: centralities and experimental dataset integration." F1000Research 3 (July 1, 2014): 139. http://dx.doi.org/10.12688/f1000research.4477.1.
Повний текст джерелаАнотація:
The growing dimension and complexity of available experimental data generating biological networks has increased the need for tools allowing to categorize nodes by their topological relevance in biological networks. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes for the identification of the most important nodes of a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be also integrated with data sets from lab experiments, such as expression or phosphorylation levels of the proteins represented in the network, using the graphical features of the tool. This opens a new perspective in the analysis of biological networks, since integration of topological analysis with lab experimental data can increase the predictive power of a bioinformatical analysis.
2
Kryukov, Ya V., D. A. Pokamestov, E. V. Rogozhnikov, S. A. Novichkov, and D. V. Lakontsev. "Analysis of Computational Complexity and Processing Time Evaluation of the Protocol Stack in 5G New Radio." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 3 (September 25, 2020): 31–37. http://dx.doi.org/10.21293/1818-0442-2020-23-3-31-37.
Повний текст джерелаАнотація:
Currently, an active deployment of radio access networks for mobile communication systems 5G New Radio is being observed. The architecture of networks is developing rapidly, where significant part of the functions is performed in a virtual cloud space of a personal computer. The computing power of a personal computer must be sufficient to execute network protocols in real time. To reduce the cost of deploying 5G NR networks, the configuration of each remote computer must be optimally matched to the scale of a particular network. Therefore, an urgent direction of research is the assessment of the execution time of the 5G NR protocol stack on various configurations of computers and the development of a mathematical model for data analysis, approximation of dependencies and making recommendations. In this paper, the authors provide an overview of the main 5G NR network architectures, as well as a description of the methods and tools that can be used to estimate the computational complexity of the 5G NR protocol stack. The final section provides an analysis of the computational complexity of the protocol stack, obtained during the experiments by colleagues in partner institutions.
3
Arefeen, Ashraful, Xinshu Xiao, and Tao Jiang. "DeepPASTA: deep neural network based polyadenylation site analysis." Bioinformatics 35, no. 22 (April 25, 2019): 4577–85. http://dx.doi.org/10.1093/bioinformatics/btz283.
Повний текст джерелаАнотація:
Abstract Motivation Alternative polyadenylation (polyA) sites near the 3′ end of a pre-mRNA create multiple mRNA transcripts with different 3′ untranslated regions (3′ UTRs). The sequence elements of a 3′ UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3′ UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites. Results In this paper, we propose a new deep learning model, called DeepPASTA, for predicting polyA sites from both sequence and RNA secondary structure data. The model is then extended to predict tissue-specific polyA sites. Moreover, the tool can predict the most dominant (i.e. frequently used) polyA site of a gene in a specific tissue and relative dominance when two polyA sites of the same gene are given. Our extensive experiments demonstrate that DeepPASTA signisficantly outperforms the existing tools for polyA site prediction and tissue-specific relative and absolute dominant polyA site prediction. Availability and implementation https://github.com/arefeen/DeepPASTA Supplementary information Supplementary data are available at Bioinformatics online.
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Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics 22, no. 5 (September 23, 2015): 545–70. http://dx.doi.org/10.5194/npg-22-545-2015.
Повний текст джерелаАнотація:
Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.
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Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics Discussions 2, no. 2 (April 30, 2015): 709–80. http://dx.doi.org/10.5194/npgd-2-709-2015.
Повний текст джерелаАнотація:
Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.
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Hafner-Burton, Emilie M., Miles Kahler, and Alexander H. Montgomery. "Network Analysis for International Relations." International Organization 63, no. 3 (July 2009): 559–92. http://dx.doi.org/10.1017/s0020818309090195.
Повний текст джерелаАнотація:
International relations research has regarded networks as a particular mode of organization, distinguished from markets or state hierarchies. In contrast, network analysis permits the investigation and measurement of network structures—emergent properties of persistent patterns of relations among agents that can define, enable, and constrain those agents. Network analysis offers both a toolkit for identifying and measuring the structural properties of networks and a set of theories, typically drawn from contexts outside international relations, that relate structures to outcomes. Network analysis challenges conventional views of power in international relations by defining network power in three different ways: access, brokerage, and exit options. Two issues are particularly important to international relations: the ability of actors to increase their power by enhancing and exploiting their network positions, and the fungibility of network power. The value of network analysis in international relations has been demonstrated in precise description of international networks, investigation of network effects on key international outcomes, testing of existing network theory in the context of international relations, and development of new sources of data. Partial or faulty incorporation of network analysis, however, risks trivial conclusions, unproven assertions, and measures without meaning. A three-part agenda is proposed for future application of network analysis to international relations: import the toolkit to deepen research on international networks; test existing network theories in the domain of international relations; and test international relations theories using the tools of network analysis.
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Goay, Chan Hong, Azniza Abd Aziz, Nur Syazreen Ahmad, and Patrick Goh. "Progress in neural network based techniques for signal integrity analysis–a survey." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 276–82. http://dx.doi.org/10.11591/eei.v8i1.1405.
Повний текст джерелаАнотація:
With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.
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Boeing, Geoff. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood." Urban Science 3, no. 1 (March 1, 2019): 28. http://dx.doi.org/10.3390/urbansci3010028.
Повний текст джерелаАнотація:
OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.
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Zanin, Massimiliano, Miguel Romance, Santiago Moral, and Regino Criado. "Credit Card Fraud Detection through Parenclitic Network Analysis." Complexity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5764370.
Повний текст джерелаАнотація:
The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.
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McDaid, Alexander, Eoghan Furey, and Kevin Curran. "Wireless Interference Analysis for Home IoT Security Vulnerability Detection." International Journal of Wireless Networks and Broadband Technologies 10, no. 2 (July 2021): 55–77. http://dx.doi.org/10.4018/ijwnbt.2021070104.
Повний текст джерелаАнотація:
The integrity of wireless networks that make up the clear majority of IoT networks lack the inherent security of their wired counterparts. With the growth of the internet of things (IoT) and its pervasive nature in the modern home environment, it has caused a spike in security concerns over how the network infrastructure handles, transmits, and stores data. New wireless attacks such as KeySniffer and other attacks of this type cannot be tracked by traditional solutions. Therefore, this study investigates if wireless spectrum frequency monitoring using interference analysis tools can aid in the monitoring of device signals within a home IoT network. This could be used enhance the security compliance guidelines set forth by OWASP and NIST for these network types and the devices associated. Active and passive network scanning tools are used to provide analysis of device vulnerability and as comparison for device discovery purposes. The work shows the advantages and disadvantages of this signal pattern testing technique compared to traditional network scanning methods. The authors demonstrate how RF spectrum analysis is an effective way of monitoring network traffic over the air waves but also possesses limitations in that knowledge is needed to decipher these patterns. This article demonstrates alternative methods of interference analysis detection.
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Romano, P., G. Bertolini, F. De Paoli, M. Fattore, D. Marra, G. Mauri, E. Merelli, I. Porro, S. Scaglione, and L. Milanesi. "Network integration of data and analysis of oncology interest." Journal of Integrative Bioinformatics 3, no. 1 (June 1, 2006): 45–55. http://dx.doi.org/10.1515/jib-2006-21.
Повний текст джерелаАнотація:
Summary The Human Genome Project has deeply transformed biology and the field has since then expanded to the management, processing, analysis and visualization of large quantities of data from genomics, proteomics, medicinal chemistry and drug screening. This huge amount of data and the heterogeneity of software tools that are used implies the adoption on a very large scale of new, flexible tools that can enable researchers to integrate data and analysis on the network. ICT technology standards and tools, like Web Services and related languages, and workflow management systems, can support the creation and deployment of such systems. While a number of Web Services are appearing and personal workflow management systems are also being more and more offered to researchers, a reference portal enabling the vast majority of unskilled researchers to take profit from these new technologies is still lacking. In this paper, we introduce the rationale for the creation of such a portal and present the architecture and some preliminary results for the development of a portal for the enactment of workflows of interest in oncology.
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Klovdahl, Alden S. "View_net: A new tool for network analysis." Social Networks 8, no. 4 (December 1986): 313–42. http://dx.doi.org/10.1016/0378-8733(86)90001-8.
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Jacob, Rinku, K. P. Harikrishnan, R. Misra, and G. Ambika. "Weighted recurrence networks for the analysis of time-series data." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (January 2019): 20180256. http://dx.doi.org/10.1098/rspa.2018.0256.
Повний текст джерелаАнотація:
Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.
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Scardoni, Giovanni, Gabriele Tosadori, Mohammed Faizan, Fausto Spoto, Franco Fabbri, and Carlo Laudanna. "Biological network analysis with CentiScaPe: centralities and experimental dataset integration." F1000Research 3 (July 7, 2015): 139. http://dx.doi.org/10.12688/f1000research.4477.2.
Повний текст джерелаАнотація:
The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.
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von Landesberger, Tatiana, Simon Diel, Sebastian Bremm, and Dieter W. Fellner. "Visual analysis of contagion in networks." Information Visualization 14, no. 2 (May 28, 2013): 93–110. http://dx.doi.org/10.1177/1473871613487087.
Повний текст джерелаАнотація:
Contagion is a process whereby the collapse of a node in a network leads to the collapse of neighboring nodes and thereby sets off a chain reaction in the network. It thus creates a special type of time-dependent network. Such processes are studied in various applications, for example, in financial network analysis, infection diffusion prediction, supply-chain management, or gene regulation. Visual analytics methods can help analysts examine contagion effects. For this purpose, network visualizations need to be complemented with specific features to illustrate the contagion process. Moreover, new visual analysis techniques for comparison of contagion need to be developed. In this paper, we propose a system geared to the visual analysis of contagion. It includes the simulation of contagion effects as well as their visual exploration. We present new tools able to compare the evolution of the different contagion processes. In this way, propagation of disturbances can be effectively analyzed. We focus on financial networks; however, our system can be applied to other use cases as well.
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Yang, Ming, Jia-Lei Chen, Li-Wen Xu, and Guang Ji. "Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–23. http://dx.doi.org/10.1155/2013/731969.
Повний текст джерелаАнотація:
The concept of “network target” has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.
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Mheich, Ahmad, Fabrice Wendling, and Mahmoud Hassan. "Brain network similarity: methods and applications." Network Neuroscience 4, no. 3 (January 2020): 507–27. http://dx.doi.org/10.1162/netn_a_00133.
Повний текст джерелаАнотація:
Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.
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Mosadegh, Mohammad Javad, and Mehdi Behboudi. "USING SOCIAL NETWORK PARADIGM FOR DEVELOPING A CONCEPTUAL FRAMEWORK IN CRM." Australian Journal of Business and Management Research 01, no. 04 (November 17, 2011): 63–71. http://dx.doi.org/10.52283/nswrca.ajbmr.20110104a06.
Повний текст джерелаАнотація:
This study develops a conceptual framework for applying social networks in usual CRM models. Recent changing in customer relationship theme and putting new media and network-based paradigm into practice makes it imperative to find how social networks affect CRMs. Accordingly, this study explains the role of social networks in customer relationship management by using its analysis, tools and aspects of this concepts based on CRM models. We have provided a SCRM framework that is based on usual CRM models and incorporates Social networks and its tools, methods and analysis. The framework is combination of Social networks concept and traditional CRM concepts.
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Périssé, André Reynaldo Santos, and José Augusto da Costa Nery. "The relevance of social network analysis on the epidemiology and prevention of sexually transmitted diseases." Cadernos de Saúde Pública 23, suppl 3 (2007): S361—S369. http://dx.doi.org/10.1590/s0102-311x2007001500004.
Повний текст джерелаАнотація:
Historically, the epidemiology of sexually transmitted diseases (STD) has been based on individual attributes and behavior. However, STD constitute a good example of diseases that depend on personal contacts for dissemination. Social network analysis is a relatively new technique that studies the interactions among people. Since 1985 when it was first used for STD, some studies have been done using the technique, especially in the last 10 years. The two network-based designs, sociocentric or complete networks and egocentric or personal networks, are currently recognized as important tools for a better understanding of STD's dynamic. Here an overview is presented of social network analysis: the technique, its use, and its limitations. Ethical considerations regarding social network analyses are also briefly discussed.
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Shvindina, Hanna, Inna Balahurovska, and Iryna Heiets. "Network Leadership Theory: A New Research Agenda." Business Ethics and Leadership 6, no. 1 (2022): 25–32. http://dx.doi.org/10.21272/bel.6(1).25-32.2022.
Повний текст джерелаАнотація:
As a social phenomenon and a mechanism for influencing others, leadership always has interested scientists. Literary reviews on the theoretical foundations, types of classifications, and prospects for leadership development are an essential element of careful study of this concept. Today, the systematization of types of leadership on various grounds is a necessary component for understanding the nature of this phenomenon. The paper analyzes 2,000 published research papers from the Scopus database, identifying critical terms related to leadership. Establishing links between leadership and related concepts creates the most accurate picture for further research on this topic. The study is devoted to studying the theoretical and practical experience of scientists worldwide who research management as a critical aspect in building an effective organization. Systematization of such data is the basis for identifying current and fundamentally new directions of effective leadership. The keyword in the described bibliometric analysis was the concept of “leadership”. New clusters have been formed with the most influential definitions of transformational leadership, communication, and shared leadership. The combination of quantitative and qualitative research methods determined the future directions of study in the further search for effective leadership tools. The content analysis of the most cited scientific works was carried out in the research, which reveals the essence of tools for the development of effective leadership and the need for timely diagnosis of the negative behavior of managers. Bibliometric analysis and analysis of the development of leadership theory reveal the need to implement the principles of Network Leadership Theory in modern organizations, which are the basis of the state’s economic development as a whole.
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Tsvetanov, Tsvetomir. "Computer Networks Security Models - A New Approach for Denial-of-Services Attacks Mitigation." Serdica Journal of Computing 4, no. 3 (October 21, 2010): 385–416. http://dx.doi.org/10.55630/sjc.2010.4.385-416.
Повний текст джерелаАнотація:
Computer networks are a critical factor for the performance of a modern company. Managing networks is as important as managing any other aspect of the company’s performance and security. There are many tools and appliances for monitoring the traffic and analyzing the network flow security. They use different approaches and rely on a variety of characteristics of the network flows. Network researchers are still working on a common approach for security baselining that might enable early watch alerts. This research focuses on the network security models, particularly the Denial-of-Services (DoS) attacks mitigation, based on a network flow analysis using the flows measurements and the theory of Markov models. The content of the paper comprises the essentials of the author’s doctoral thesis.
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Coffey, Kyle, Richard Smith, Leandros Maglaras, and Helge Janicke. "Vulnerability Analysis of Network Scanning on SCADA Systems." Security and Communication Networks 2018 (March 13, 2018): 1–21. http://dx.doi.org/10.1155/2018/3794603.
Повний текст джерелаАнотація:
Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICSs) have controlled the regulation and management of Critical National Infrastructure environments for decades. With the demand for remote facilities to be controlled and monitored, industries have continued to adopt Internet technology into their ICS and SCADA systems so that their enterprise can span across international borders in order to meet the demand of modern living. Although this is a necessity, it could prove to be potentially dangerous. The devices that make up ICS and SCADA systems have bespoke purposes and are often inherently vulnerable and difficult to merge with newer technologies. The focus of this article is to explore, test, and critically analyse the use of network scanning tools against bespoke SCADA equipment in order to identify the issues with conducting asset discovery or service detection on SCADA systems with the same tools used on conventional IP networks. The observations and results of the experiments conducted are helpful in evaluating their feasibility and whether they have a negative impact on how they operate. This in turn helps deduce whether network scanners open a new set of vulnerabilities unique to SCADA systems.
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Truchaud, Alain, Tanguy Le Neel, Hugues Brochard, Sophie Malvaux, Marine Moyon, and Murielle Cazaubiel. "New tools for laboratory design and management." Clinical Chemistry 43, no. 9 (September 1, 1997): 1709–15. http://dx.doi.org/10.1093/clinchem/43.9.1709.
Повний текст джерелаАнотація:
Abstract The clinical laboratory is changing from a place of activity based on sample analysis to an in vitro diagnostic network. To convince our team, partners, and administrators, we need new comprehensive tools to define a strategy with limited risk of failure or conflicts. Specific quality goals should be established before choosing automated tools for sample handling, analytical systems, laboratory information systems, communication systems, or advanced technologies. A system approach maps and simplifies the process, based more on a functional study than on classical disciplines. A customer–supplier approach establishes the requirements between partners either inside or outside the laboratory. The quality system must be a management tool, linking samples, tasks, information, and documents. Quantitative simulation modeling explores different automation alternatives and their impact on laboratory workflow. Finally, integration of results in interactive semirealistic simulation tools for laboratory design or reengineering can be used as communications tools to involve laboratory professionals in the change of their practice.
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Hodge, Emily, Joshua Childs, and Wayne Au. "Power, brokers, and agendas: New directions for the use of social network analysis in education policy." education policy analysis archives 28 (August 17, 2020): 117. http://dx.doi.org/10.14507/epaa.28.5874.
Повний текст джерелаАнотація:
In this special issue, Researching 21st Century Education Policy Through Social Network Analysis, authors use social network analysis (SNA) to explore policy networks, broaden the current literature of sociological approaches to SNA, and/or incorporate new lenses for interpreting policy networks from political science or other academic disciplines. This editorial introduction first provides an overview of policy networks and their relevance in education. Then, the editors describe existing work applying the tools of SNA to education policy and highlight understudied areas before describing the articles included in this issue. These articles apply SNA to a variety of education policy issues, including large scale policies such as the Every Student Succeeds Act and the Common Core State Standards, charter schools, and the relationship between system and non-system actors. Articles highlight multiple applications of SNA, including how SNA can be used to advance theory, as well as describe and predict policy networks.
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Morrison, David A. "Networks in phylogenetic analysis: new tools for population biology." International Journal for Parasitology 35, no. 5 (April 2005): 567–82. http://dx.doi.org/10.1016/j.ijpara.2005.02.007.
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Heleno, Ruben, Cristina Garcia, Pedro Jordano, Anna Traveset, José Maria Gómez, Nico Blüthgen, Jane Memmott, et al. "Ecological networks: delving into the architecture of biodiversity." Biology Letters 10, no. 1 (January 2014): 20131000. http://dx.doi.org/10.1098/rsbl.2013.1000.
Повний текст джерелаАнотація:
In recent years, the analysis of interaction networks has grown popular as a framework to explore ecological processes and the relationships between community structure and its functioning. The field has rapidly grown from its infancy to a vibrant youth, as reflected in the variety and quality of the discussions held at the first international symposium on Ecological Networks in Coimbra—Portugal (23–25 October 2013). The meeting gathered 170 scientists from 22 countries, who presented data from a broad geographical range, and covering all stages of network analyses, from sampling strategies to effective ways of communicating results, presenting new analytical tools, incorporation of temporal and spatial dynamics, new applications and visualization tools. 1 During the meeting it became evident that while many of the caveats diagnosed in early network studies are successfully being tackled, new challenges arise, attesting to the health of the discipline.
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Hagan, Daniel, and Martin Hagan. "Soft Computing Tools for Virtual Drug Discovery." Journal of Artificial Intelligence and Soft Computing Research 8, no. 3 (July 1, 2018): 173–89. http://dx.doi.org/10.1515/jaiscr-2018-0012.
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AbstractIn this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.
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Alenina, Elena Eduardovna, Sergey Vladimirovich Bolotnikov, Lyubov Viktorovna Borodacheva, Viktoriya Leonidovna Grankina, Dmitri Vladimirovich Redin, and Vitaly Lvovich Senderov. "Management tools in modern distributed social communities." LAPLAGE EM REVISTA 7, Extra-C (June 19, 2021): 48–56. http://dx.doi.org/10.24115/s2446-622020217extra-c983p.48-56.
Повний текст джерелаАнотація:
The article is devoted to the consideration and generalization of modern management capabilities and tools of distributed social communities formed based on online resources (social networks) to achieve the set socio-economic management goals. The authors conducted a problem analysis of the identified opportunities for managing specialized social thematic resources in the implementation of joint projects, the formation of social groups based on interests and hobbies, and the promotion of brands and products. The authors identify software tools for managing social network media resources. These tools allow collecting data on consumer interaction (b2c), monitoring thematic information, and attracting a new target audience.
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Cumbo, Fabio, Giovanni Felici, and Paola Bertolazzi. "Selecting relevant nodes and structures in biological networks. BiNAT: a new plugin for Cytoscape." F1000Research 3 (November 21, 2014): 287. http://dx.doi.org/10.12688/f1000research.5753.1.
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Summary: In order to understand a network function, it’s necessary the understanding of its topology, since the topology is designed to better undertake the function, and the efficiency of network function is influenced by its topology. For this reason, topological analysis of complex networks has been an intensely researched area in the last decade.Results: Here we propose BiNAT, a Cytoscape [1] plugin able to perform network analysis, providing a full set of useful tools to discover the most significant nodes and structures in a network.Conclusions: The plugin has been approved on the official Cytoscape plugins repository and it is downloadable directly from this site: http://dmb.iasi.cnr.it/binat.php where a full guide is also available.
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Iakovakis, George, Constantinos-Giovanni Xarhoulacos, Konstantinos Giovas, and Dimitris Gritzalis. "Analysis and Classification of Mitigation Tools against Cyberattacks in COVID-19 Era." Security and Communication Networks 2021 (August 19, 2021): 1–21. http://dx.doi.org/10.1155/2021/3187205.
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The COVID-19 outbreak has forced businesses to shift to an unprecedented “work from home” company environment. While this provides advantages for employees and businesses, it also leads to a multitude of shortcomings, most prevalent of which is the emergence of additional security risks. Previous to the outbreak, company computer networks were mainly confined within its facilities. The pandemic has now caused this network to “spread thin,” as the majority of employees work remotely. This has opened up a variety of new vulnerabilities, as workers’ cyber protection is not the same at home as it is in office. Although the effects of the virus are now subsiding, working remotely has embedded itself as the new normal. Thus, it is imperative for company management to take the necessary steps to ensure business continuity and be prepared to deal with an increased number of cyber threats. In our research, we provide a detailed classification for a group of tools which will facilitate risk mitigation and prevention. We also provide a selection of automated tools such as vulnerability scanners, monitoring and logging tools, and antivirus software. We outline each tool using tables, to show useful information such as advantages, disadvantages, scalability, cost, and other characteristics. Additionally, we implement decision trees for each category of tools, in an attempt to assist in navigating the large amount of information presented in this paper. Our objective is to provide a multifaceted taxonomy and analysis of mitigation tools, which will support companies in their endeavor to protect their computer networks. Our contribution can also help companies to have some type of cyber threat intelligence so as to put themselves one step ahead of cyber criminals.
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PINHEIRO, MIGUEL LINHARES, CÂNDIDA LUCAS, and JOSÉ CARLOS PINHO. "SOCIAL NETWORK ANALYSIS AS A NEW METHODOLOGICAL TOOL TO UNDERSTAND UNIVERSITY–INDUSTRY COOPERATION." International Journal of Innovation Management 19, no. 01 (January 22, 2015): 1550013. http://dx.doi.org/10.1142/s1363919615500139.
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Purpose: This work tests the use of social network analysis (SNA) as a new methodological approach to better understand university–industry (U–I) relationships in the context of research and development (R&D) cooperation networks for innovation. Methodology: Following a thorough review of the literature on U–I links from the last two decades, focusing on methodologies, SNA was applied to data on work relationships, obtained through a survey of the participants from University and Industry, engaged on a FP7 project. Findings: SNA is suggested as a useful and relevant tool to understand and examine U–I R&D cooperation at both personal and organizational levels. In support of this statement, several examples and an empirical illustration are provided. The assessment of the processes underlying the establishment and maintenance of U–I relationships within R&D cooperation with SNA suggested that interpersonal relationships are crucial for the establishment of successful cooperative activities. Unlike other tools, SNA allows the recognition of preferential relationships between institutions, and reveals asymmetries from within the U–I R&D network. Originality/value: This paper addresses the interactional dynamics embedded in U–I links. Most studies regarding U–I links focus on describing the downstream processes associated with technology transfer and commercialization. This study applies SNA to understand the ex ante establishment and maintenance of U–I relationships within R&D cooperation. The high volatility of these relationships, in view of the importance of the expected outcomes, justifies the need to understand the fundamentals of successful cooperation.
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Dekermanjian, Jonathan, Wladimir Labeikovsky, Debashis Ghosh, and Katerina Kechris. "MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools." Metabolites 11, no. 10 (October 2, 2021): 678. http://dx.doi.org/10.3390/metabo11100678.
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The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.
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Bidwell, David, and Donal Skelly. "New tools for enhanced diagnostics of DGA data." Journal of Energy - Energija 63, no. 1-4 (July 4, 2022): 141–49. http://dx.doi.org/10.37798/2014631-4172.
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In the last decade there has been a significant change in the way transformers are viewed. Their importance together with their obvious value to the network has been enhanced and recognized, especially in light of the ageing fleet worldwide. At the other end of the spectrum, new transformers are now being designed and built to tighter tolerances as a result of competitive market conditions, with the knock-on effect that these “modern” devices do not appear to provide the same stability and longevity as those that were entering service in the 1970s and 1980s. Against this backdrop, the advent of transformer monitoring has emerged and continues to develop at a rapid pace. Although still considered an emerging component of asset management practice, online DGA is rapidly gaining acceptance and recognition as one of the most powerful tools in protection against asset failures. While other transformer monitoring technologies abound, many of them now online, such as partial discharge, these products collectively combine to enable the move to condition based monitoring of transformer assets. As online DGA monitors have evolved new products and technologies are reaching the market at an ever increasing rate. However, the quiet revolution is in the analysis of the data. As more and more monitors are installed, so the burden of data analysis becomes increasingly large. New ways of extracting value from this data required. One important approach is the use of Artificial Neural Networks (ANN) for DGA data analysis. Additionally, with the recognition that data from monitors must be easily transferred into meaningful information for the end-user, diagnostic tools, such as the Duval Triangle, have evolved where the addition of Triangles 4 and 5 brings significantly more value to previously mined data. The mute question in this paper relates to whether or not existing online monitoring hardware has sufficient accuracy and repeatability of measurement to be of use with these more advanced diagnostic tools.
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Shum, Simon Buckingham, Lorella Cannavacciuolo, Anna De Liddo, Luca Iandoli, and Ivana Quinto. "Using Social Network Analysis to Support Collective Decision-Making Process." International Journal of Decision Support System Technology 3, no. 2 (April 2011): 15–31. http://dx.doi.org/10.4018/jdsst.2011040102.
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Current traditional technologies, while enabling effective knowledge sharing and accumulation, seem to be less supportive of knowledge organization, use and consensus formation, as well as of collaborative decision making process. To address these limitations and thus to better foster collective decision-making around complex and controversial problems, a new family of tools is emerging able to support more structured knowledge representations known as collaborative argument mapping tools. This paper argues that online collaborative argumentation has the rather unique feature of combining knowledge organization with social mapping and that such a combination can provide interesting insights on the social processes activated within a collaborative decision making initiative. In particular, the authors investigate how Social Network Analysis can be used for the analysis of the collective argumentation process to study the structural properties of the concepts and social networks emerging from users’ interaction. Using Cohere, an online platform designed to support collaborative argumentation, some empirical findings obtained from two use cases are presented.
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Diniz, Wellison J. S., Priyanka Banerjee, Soren P. Rodning, and Paul W. Dyce. "Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows." Animals 12, no. 19 (October 9, 2022): 2715. http://dx.doi.org/10.3390/ani12192715.
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Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.
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Fiala, Petr. "New trends in project portfolio management." Trendy v podnikání 10, no. 3 (2021): 4–11. http://dx.doi.org/10.24132/jbt.2020.10.3.4_11.
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The use of project portfolio management is increasingly becoming a tool for promoting the strategy of the organization. Using sophisticated quantitative tools becomes a significant competitive advantage for project portfolio management. Project portfolio management is a dynamic multi-criteria decision-making problem under risk. The paper presents new approaches for analyzing the problem. A dynamic version of the Analytic Network Process (ANP) captures the network, multicriteria and dynamic structure of the problem. Multicriteria decision trees analyze risk of project portfolios. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realization and outputs measure multiple criteria of goals of the organization. The Data Envelopment Analysis (DEA) is an appropriate approach to select efficient project portfolios.
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Macedo-Soares, T. Diana L. Van Aduard de. "Ensuring dynamic strategic fit of firms that compete globally in alliances and networks: proposing the Global SNA - Strategic Network Analysis - framework." Revista de Administração Pública 45, no. 1 (February 2011): 67–105. http://dx.doi.org/10.1590/s0034-76122011000100005.
Повний текст джерелаАнотація:
In order to sustain their competitive advantage in the current increasingly globalized and turbulent context, more and more firms are competing globally in alliances and networks that oblige them to adopt new managerial paradigms and tools. However, their strategic analyses rarely take into account the strategic implications of these alliances and networks, considering their global relational characteristics, admittedly because of a lack of adequate tools to do so. This paper contributes to research that seeks to fill this gap by proposing the Global Strategic Network Analysis - SNA - framework. Its purpose is to help firms that compete globally in alliances and networks to carry out their strategic assessments and decision-making with a view to ensuring dynamic strategic fit from both a global and relational perspective.
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Bazzani, Susanna. "Article Commentary: Promise and Reality in the Expanding Field of Network Interaction Analysis: Metabolic Networks." Bioinformatics and Biology Insights 8 (January 2014): BBI.S12466. http://dx.doi.org/10.4137/bbi.s12466.
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In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.
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Amin, Shahid, M. A. Rehman, Amir Naseem, Ilyas Khan, Nawa Alshammari, and Nawaf N. Hamadneh. "Analysis of Complex Networks via Some Novel Topological Indices." Mathematical Problems in Engineering 2022 (April 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/9040532.
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Chemical graph theory is a field of mathematical chemistry that links mathematics, chemistry, and graph theory to solve chemistry-related issues quantitatively. Mathematical chemistry is an area of mathematics that employs mathematical methods to tackle chemical-related problems. A graphical representation of chemical molecules, known as the molecular graph of the chemical substance, is one of these tools. A topological index (TI) is a mathematical function that assigns a numerical value to a (molecular) graph and predicts many physical, chemical, biological, thermodynamical, and structural features of that network. In this work, we calculate a new topological index namely, the Sombor index, the Super Sombor index, and its reduced version for chemical networks. We also plot our computed results to examine how they were affected by the parameters involved. This document lists the distinct degrees and degree sums of enhanced mesh network, triangular mesh network, star of silicate network, and rhenium trioxide lattice. The edge partitions of these families of networks are tabled which depend on the sum of degrees of end vertices and the sum of the degree-based edges. These edge partitions are used to find closed formulae for numerous degree-based topological indices of the networks.
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Kimanius, Dari, Liyi Dong, Grigory Sharov, Takanori Nakane, and Sjors H. W. Scheres. "New tools for automated cryo-EM single-particle analysis in RELION-4.0." Biochemical Journal 478, no. 24 (December 16, 2021): 4169–85. http://dx.doi.org/10.1042/bcj20210708.
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We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.
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Toiviainen, Hanna, Hannele Kerosuo, and Tuula Syrjälä. "“Development Radar”: the co‐configuration of a tool in a learning network." Journal of Workplace Learning 21, no. 7 (September 11, 2009): 509–24. http://dx.doi.org/10.1108/13665620910985513.
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PurposeThe paper aims to argue that new tools are needed for operating, developing and learning in work‐life networks where academic and practice knowledge are intertwined in multiple levels of and in boundary‐crossing across activities. At best, tools for learning are designed in a process of co‐configuration, as the analysis of one tool, Development Radar, aims to demonstrate.Design/methodology/approachThe “Development Radar” narrative offers a way to analyse what co‐configuration might mean in the development practices of the learning network. The data consist of the researchers' and participants' tool‐related actions in planning and running a workshop of the Forum of Workplace Development, for which Development Radar was created. Analysis draws from cultural‐historical activity theory by including cultural sources of knowledge beyond the immediate pedagogic interaction.FindingsMetaphors seem to be facilitative in the early phase of co‐configuration of a tool but not enough for sustainable workplace learning. What is needed is opening up the core concepts for all parties involved and providing ongoing negotiations and elaboration concerning their potential and meaning.Research limitations/implicationsExpansive learning is supported by co‐configuration of tools that simultaneously provide a generic orientation basis of learning and are open to contextual knowledge creation in and across the levels of developmental activities.Practical implicationsThe visual co‐configuration of tools may be crucial for understanding learning, development and the implementation of tools in a specific context, and even have an effect on the professional identity of users.Originality/validityThe significance of tools for the quality of workplace learning is generally acknowledged but the investigation into the pedagogical dynamics and material co‐configuration of tools needs more attention.
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Savareh, Behrouz Alizadeh, Hassan Emami, Mohamadreza Hajiabadi, Seyed Majid Azimi, and Mahyar Ghafoori. "Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm." Biomedical Engineering / Biomedizinische Technik 64, no. 2 (April 24, 2019): 195–205. http://dx.doi.org/10.1515/bmt-2017-0178.
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Abstract Purpose: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. Materials and methods: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Results: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Conclusion: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.
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Kowalczyk, Anna Maria, and Tomasz Bajerowski. "Development of the Theory of Six Value Aggregation Paths in Network Modeling for Spatial Analyses." ISPRS International Journal of Geo-Information 9, no. 4 (April 10, 2020): 234. http://dx.doi.org/10.3390/ijgi9040234.
Повний текст джерелаАнотація:
The dynamic development of spatial structures entails looking for new methods of spatial analysis. The aim of this article is to develop a new theory of space modeling of network structures according to six value aggregation paths: minimum and maximum value difference, minimum and maximum value decrease, and minimum and maximum value increase. The authors show how values presenting (describing) various phenomena or states in urban space can be designed as network structures. The dynamic development of spatial structures entails looking for new methods of spatial analysis. This study analyzes these networks in terms of their nature: random or scale-free. The results show that the paths of minimum and maximum value differences reveal one stage of the aggregation of those values. They generate many small network structures with a random nature. Next four value aggregation paths lead to the emergence of several levels of value aggregation and to the creation of scale-free hierarchical network structures. The models developed according to described theory present the quality of urban areas in various versions. The theory of six paths of value combination includes new measuring tools and methods which can impact quality of life and minimize costs of bad designs or space destructions. They are the proper tools for the sustainable development of urban areas.
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Sun, Jiacheng, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, and Zhenguo Li. "New Interpretations of Normalization Methods in Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5875–82. http://dx.doi.org/10.1609/aaai.v34i04.6046.
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In recent years, a variety of normalization methods have been proposed to help training neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, some necessary tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help to stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.
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Oksiiuk, Oleksandr, and Vadym Krotov. "ANALYSIS AND CHOICE OF ROUTING PROTOCOLS IN WIRELESS AD HOC NETWORKS BASED ON THE USE THE NEURAL NETWORK." Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska 9, no. 1 (March 3, 2019): 53–55. http://dx.doi.org/10.5604/01.3001.0013.0921.
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In the past few years, we have seen a rapid expansion in the field of mobile computing due to the proliferation of inexpensive, widely available wireless devices. However, current devices, applications and protocols are solely focused on cellular or wireless local area networks (WLANs), not taking into account the great potential offered by ad hoc networking. Ad hoc networks are wireless mobile networks that can operate without infrastructure and without centralized network management. In such networks, the wireless mobile nodes may dynamically enter the network as well as leave the network. Mobility and dynamic topology are the main characteristics of ad hoc networks. In the last years, the hundreds of new routing protocols were designed, that are used for the various scenarios of this design space. The routing features in wireless ad hoc networks are described. The corresponding routing protocols are reviewed. The paper proposes a method for selecting the preferred protocol wireless networks using the mathematical tools of neural networks.
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Boisvert, Jasmin, Nassir El-Jabi, Salah-Eddine El Adlouni, Daniel Caissie, and Alida Nadège Thiombiano. "New Brunswick hydrometric network analysis and rationalization." Canadian Journal of Civil Engineering 44, no. 10 (October 2017): 829–37. http://dx.doi.org/10.1139/cjce-2016-0487.
Повний текст джерелаАнотація:
The availability of hydrometric data, as well as its spatial distribution, is important for water resources management. An overly dense network or an under developed network can cause inaccurate hydrological regional estimates. The objective of this study is to propose a methodology for rationalizing a network, specifically the New Brunswick Hydrometric Network. A hierarchical clustering analysis allowed dividing the province into two regions (North and South), based on latitude and high flow timing. These groups were subsequently split separately into three homogeneous subgroups, based on the generalized extreme value (GEV) distribution shape parameter of each station for annual maximum flow series. An entropy method was then applied to compute the amount of information shared between stations, ranking each station’s importance. A station with a lot of shared information is redundant (less important), whereas one with little shared information is unique (very important). The entropy method appears to be a useful decisional tool in a network rationalization.
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Ono, Keiichiro, Tanja Muetze, Georgi Kolishovski, Paul Shannon, and Barry Demchak. "CyREST: Turbocharging Cytoscape Access for External Tools via a RESTful API." F1000Research 4 (August 5, 2015): 478. http://dx.doi.org/10.12688/f1000research.6767.1.
Повний текст джерелаАнотація:
As bioinformatic workflows become increasingly complex and involve multiple specialized tools, so does the difficulty of reliably reproducing those workflows. Cytoscape is a critical workflow component for executing network visualization, analysis, and publishing tasks, but it can be operated only manually via a point-and-click user interface. Consequently, Cytoscape-oriented tasks are laborious and often error prone, especially with multistep protocols involving many networks.In this paper, we present the new cyREST Cytoscape app and accompanying harmonization libraries. Together, they improve workflow reproducibility and researcher productivity by enabling popular languages (e.g., Python and R, JavaScript, and C#) and tools (e.g., IPython/Jupyter Notebook and RStudio) to directly define and query networks, and perform network analysis, layouts and renderings. We describe cyREST’s API and overall construction, and present Python- and R-based examples that illustrate how Cytoscape can be integrated into large scale data analysis pipelines.cyREST is available in the Cytoscape app store (http://apps.cytoscape.org) where it has been downloaded over 1900 times since its release in late 2014.
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Jerome, Jovitha, and P. Vinoth. "ARTIFICIAL INTELLIGENT SYSTEM FOR MEASUREMENT OF HARMONIC POWERS." ASEAN Journal on Science and Technology for Development 25, no. 1 (November 19, 2017): 47–59. http://dx.doi.org/10.29037/ajstd.230.
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The importance of the electric power quality (PQ) demands new methodologies and measurement tools in the power industry for the analysis and measurement of the basic electric magnitudes necessary. This paper presents a new measurement procedure based on neural networks for the estimation of harmonic amplitudes of current/voltage and respective harmonic powers. The measurement scheme is built with two neural network modules. The first module is an adaptive linear neuron (ADALINE) that is the kernel part of estimation of complex harmonic coefficients of the current/voltage. The second module is feedforward neural network that obtains the harmonic active/reactive powers. In order to perform digital simulation the Feedforward and Adaline neural network tools were developed in LabVIEW. This measurement algorithm was tested for the practical cases and found to be robust, computationally fast and efficient.
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B A Sujatha Kumari, Ankitha D V, Anupama N, Keerthana D, and Poonam K S. "Comparative Analysis of Network Parameters using Noxim Simulator." international journal of engineering technology and management sciences 6, no. 6 (November 28, 2022): 435–41. http://dx.doi.org/10.46647/ijetms.2022.v06i06.077.
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In the field of Network-on-Chip, simulation is one of the primary techniques used to examine and evaluate novel ideas. It is necessary to have simulation tools available that are frequently restricted to modelling particular network configurations in order to evaluate the performance and power figures of NoC and WiNoC systems. This article introduces Noxim, a SystemC-developed open, extendible, customizable, cycle-accurate NoC simulator that enables the analysis of the performance and power statistics of both established wired NoC and new WiNoC designs. The Noxim simulator stands out among the many simulators that can be discovered in the literature. A lot of scholars use it since it supports wifi and is open-source. Hybrid wired-wireless networks aim to combine the finest aspects of the two strategies. In this study, investigation on various optimal configurations are carried out considering various test scenarios. Different parameters like Throughput, delivered packets, delay, and battery consumption and Packet Injection Rate are considered for optimisation. The findings demonstrate that the test cases utilised only require a small number of wireless routers, enhancing the metrics desired.
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Mahfouz, Ahmed, Abdullah Abuhussein, Deepak Venugopal, and Sajjan Shiva. "Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset." Future Internet 12, no. 11 (October 26, 2020): 180. http://dx.doi.org/10.3390/fi12110180.
Повний текст джерелаАнотація:
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.