The Friend of a Friend (FOAF) project is creating a Web of machine-readable pages describing people, the links between them and the things they create and do. FOAF is about your place in the Web, and the Web's place in our world. FOAF is a simple technolo
The goal of the JEM thematic network is to pool together the required expertise and to contribute to the coordination of content enrichment activities in the area of mathematics, to the maintenance of agreed standards and to the delivery of powerful synoptic high-quality user information and support pages, invoked in e-learning platforms operated by the partners.
JavaNNS is the successor of SNNS. It is based on its computing kernel, with a newly developed, comfortable graphical user interface written in Java set on top of it. Hence the compatibility with SNNS is achieved, while the platform-independence is increa
OpenNMS is the world's first enterprise grade network management platform developed under the open source model. It consists of a community supported open-source project as well as a commercial services, training, and support organization.
The Complex Systems Registry is an Open Source federative platform that has been developed within the FET coordination action Open Network of Centres of Excellence in Complex Systems (ONCE-CS) within the framework of the Complex Systems Society.
The aims of this initiative are
* to become the main Information Hub for the Complex Systems Community,
* to offer pre-installed Web facilities to all institutions, projects, working groups, ... of the Complex Systems Community,
* to guarantee the inter-operability between all installed Web sites.
In early discussions within the Open Sustainability Network, it was agreed that we didn’t want Yet Another Website. So we use existing resources: We didn’t set up a separate wiki, instead using Appropedia.* We didn’t set up a new social network, instead using an existing, like-minded community of people doing serious sustainability and knowledge-sharing work: Global Swadeshi. When someone suggested that OSN should be building a knowledge base, Lonny Grafman expressed that this is a job some of us are passionate about (indeed, that’s what Appropedia is doing) - but it’s not the role of OSN. OSN is for supporting and connecting these initiatives.
This project, funded for two years starting September 2008 by the NSF Cyber-enabled Discovery and Innovation (CDI) Program will develop a suite of tools and services to encourage formation of virtual organizations in scientific communities of various sizes, such as conference groups and departmental research groups, and allow such organizations to filter out relevant documents from various input streams, select and enhance the quality of bibliographic data associated with the organization, and attract students, teachers and researchers to contribute to activity of the organization.
Graph mining refers to extracting knowledge from massive graphs. The data sets of telephone calls we see at AT&T can be viewed as a single graph, with several hundred million phone numbers as nodes, and calls between phone numbers as edges. It is a giant social network, like an internet connections graph or a rich citation network.
The Datagram Congestion Control Protocol (DCCP) is a transport protocol that provides bidirectional unicast connections of congestion-controlled unreliable datagrams. DCCP is suitable for applications that transfer fairly large amounts of data and that can benefit from control over the tradeoff between timeliness and reliability.
My advisor is Prof. Ben Shneiderman, and I am a member of HCIL. My research is in the Information Visualization area of the Human-Computer Interaction field. My current research involves Network Visualization. I developed a tool called NVSS (see NVSS project page) to visualize netwoks (citation networks, food webs, social networks, etc.) using semantic substrates.
H. TARIQ, W. YANG, I. HAMEED, B. AHMED, и R. KHAN. IJIRIS:: International Journal of Innovative Research Journal in Information Security, Volume IV (Issue XII):
01-07(декабря 2017)1 Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. “Bayesian CART Model Search.” Journal of the American Statistical Association, Vol. 93(443), pp 935–948, September 1998. 2 Sujata Garera, Niels Provos, Monica Chew, and Aviel D. Rubin. “A framework for detection and measurement of phishing attacks.” In Proceedings of the 2007 ACM workshop on Recurring malicious code - WORM ’07, page 1, 2007. 3 Abhishek Gattani, AnHai Doan, Digvijay S. Lamba, NikeshGarera, Mitul Tiwari, Xiaoyong Chai, Sanjib Das, Sri Subramaniam, AnandRajaraman, and VenkyHarinarayan. “Entity extraction, linking, classifica- tion, and tagging for social media.” Proceedings of the VLDB Endowment, Vol. 6(11), pp 1126–1137, August 2013. 4 David D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. pages 4–15. 1998. 5 Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. “Learning to detect malicious URLs.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), pp 1–24, April 2011. 6 FadiThabtah Maher Aburrous, M.A.Hossain, KeshavDahal. “Intelligent phishing detection system for e-banking using fuzzy data mining.” Expert Systems with Applications, Vol. 37(12), pp 7913–7921, Dec 2010. 7 AnkushMeshram and Christian Haas. “Anomaly Detection in Industrial. Networks using Machine Learning: A Roadmap.” In Machine Learning for Cyber Physical Systems, pages 65–72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2017. 8 Xuequn Wang Nik Thompson,Tanya Jane McGill. “Security begins at home: Determinants of home computer and mobile device security behavior.” Computers & Security, Vol. 70, pp 376–391, Sep 2017. 9 Dan Steinberg and Phillip Colla. “CART: Classification and Regression Trees.” The Top Ten Algorithms in Data Mining, pp 179–201, 2009. 10 D. Teal. “Information security techniques including detection, interdiction and/or mitigation of memory injection attacks,” Google patents. Oct 2013. 11 Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. “Design and Evaluation of a Real-Time URL Spam Filtering Service.” In 2011 IEEE Symposium on Security and Privacy, pp 447–462. May 2011. 12 Sean Whalen, Nathaniel Boggs, and Salvatore J. Stolfo. “Model Aggregation for Distributed Content Anomaly Detection.” In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec ’14, pp 61–71, New York, USA, 2014. ACM Press. 13 Ying Yang and Geoffrey I. Webb. “Discretization for Naive-Bayes learning: managing a discretization bias and variance.” Machine Learning, Vol. 74(1), pp 39–74, Jan 2009..