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Diablo III Datamining Application Demonstration Video 2
 
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Second update to our new tool, titled "Deckard's Data Dump". Questions, comments, etc. can be directed to us at [email protected] Keep an eye out for us on diablo.incgamers!
Views: 9272 Jom Darbert
Basics of Social Network Analysis
 
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Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 34440 Alexandra Ott
DATA MINING   1 Data Visualization   3 1 3  Graph Visualization
 
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https://www.coursera.org/learn/datavisualization
Views: 158 Ryo Eng
"Artificial Intelligence with Bayesian Networks" with Dr. Lionel Jouffe
 
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Title: Artificial Intelligence with Bayesian Networks - Data Mining, Knowledge Modeling and Causal Analysis Speaker: Dr. Lionel Jouffe Date: 1/12/2018 Abstract: Probabilistic models based on directed acyclic graphs have a long and rich tradition, beginning with the work of geneticist Sewall Wright in the 1920s. Variants have appeared in many fields. Within Statistics, such models are known as directed graphical models; within Cognitive Science and Artificial Intelligence, such models are known as Bayesian Networks (BNs), a term coined in 1985 by UCLA Professor Judea Pearl to honor the Rev. Thomas Bayes (1702-1761), whose rule for updating probabilities in the light of new evidence is the foundation of the approach. BNs provide an elegant and sound approach to represent uncertainty and to carry out rigorous probabilistic inference by propagating the pieces of evidence gathered on a subset of variables on the remaining variables. BNs are not only effective for representing expert’s belief, uncertain knowledge and vague linguistic representations of knowledge via an intuitive graphical representation, but are also a powerful Knowledge Discovery tool when associated with Machine Learning/Data Mining techniques. In 2004, the MIT Press of Technology (Massachusetts Institute of Technology) classified Bayesian Machine Learning at the 4th rank among the “10 Emerging Technologies That Will Change Your World”. Most recently, Judea Pearl, the father of BNs, received the 2012 ACM A.M. Turing Award, the most prestigious award in Computer Science, widely considered the "Nobel Prize in Computer Science," for contributions that transformed Artificial Intelligence, especially for the development of the theoretical foundations for reasoning under uncertainty using BNs. Over the last 25 years, BNs have then emerged as a practically feasible form of knowledge representation and as a new comprehensive data analysis framework. With the ever-increasing computing power, their computational efficiency and inherently visual structure make them attractive for exploring and explaining complex problems. BNs are now a powerful tool for deep understanding of very complex and high-dimensional problem domains. Deep understanding means knowing, not merely how things behaved yesterday, but also how things will behave under new hypothetical circumstances tomorrow. More specifically, a BN allows explicit reasoning, and deliberate reasoning to allow the anticipation of the consequences of actions that have not yet been taken. We will use this 45-minute tutorial for describing what BN are, how we can design these probabilistic expert systems by expertise and how we can use data to automatically machine-learn these models. SPEAKER Dr. Lionel Jouffe, Co-founder and CEO of Bayesia S.A.S. Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has been working in the field of Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development BayesiaLab, which has since emerged as a leading software package for knowledge discovery, data mining and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities as well as in business and industry. MODERATOR Plamen Petrov, Director of Cognitive Technology, KPMG LLP; SIGAI Industry Liaison Officer MODERATOR Rose Paradis, Data Scientist at Leidos Health and Life Sciences; SIGAI Secretary/Treasurer
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 7 of this video series includes specific coverage of: - The trade-offs of expanding the text analytics feature space with n-grams. - How bag-of-words representations map to the vector space model (VSM). - Usage of the dot product between document vectors as a proxy for correlation. - Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. - How LSA is implemented using singular value decomposition (SVD). - Mapping new data into the lower dimensional SVD space. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JVc0 See what our past attendees are saying here: https://hubs.ly/H0f5K6Q0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 9028 Data Science Dojo
Scalable Learning of Graphical Models (Part 1)
 
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Authors: Geoff Webb, Faculty of Information Technology, Monash University François Petitjean, Faculty of Information Technology, Monash University Abstract: From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling. This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 193 KDD2016 video
Qualitative and Quantitative research in hindi  | HMI series
 
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For full course:https://goo.gl/J9Fgo7 HMI notes form : https://goo.gl/forms/W81y9DtAJGModoZF3 Topic wise: HMI(human machine interaction):https://goo.gl/bdZVyu 3 level of processing:https://goo.gl/YDyj1K Fundamental principle of interaction:https://goo.gl/xCqzoL Norman Seven stages of action : https://goo.gl/vdrVFC Human Centric Design : https://goo.gl/Pfikhf Goal directed Design : https://goo.gl/yUtifk Qualitative and Quantitative research:https://goo.gl/a3izUE Interview Techniques for Qualitative Research :https://goo.gl/AYQHhF Gestalt Principles : https://goo.gl/Jto36p GUI ( Graphical user interface ) Full concept : https://goo.gl/2oWqgN Advantages and Disadvantages of Graphical System (GUI) : https://goo.gl/HxiSjR Design an KIOSK:https://goo.gl/Z1eizX Design mobile app and portal sum:https://goo.gl/6nF3UK whatsapp: 7038604912
Views: 46161 Last moment tuitions
Graph Mining and Analysis  Lecture_4
 
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Graph Mining and Analysis Lecture_4 18 December 2015
Footprinting a Target Using Maltego
 
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Maltego is an interactive data mining tool that renders directed graphs for link analysis. The tool is used in online investigations for finding relationships between pieces of information from various sources located on the Internet.
Views: 77 mumenkm
BigDataX: Random graph and scale-free graph models
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Visual Exploration of Market Basket Analysis with JMP
 
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Association rules are a popular data mining technique for exploring relationships in databases. These rules use a variety of algorithms and attempt to identify strong rules or associations among variables. One example is the classic market basket case, which finds that when bread and cheese are purchased, wine is more often purchased. Rules can also easily serve as supervised learning algorithms by directing that one element be a target variable of interest. JMP does not include association rule methods -- but does offer connectivity and flexibility, in addition to great interactive visualization tools. This presentation, by Matthew Flynn, PhD, Marketing Manager at Aetna, demonstrates that strength by connecting JMP to other software tools -- such as SAS® Enterprise Miner™, open-source R, Weka and (now with JMP 11) MATLAB -- to access association rules methods and enliven them by visually exploring the generated rule results in JMP. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 2665 JMPSoftwareFromSAS
Data Analysis for Social Scientists | MITx on edX | Course About Video
 
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Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. Take this course free on edX: https://www.edx.org/course/data-analysis-social-scientists-mitx-14-310x#! ABOUT THIS COURSE This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses. This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively. WHAT YOU'LL LEARN - Intuition behind probability and statistical analysis - How to summarize and describe data - A basic understanding of various methods of evaluating social programs - How to present results in a compelling and truthful way - Skills and tools for using R for data analysis
Views: 7112 edX
Stanford Seminar - Topological Data Analysis: How Ayasdi used TDA to Solve Complex Problems
 
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"Topological Data Analysis: How Ayasdi used TDA to Solve Complex Problems" -Anthony Bak, Ayasdi Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5
Views: 12606 stanfordonline
Large scale geospatial data analysis through efficient supervised machine learning
 
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The present thesis aims to test the viability of the integration of machine learning capabilities into web map servers. The validation of this hypothesis has been carried out by the development of a pre-operational prototype. The developed prototype is a platform for thematic mapping by supervised learning from very high resolution remote sensing imagery data through a web platform. This contribution overcomes the current state of art, characterized by the separation of the two areas, which requires a continuous involvement of remote sensing experts in thematic mapping intensive tasks: labour intensive tasks are supplemented by the integration of the scalability capabilities from machine learning engines and web map servers. With this hypothesis the application field referred to the semi-automatic creation of large scale thematic maps can open up different fields, from agriculture to the environmental monitoring field, to expert users of these applications domains with limited specific knowledge of remote sensing techniques. Semantic tagging algorithms based on supervised classification methods can be exploited for thematic map creation from raster data based on user needs. This requires the integration of machine learning capabilities within web map servers, along with a simple interface that enables navigation and the monitoring of geospatial learning. The adaptive nature of this learning, along with its integration into a web server, requires a classification algorithm characterized by efficient management and processing of data in time scales compatible with traditional web browsing. At the same time, the volume of data managed by remote sensing applications motivates the transfer of the developed methodology to cloud environments under the Big Data paradigm. Ph.D. work developed by Dr. Javier Lozano in Vicomtech-IK4 and presented at the University of the Basque Country. Directed by: Dr. Ekaitz Zulueta and Dr. Marco Quartulli. More information: [email protected]
Views: 611 Vicomtech
12. Greedy Algorithms: Minimum Spanning Tree
 
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MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Erik Demaine In this lecture, Professor Demaine introduces greedy algorithms, which make locally-best choices without regards to the future. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 72666 MIT OpenCourseWare
Improved Code Clone Categorization
 
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Google Tech Talk June 24, 2010 Presented by Dr. Nicholas A. Kraft. ABSTRACT Because 50% to 90% of developer effort during software maintenance is spent on program comprehension activities, techniques and tools that can reduce the effort spent by developers on these activities are required to reduce maintenance costs. One characteristic of a software system that can adversely affect its comprehensibility is the presence of similar or identical segments of code, or code clones. To promote developer awareness of the existence of code clones in a system, researchers recently have directed much attention to the problem of detecting these clones; these researchers have developed techniques and tools for clone detection and have discovered that significant portions of popular software systems such as the Linux kernel are cloned code. However, knowledge of the existence of clones is not sufficient to allow a developer to perform maintenance tasks correctly and completely in the presence of clones. Proper performance of these tasks requires a deep and complete understanding of the relationships among the clones in a system. Thus, new techniques and tools that will assist developers in the analysis of large numbers of clones are a critical need. In this talk I will describe preliminary work on code clone categorization that I am leading at The University of Alabama. In particular, I will describe the development of techniques and tools for categorization of code clones using structural and semantic properties of the clones. Specific research outcomes that we are working towards include: (1) a suite of metrics for measuring the congruence and complementarity of a number of static program representations that capture structural properties of the clones, (2) a process to categorize code clones based on these metrics, and (3) serial and integrated processes that combine structural categorization of code clones and semantic categorization of code clones. Bio: Nicholas A. Kraft is an assistant professor in the Department of Computer Science at The University of Alabama. He received his Ph.D. in computer science from the School of Computing at Clemson University. His research interests are in software engineering and languages, particularly source-code based reverse engineering techniques and tools for software understanding and maintenance. He has published on these topics in IEEE Transactions on Software Engineering, Science of Computer Programming, Information and Software Technology, and the Journal of Systems and Software. His current work is supported by four grants from the National Science Foundation. He has served on the program committees of conferences such as the International Conference on Program Comprehension and the International Conference on Software Language Engineering.
Views: 4635 GoogleTechTalks
Learning Representations of Large-scale Networks part 1
 
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Authors: Qiaozhu Mei, Department of Electrical Engineering and Computer Science, University of Michigan Jian Tang, Montreal Institute for Learning Algorithms (MILA), University of Montreal Abstract: Large-scale networks such as social networks, citation networks, the World Wide Web, and traffic networks are ubiquitous in the real world. Networks can also be constructed from text, time series, behavior logs, and many other types of data. Mining network data attracts increasing attention in academia and industry, covers a variety of applications, and influences the methodology of mining many types of data. A prerequisite to network mining is to find an effective representation of networks, which largely determines the performance of downstream data mining tasks. Traditionally, networks are usually represented as adjacency matrices, which suffer from data sparsity and high-dimensionality. Recently, there is a fast-growing interest in learning continuous and low-dimensional representations of networks. This is a challenging problem for multiple reasons: (1) networks data (nodes and edges) are sparse, discrete, and globally interactive; (2) real-world networks are very large, usually containing millions of nodes and billions of edges; and (3) real-world networks are heterogeneous. Edges can be directed, undirected or weighted, and both nodes and edges may carry different semantics. In this tutorial, we will introduce the recent progress on learning continuous and low-dimensional representations of large-scale networks. This includes methods that learn the embeddings of nodes, methods that learn representations of larger graph structures (e.g., an entire network), and methods that layout very large networks on extremely low (2D or 3D) dimensional spaces. We will introduce methods for learning different types of node representations: representations that can be used as features for node classification, community detection, link prediction, and network visualization. We will introduce end-to-end methods that learn the representation of the entire graph structure through directly optimizing tasks such as information cascade prediction, chemical compound classification, and protein structure classification, using deep neural networks. We will highlight open source implementations of these techniques. Link to tutorial: https://sites.google.com/site/pkujiantang/home/kdd17-tutorial More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 248 KDD2017 video
Verification: Truth in Statistics by Tennessee Leeuwenburg
 
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Come to this talk if you want to learn a few basic techniques for putting numerical data in context. If you've ever predicted anything, or tried to work out whether some number was "good enough", you'll probably get something out of this presentation. All techniques and tools demonstrated using Python. Every day, decisions both big and small are made on the basis of the information published by the Bureau of Meteorology. These include simple decisions such as taking an umbrella or planning a barbecue. Our forecasts also inform Australia's emergency services on where extreme weather events may have occurred, to help with planning and preparation. Understanding and communicating our strengths and weaknesses is very important, both as an organisation and also internally within the Environment and Research division. This presentation will focus on the statistical methods and systems used to evaluate the objective, scientific performance of our forecast systems. The name for this area of study is "Verification". While the concepts have come from the research environment, they are widely applicable and can help anyone who is assessing the performance of any system. This presentation will include: -- An overview of the major ideas of verification -- How to create a 'skill score' -- The application of these concepts to thunderstorm forecasting -- How to use Python tools for verification analyses -- Tips on how to apply these ideas easily in other contexts Obtaining relevant thunderstorm observational data can be particularly challenging, particularly pertaining to severe and damaging aspects: lightning, hail, heavy rain and very strong wind gusts. In order achieve a stronger footing, some new methods of analysis are under development. It is necessary to establish the scientific validity of the verification metrics at the same time as constructing the systems to support the data analysis. A prototype web-based tool written in Python (and under active development by the presenter) will be demonstrated. This tool can run locally to provide an enhanced lab environment for assessing case study data, or be set up as a server for continuous monitoring and reporting. No pre-existing knowledge of Python or statistics is assumed. The talk will include several technical aspects, such as working at different computing scales, usability and user experience, working with statistical algorithms, data visualisation for both web and journal publications, and the architectural challenges of a complex application. PyCon Australia is the national conference for users of the Python Programming Language. In August 2014, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World. August 1-5, Brisbane, Queensland, Australia
Views: 1128 PyCon Australia
"A Systems Approach to Data Privacy in the Biomedical Domain" (CRCS Lunch Seminar)
 
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CRCS Privacy and Security Lunch Seminar (Wednesday, May 13, 2009) Speaker: Bradley Malin Title: A Systems Approach to Data Privacy in the Biomedical Domain Abstract: The healthcare community has made considerable strides in the development and deployment of information systems, with particular gains in electronic health records and cheap genome sequencing technologies. Given the recent passage of the American Recovery and Reinvestment Act of 2009, and the HITECH Act in particular, advancement and adoption of such systems is expected to grow at unprecedented rates. The quantity of patient-level data that will be generated is substantial and can enable more cost-effective care as well as support a host of secondary uses, such biomedical research and biosurveillance. At the same time, care must be taken to ensure that such records are accessed and shared without violating a patient's privacy rights. The construction and application of data privacy technologies in the biomedical domain is a complex endeavor and requires the resolution of often competing computational, organizational, regulatory, and scientific needs. In this talk, I will introduce how the Vanderbilt Health Information Privacy Laboratory builds and applies data privacy solutions to support various biomedical settings. Our solutions are rooted in computational formalisms, but are driven by real world requirements and, as such, draw upon various tools and techniques from a number of fields, including cryptography, databases and data mining, public policy, risk analysis, and statistics. Beyond a high-level overview, I will delve into recent research on how we are measuring and mitigating privacy risks when sharing patient-level data from electronic medical and genomic records from the Vanderbilt University Medical Center to local researchers and an emerging de-identified repository at the National Institutes of Health. Bio: Brad Malin is an Assistant Professor of Biomedical Informatics in the School of Medicine and an Assistant Professor of Computer Science in the School of Engineering at Vanderbilt University. He is the founder and director of the Vanderbilt Health Information Privacy Laboratory (HIPLab), which focuses on basic and applied research in a number of health-related areas, including primary care and secondary sharing of patient-specific clinical and genomic data. His research has received several awards of distinction from the American and International Medical Informatics Associations and the HIPLab is currently supported by grant funding from the National Science Foundation, National Institutes of Health, and Veterans Health Administration. For the past several years, he has directed a data privacy research and consultation team for the Electronic Medical Records and Genomics (eMERGE) project, a consortium sponsored by the National Human Genome Research Institute. He has served as a program committee member and workshop chair for numerous conferences on data mining, privacy, and medical informatics. He has also edited several volumes for Springer Lecture Notes in Computer Science, a special issue for the journal Data and Knowledge Engineering, and is currently on the editorial board of the journal Transactions on Data Privacy. He received a bachelor's in biology (2000), master's in knowledge discovery and data mining (2002), master's in public policy & management (2003), and a doctorate in computation, organizations & society (2006) from the School of Computer Science at Carnegie Mellon University. His home on the web can be found at http://www.hiplab.org/people/malin
Views: 200 Harvard's CRCS
2 Text Mining pt 1
 
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Views: 225 InsiderMiner
Andreas Weigend interviewed at Strata Jumpstart 2011
 
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Andreas Weigend (PhD Physics 1991, Stanford University; Diplom Physik, Philosophie 1986, Universitaet Bonn) is an Associate Professor of Information Systems at the Stern School of Business, New York University. He came to Stern from the University of Colorado at Boulder where he had founded the Time Series Analysis Group, after working at Xerox PARC (Palo Alto Research Center) on knowledge discovery. At the Santa Fe Institute, he co-directed the Time Series Prediction Competition that led to the volume Time Series Prediction: Forecasting the Future and Understanding the Past (1994, Addison Wesley). His research focuses on basic methodologies for modeling and extracting knowledge from data and their application across different disciplines. He develops and integrates ideas and analytical tools from statistics and information theory with neural networks and other machine learning paradigms. His approach, basic science on real problems, emphasizes the importance of rigorous evaluations of new methods in data mining. His recent work uses computational intelligence to extract and understand hidden states in financial markets, and to exploit this information to improve density predictions. He has published about one hundred articles in scientific journals, books and conference proceedings. He co-edited four books including Decision Technologies for Financial Engineering (1998, World Scientific). Prof. Weigend received a Research Initiation Award by the National Science Foundation (NSF), a major grant by the Air Force Office of Scientific Research (AFOSR), a Junior Faculty Development Award by the University of Colorado and a NYU Curricular Development Challenge Grant for his innovative course Data Mining in Finance. This course covers the foundations of knowledge discovery, data mining, prediction and nonlinear modeling, as well as specific techniques including neural networks, graphical models, evolutionary programming and clustering techniques. It develops solutions to current problems in finance and includes integrated in-depth projects with major Wall Street firms. Prof. Weigend organized the sixth international conference Computational Finance that took place at Stern on January 6-8, 1999, drawing more than 300 attendees. He has given tutorials and short executive courses on time series analysis, volatility prediction, nonlinear modeling, risk management and decision making under uncertainty and consulted for a broad spectrum of firms ranging from financial boutiques to Goldman Sachs, J. P. Morgan, Morgan Stanley and Nikko Securities.
Views: 490 O'Reilly
Dense Subgraph Discovery - Part 2
 
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Authors: Aristides Gionis, Charalampos E. Tsourakakis Abstract: Finding dense subgraphs is a fundamental graph-theoretic problem, that lies in the heart of numerous graph-mining applications, ranging from finding communities in social networks, to detecting regulatory motifs in DNA, and to identifying real-time stories in news. The problem of finding dense subgraphs has been studied extensively in theoretical computer science, and recently, due to the relevance of the problem in real-world applications, it has attracted considerable attention in the data-mining community. In this tutorial we aim to provide a comprehensive overview of (i) major algorithmic techniques for finding dense subgraphs in large graphs and (ii) graph mining applications that rely on dense subgraph extraction. We will present fundamental concepts and algorithms that date back to 80's, as well as the latest advances in the area, from theoretical and from practical point-of-view. We will motivate the problem of finding dense subgraphs by discussing how it can be used in real-world applications. We will discuss different density definitions and the complexity of the corresponding optimization problems. We will also present efficient algorithms for different density measures and under different computational models. Specifically, we will focus on scalable streaming, distributed and MapReduce algorithms. Finally we will discuss problem variants, extensions, and will provide pointers for future research directions. ACM DL: http://dl.acm.org/citation.cfm?id=2789987 DOI: http://dx.doi.org/10.1145/2783258.2789987
Exploiting Undefined Behaviors for Efficient Symbolic Execution
 
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Symbolic execution is an important and popular technique used in several software engineering tools for test case generation, debugging and program analysis. As such improving the performance of symbolic execution can have huge impact on the effectiveness of such tools. In this paper, we present a technique to systematically introduce undefined behaviors during compilation to speed up the subsequent symbolic execution of the program. We have implemented our technique inside LLVM and tested with an existing symbolic execution engine (Pathgrind). Preliminary results on the SIR repository benchmark are encouraging and show 48% speed up in time and 30% reduction in the number of constraints. This is teaser video for the following publication by Asankhaya Sharma. http://www.comp.nus.edu.sg/~asankhs/pdf/Exploiting_Undefined_Behaviors_for_Efficient_Symbolic_Execution.pdf
Views: 691 asankhs
1.5 Perturbation Analysis and Machine Learning
 
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Unit 1, Module 5 Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems---From Networks to Cells by Hector Zenil and Narsis A. Kiani Algorithmic Dynamics Lab www.algorithmicdynamics.net
Views: 958 Complexity Explorer
The Basics of Data Classification
 
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The Basics of Data Classification training session with Michele Robinson, California Department of Technology. OIS Training Resources Link https://cdt.ca.gov/security/resources/#Training-Resources
Views: 3662 californiacio
Social Media Analysis Using Optimized K-Means Clustering
 
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Social Media Analysis Using Optimized K-Means Clustering -IEEE PROJECTS 2017-2018 MICANS INFOTECH PVT LTD, CHENNAI ,PONDICHERRY http://www.micansinfotech.com http://www.finalyearprojectsonline.com http://www.softwareprojectscode.com +91 90036 28940 ; +91 94435 11725 ; [email protected] Download [email protected] http://www.micansinfotech.com/VIDEOS.html Abstract: The increasing influence of social media and enormous participation of users creates new opportunities to study human social behavior along with the capability to analyze large amount of data streams. One of the interesting problems is to distinguish between different kinds of users, for example users who are leaders and introduce new issues and discussions on social media. Furthermore, positive or negative attitudes can also be inferred from those discussions. Such problems require a formal interpretation of social media logs and unit of information that can spread from person to person through the social network. Once the social media data such as user messages are parsed and network relationships are identified, data mining techniques can be applied to group different types of communities. However, the appropriate granularity of user communities and their behavior is hardly captured by existing methods. In this paper, we present a framework for the novel task of detecting communities by clustering messages from large streams of social data. Our framework uses K-Means clustering algorithm along with Genetic algorithm and Optimized Cluster Distance (OCD) method to cluster data. The goal of our proposed framework is twofold that is to overcome the problem of general K-Means for choosing best initial centroids using Genetic algorithm, as well as to maximize the distance between clusters by pairwise clustering using OCD to get an accurate clusters. We used various cluster validation metrics to evaluate the performance of our algorithm. The analysis shows that the proposed method gives better clustering results and provides a novel use-case of grouping user communities based on their activities. Our approach is optimized and scalable for real-time clustering of social media data.
Address Parse Tool-Part1
 
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View other tools, view the tool's .pdf guide, or download the tool from the link here: http://www.sco.wisc.edu/parcels/tools/ The Address Parsing Tool parses a full site address field into individual elements. It will parse any address contained as a field in a file geodatabase feature class into individual elements that fit the FGDC’s United States Thoroughfare, Landmark, and Postal Address Data Standard. The tool, which is run in several cycles, is designed to accomplish as much automatic parsing as feasible. However, for some addresses, the workflow may require altering an input site address or manual parsing. This video will describe common address structure and methods to optimize address parse tool results. Questions or comments may be directed to: Codie See - [email protected] David Vogel - [email protected] Address Parse Tool - Part 1: Overview of the Tool and Addresses - https://www.youtube.com/watch?v=FJrH7TvJqPY Address Parse Tool - Part 2: Installing the Tool - https://www.youtube.com/watch?v=LP3C-1BKDs4 Address Parse Tool - Part 3: Configure and Run First Parse - https://www.youtube.com/watch?v=A8ZI-pohub4 Address Parse Tool - Part 4: Further Parsing - https://www.youtube.com/watch?v=oQ_AFR3UsXQ Address Parse Tool - Part 5: Final Parse - https://www.youtube.com/watch?v=v3hWxZXuiXY
Advanced Business Intelligence Techniques 14: Force-based graph layout experiments
 
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Advanced Business Intelligence Techniques 14th lesson by professor Mauro Brunato at University of Trento.
Views: 140 ReactiveSearch
September Event 2018 — Apple
 
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Apple Special Event. September 12, 2018. Big news all around. Take a look at the all-new Apple Watch Series 4, iPhone XS and iPhone XS Max, and iPhone XR. 7:48 — Apple Watch Series 4 Learn more at https://apple.co/2p4OlCZ 38:46 — iPhone XS 43:08 — iPhone XS Max Learn more at https://apple.co/2x7ApN9 1:26:26 — iPhone XR Learn more at https://apple.co/2QsyzOQ
Views: 2014731 Apple
Returns in the Data Binding Process - Data Visualization and D3.js
 
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This video is part of an online course, Data Visualization and D3.js. Check out the course here: https://www.udacity.com/course/ud507. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 616 Udacity
Dense Subgraph Discovery - Part 1
 
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Authors: Aristides Gionis, Charalampos E. Tsourakakis Abstract: Finding dense subgraphs is a fundamental graph-theoretic problem, that lies in the heart of numerous graph-mining applications, ranging from finding communities in social networks, to detecting regulatory motifs in DNA, and to identifying real-time stories in news. The problem of finding dense subgraphs has been studied extensively in theoretical computer science, and recently, due to the relevance of the problem in real-world applications, it has attracted considerable attention in the data-mining community. In this tutorial we aim to provide a comprehensive overview of (i) major algorithmic techniques for finding dense subgraphs in large graphs and (ii) graph mining applications that rely on dense subgraph extraction. We will present fundamental concepts and algorithms that date back to 80's, as well as the latest advances in the area, from theoretical and from practical point-of-view. We will motivate the problem of finding dense subgraphs by discussing how it can be used in real-world applications. We will discuss different density definitions and the complexity of the corresponding optimization problems. We will also present efficient algorithms for different density measures and under different computational models. Specifically, we will focus on scalable streaming, distributed and MapReduce algorithms. Finally we will discuss problem variants, extensions, and will provide pointers for future research directions. ACM DL: http://dl.acm.org/citation.cfm?id=2789987 DOI: http://dx.doi.org/10.1145/2783258.2789987
Meetup: Brendan Madden on Graph Visualization and Analysis
 
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Brendan Madden, Chief Executive Officer, Tom Sawyer Software Learn about various advanced graph visualization, layout, and analysis techniques developed over many years of practical experience. Brendan also discusses some of the software engineering challenges that we face in this era. Among the important features, Brendan demonstrates viewing, editing, and interaction methods. Further, he shows various scalable, incremental, and constraint-based layout algorithms and their usage in various industries, and demonstrates problems and solutions in graph analysis. In addition, Brendan also discusses data representation, federated data integration, model management, rules, filters, and synchronized views of data in both desktop and web architectures. To attend future Meetups, join Tom Sawyer Software's Berkeley Graph Visualization group: http://www.meetup.com/Berkeley-Graph-Visualization/
Views: 554 Tom Sawyer Software
qdap for R: The check_spelling function
 
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See https://github.com/trinker/qdap for more
Views: 2178 Tyler Rinker
What is Text Analytics?
 
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ttp://www.ibm.com/software/data/bigdata/ Big Data Text Analytics defined in 3 minutes with Rafael Coss, manager Big Data Enablement for IBM. This is number twelve and the final episode in this series of 'What is...' videos. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
Views: 7533 IBM Analytics
Concolic Testing of High-Level Languages, Konstantinos Sagonas
 
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Concolic testing is a software testing technique that combines concrete execution of a program unit with symbolic execution in an attempt to generate inputs that explore all the paths in this unit. So far, concolic testing has been applied, mainly at the level of bytecode or assembly code, to programs written in imperative languages that manipulate primitive data types such as integers and arrays. In this talk, we will describe how concolic testing can be applicable to high-level languages in general and to functional programming languages in particular. At this high level, the concolic engine needs to efficiently support pattern matching, recursive data types such as lists, recursion and higher-order functions. The talk will also include a short demo of CutEr (as in "more cute"), a concolic testing tool for Erlang, and our experiences from its use so far. Kostis Sagonas is an Associate Professor at the the Department of Information Technology of Uppsala University. His research interests are in programming language and software technology. His Ph.D. thesis work was in the implementation of tabling in logic programming. In the last fifteen years he has been heaviily involved in the design and implementation of the concurrent functional language Erlang, having made significant contributions both to the evolution of the language itself (in particular, he has designed its language of types and specs and bitstring pattern matching), its runtime system and virtual machine, its native code compiler (HiPE, ErLLVM), and to static analysis and transformation tools for the language (Dialyzer, TypEr and Tidier). In the last few years he has been working on developing novel techniques and tools for testing concurrent programs (Concuerror, PropEr, CutEr, Nifty, Target and Nidhugg), and the ArgoDSM system for scalable software distributed shared memory.
Views: 463 RISE SICS
Running Large Graph Algorithms: Evaluation of Current State-Of-the-Art and Lessons Learned
 
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Google Tech Talk February 11, 2010 ABSTRACT Presented by Dr. Andy Yoo, Lawrence Livermore National Laboratory. Graphs have gained a lot of attention in recent years and have been a focal point in many emerging disciplines such as web mining, computational biology, social network analysis, and national security, just to name a few. These so-called scale-free graphs in the real world have very complex structure and their sizes already have reached unprecedented scale. Furthermore, most of the popular graph algorithms are computationally very expensive, making scalable graph analysis even more challenging. To scale these graph algorithms, which have different run-time characteristics and resource requirements than traditional scientific and engineering applications, we may have to adopt vastly different computing techniques than the current state-of-art. In this talk, I will discuss some of the findings from our studies on the performance and scalability of graph algorithms on various computing environments at LLNL, hoping to shed some light on the challenges in scaling large graph algorithms. Andy Yoo is a computer scientist in the Center for Applied Scientific Computing (CASC). His current research interests are scalable graph algorithms, high performance computing, large-scale data management, and performance evaluation. He has worked on the large graph problems since 2004. In 2005, he developed a scalable graph search algorithm and demonstrated it by searching a graph with billions of edges on IBM BlueGene/L, then the largest and fastest supercomputer. Andy was nominated for 2005 Gordon Bell award for this work. He is currently working on finding right combination of architecture, systems, and programming model to run large graph algorithms. Andy earned his Ph.D. degree in Computer Science and Engineering from the Pennsylvania State University in 1998. He joined LLNL in 1998. Andy is a member of the ACM, IEEE and the IEEE Computer Society, and SIAM.
Views: 19029 GoogleTechTalks
Data mining- Clustering based on Expectation-Maximization (EM) algorithm- PASS MSC PROJECTS
 
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Ph: 0452 4243340; Mobile: 9840992340; http://pandianss.com Pandian Systems and Solutions Pvt Ltd 2nd Floor, No 393 Annanagar Main Road Indian Bank Complex Madurai - 625020, Tamil Nadu India E-Mail: [email protected], [email protected]
Views: 199 Pass Tutors
Behavior Research in AIS - Current Topics in Auditing - Spring 2018 - Professor Vasarhelyi
 
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Current Topics In Auditing Lecture 9 Professor Vasarhelyi March 20, 2018 Please visit our website at http://raw.rutgers.edu Time Stamps: Topic 1: The Impact of Initial Information Ambiguity on the Accuracy of Analytical Review Judgments. 0:04:15 Background & Research 0:04:53 Concepts 0:07:18 Hypotheses 0:07:59 Experiments 0:09:51 Results 0:11:43 Additional Test 0:18:45 Conclusion & Contributions Topic 2: Attention-Directing Analytical Review Using Accounting Ratios: A Case Study 0:26:19 Attention-Directing Analytical Review 0:27:05 Background 0:27:53 Case Study Method 0:29:01 The Case Firm 0:29:48 Often Used Ratios 0:30:07 Types of Errors 0:30:45 Errors’ Effects 0:31:20 Investigating Rules 0:34:31 Conclusions Topic 3: Clustering Based Peer Selection with Financial Ratios 0:48:17 Research Objectives and Contributions 0:49:28 Motivation 0:52:53 Existing Classification 0:55:09 Why base clustering on financial ratios? 0:57:47 Research Methodology 1:14:53 How are Comparisons Made Across Classification Schemes? 1:17:57 SIC vs Clustering - Within Group Dispersion 1:20:38 Comparison of Adjusted R-Squared 1:21:08 Conclusion Topic 4: Adding an Accounting Layer to Deep Neural Network: 1:29:34 Financial Statements Fraud and Earnings Management 1:30:10 Fraud Detection, Data Mining, and Audit Analytics 1:30:31 Framework Topic 5: A Field Study on the Use of Process Mining of Event Logs as an Analytical Procedure in Auditing 1:59:23 Introduction 2:00:58 Protocol for Applying Process Mining 2:04:28 Field Study Site 2:05:38 Identify the Designed Process 2:07:28 Preliminary Analysis of the Event Log 2:09:29 Process Discovery 2:16:26 Role Analysis 2:20:27 Verification by Attribute Analysis 2:29:18 Social Network Analysis 2:30:02 Research Implications of Process Mining 2:30:31 Conclusion Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subscribe.html
Project Cybercrime
 
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Views: 1126 akwaaba holland
Mobile Visual Analytics Law Enforcement Toolkit (iVALET) Features
 
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The Mobile Visual Analytics Law Enforcement Toolkit (iVALET) is a suite of advanced visual analytic tools for enhanced exploration and analysis of criminal, traffic, and civil incidence reports on the iPad and the iPhone.This video highlights the different features of the Mobile Visual Analytics Law Enforcement Toolkit (iVALET). For more information, please visit: http://www.purdue.edu/discoverypark/vaccine/
Views: 236 PurdueVACCINE
Data Mining   Preprocessing Missing Value
 
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Sudah tahu kah kamu bagaimana cara mem-pre-processing data menggunakan aplikasi rapidminer? Yuk disimak. Semoga membantu. Anggota kelompok: Indra, Sofyan, Haga, Alberi, Emli --------------------------------------------------------------------------------- Directed and editted by M. Sofyan Bahrum Juniardi Thanks for watching! Thanks also if you wanna subrscribe.
Views: 237 Sofyan Bahroem
PRACTICE: Outside In | Inside Out
 
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This symposium considers discourse on contemporary issues of design practice in two parts: the external pressures of economic, environmental, and political systems, and internal forces of tools, techniques, and strategies for design. Addressing the multifaceted nature of the profession, we will explore themes for the design of practice, such as work and labor, tools and technology, and ethics and agency. The symposium highlights potential avenues for the growth and constitution of practice, as well as the issues currently at stake within the profession. The following discussions confront pressing questions regarding the shifting responsibilities of design practice, and the future of practice itself. This symposium is generously sponsored by the Carl M. Sapers Ethics in Practice Fund, and co-hosted by the GSD Practice Platform and the Department of Architecture. Panelists: Aaron Cayer, Neena Verma, Jesse Keenan, Alison Brooks, Eduard Sancho Pou, Sawako Kaijima, Randy Deutsch, Robert Pietrusko Moderators: Mark Lee, Grace La
Views: 1585 Harvard GSD
Graphs visualization. Fruchterman-Reingold algorithm and radial algorithm
 
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Prezentacja dwóch algorytmów wizualizacji grafów: Fruchtermana-Reingolda i radialnego. Presentation of two graphs visualization algorithms: Fruchterman-Reingold and radial.
Views: 295 Piotr Pilny
Glenn Roe: Distant Readings
 
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The challenge of ‘Big Data’ in the humanities has led in recent years to a host of innovative technological and algorithmic approaches to the growing digital human record. These techniques—from data mining to distant reading—can offer students and scholars new perspectives on the exploration and visualisation of increasingly intractable data sets in both the human and social sciences; perspectives that would have previously been unimaginable. The danger, however, in these kinds of ‘macro-analyses’, is that researchers find themselves ever more disconnected from the raw materials of their research, engaging with massive collections of texts in ways that are neither intuitive nor transparent, and that provide few opportunities to apply traditional modes of close reading to these new resources. In this talk, I will outline some of my previous work using data mining and machine learning techniques to explore large-scale data sets drawn primarily from the French Enlightenment period. And, building upon these past experiences, I will then present several of my current research projects, which use sequence alignment algorithms to identify intertextual relationships between authors and texts in the 18th-century ‘Republic of Letters’. By reintroducing the notion of (inter)textuality into algorithmic and data-driven methods of analysis we can move towards bridging the gap between distant and close readings, by way of an intermediary mode of scholarship I term ‘directed’ or ‘scalable’ reading. Glenn's current research agenda is primarily located at the intersection of new computational approaches with traditional literary and historical research questions. Drawing from diverse domains such as intellectual history, the history of ideas, literary theory, book history, and digital humanities, Glenn is chiefly interested in the idea of ‘intertextuality’ as it pertains to various editorial, authorial, and critical practices over the longue durée.
Police "Real-Time" communications for Intelligence Led Policing...The PoliceArch ICP™
 
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The PoliceArch ICP™ allows agencies to communicate real-time criminal and administrative data. The PoliceArch™ utilizes real time data in its robust analytic search tools to ensure you have current data at your finger tips. Communicate with the citizens you serve the region surrounding you and/or officers within your agency, all on a secure law enforcement platform. The PoliceArch ICP™ provides officer safety notifications on person, vehicles, locations and associates based the "real-time" threats they face!
Views: 486 PoliceArch
Understanding The Book Of Life (1/3) - Cracking The Code episode 7
 
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http://www.medicinema.com/genetics.html Medicinema Ltd. From the award winning science teaching series CRACKING THE CODE: The Continuing Saga of Genetics - 9 half hour films that cover the history and basic concepts of genetics - from Aristotle and Mendel through to the new DNA-based world of genomics and GM crops - in a fresh and entertaining style. Featuring original songs by Moxy Früvous Produced, written and directed by Jack Micay Major sponsor - The National Science Foundation Episode 7 - Understanding The Book Of Life Reading the Book of Life was just the first step. The ultimate goal is to understand how it works. We are guided towards this new genetic horizon by Francis Collins and Craig Venter, the leaders of the two competing teams that first sequenced the human genome. The initial task is to separate out the genes from the other 98% or so of the genome that doesnt code for proteins, no easy feat since the genes themselves are split into even smaller bits (exons), which are also surrounded by DNA noise. Three different gene finding techniques are explained. One method uses an RNA message to tag the gene that produced it. Another makes use of the codons that act as start and stop signals for the machinery of transcription. Still another method exploits the striking similarity between many of our genes and those of other creatures. The next task is to work out the function of the proteins produced by these genes. Since many different proteins can be derived from the same gene, this a daunting long term project. Protein function is studied using experimental techniques such as site-directed mutagenesis, which is explained by its inventor, Canadian Nobel laureate Michael Smith. The holy grail of genomics is to program computers to predict the function of a protein from the sequence of its gene - still a distant goal. Another challenge will be to work out which genes act together in networks to produce a complex trait. A key tool in uncovering these networks is the gene chip, which is explained in a visual, easy to understand way. Small variations in our DNA play a crucial role in disease. The most important human diseases are caused by combinations of variant genes, interacting with environmental and lifestyle factors. These variant networks are far more difficult to track down than the single mutations that cause classic genetic diseases like cystic fibrosis. One way around this problem is to study isolated populations with a high incidence of a particular disease. One such group is the Cochin Jews of Israel, who suffer from a very high rate of asthma. The end result will be a new kind of medicine, based on genetic testing and prevention rather than after- the-fact diagnosis and treatment. This episode also features John Sulston, Eric Lander, Sydney Brenner and Joshua Lederberg. 30 minutes.
Views: 20881 jackmicay
What is CONCOLIC TESTING? What does CONCOLIC TESTING mean? CONCOLIC TESTING meaning & explanation
 
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What is CONCOLIC TESTING? What does CONCOLIC TESTING mean? CONCOLIC TESTING meaning - CONCOLIC TESTING definition - CONCOLIC TESTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Concolic testing (a portmanteau of concrete and symbolic) is a hybrid software verification technique that performs symbolic execution, a classical technique that treats program variables as symbolic variables, along a concrete execution (testing on particular inputs) path. Symbolic execution is used in conjunction with an automated theorem prover or constraint solver based on constraint logic programming to generate new concrete inputs (test cases) with the aim of maximizing code coverage. Its main focus is finding bugs in real-world software, rather than demonstrating program correctness. A description and discussion of the concept was introduced in "DART: Directed Automated Random Testing" by Patrice Godefroid, Nils Klurland, and Koushik Sen. The paper "CUTE: A concolic unit testing engine for C", by Koushik Sen, Darko Marinov, and Gul Agha, further extended the idea to data structures, and first coined the term concolic testing. Another tool, called EGT (renamed to EXE and later improved and renamed to KLEE), based on similar ideas was independently developed by Cristian Cadar and Dawson Engler in 2005, and published in 2005 and 2006. PathCrawler first proposed to perform symbolic execution along a concrete execution path, but unlike concolic testing PathCrawler does not simplify complex symbolic constraints using concrete values. These tools (DART and CUTE, EXE) applied concolic testing to unit testing of C programs and concolic testing was originally conceived as a white box improvement upon established random testing methodologies. The technique was later generalized to testing multithreaded Java programs with jCUTE, and unit testing programs from their executable codes (tool OSMOSE). It was also combined with fuzz testing and extended to detect exploitable security issues in large-scale x86 binaries by Microsoft Research's SAGE. The concolic approach is also applicable to model checking. In a concolic model checker, the model checker traverses states of the model representing the software being checked, while storing both a concrete state and a symbolic state. The symbolic state is used for checking properties on the software, while the concrete state is used to avoid reaching unreachable state. One such tool is ExpliSAT by Sharon Barner, Cindy Eisner, Ziv Glazberg, Daniel Kroening and Ishai Rabinovitz.
Views: 400 The Audiopedia
Visualizing a network
 
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This is a little project of mine, a visualization of the wireless network at my library, although it could visualize any network. It's programmed in Java and for the graphics I used Processing, for the Physics Toxiclibs and for the network access jnetpcap. The colour of the nodes are determined by their MAC address and the size of the node represents the packets per second. Those circles around a node come up, when a broadcast is sent. You can get the souce code under https://bitbucket.org/madsen953/ethervisu for the Ethernet part and https://bitbucket.org/madsen953/jgv for the visualization part. Feel free to give feedback.
Views: 1539 Kristian Lange

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