e-Xploration
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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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Rescooped by luiy from Educación a Distancia y TIC
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Abierto el plazo de matriculación gratuita para los 58 Cursos Online Masivos en Abierto (MOOC's) que ofrece Miríada X

Abierto el plazo de matriculación gratuita para los 58 Cursos Online Masivos en Abierto (MOOC's) que ofrece Miríada X | e-Xploration | Scoop.it
El proyecto Miríada X arranca con la participación de 18 universidades iberoamericanas, que impartirán 58 Cursos Online Masivos en Abierto (MOOC's).

Via L. García Aretio
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Rescooped by luiy from Digital Protest
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The Death of Aaron Swartz and the New Hacker Crackdown

The Death of Aaron Swartz and the New Hacker Crackdown | e-Xploration | Scoop.it
In 1992, the sci-fi writer Bruce Sterling published The Hacker Crackdown, a riveting nonfiction book about a string of high-profile hacker busts on the early "electronic frontier" of the late '80s and early '90s.

Via John Postill
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Introduction to Circos, Features and Uses // CIRCOS Circular Genome Data Visualization

Introduction to Circos, Features and Uses // CIRCOS Circular Genome Data Visualization | e-Xploration | Scoop.it
WHAT IS CIRCOS?CIRCULAR VISUALIZATION

Circos is a software package for visualizing data and information. It visualizes data in a circular layout — this makes Circos ideal for exploring relationships between objects or positions. There are other reasons why a circular layout is advantageous, not the least being the fact that it is attractive.

Circos is ideal for creating publication-quality infographics and illustrations with a high data-to-ink ratio, richly layered data and pleasant symmetries. You have fine control each element in the figure to tailor its focus points and detail to your audience.

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Rescooped by luiy from Digital Protest
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Fiasco global de la guerra antidescargas

Fiasco global de la guerra antidescargas | e-Xploration | Scoop.it
Es pronto para hacer un balance pleno de la llamada ley Sinde-Wert, pero el marco global permite sacar conclusiones. Pensada para agilizar los siempre

Via John Postill
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John Postill's curator insight, January 14, 2013 1:54 AM

via @bufetalmeida

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Is Facebook Making Us Lonely?

Is Facebook Making Us Lonely? | e-Xploration | Scoop.it
Social media—from Facebook to Twitter—have made us more densely networked than ever.
luiy's insight:

The new studies on loneliness are beginning to yield some surprising preliminary findings about its mechanisms. Almost every factor that one might assume affects loneliness does so only some of the time, and only under certain circumstances. People who are married are less lonely than single people, one journal article suggests, but only if their spouses are confidants. If one’s spouse is not a confidant, marriage may not decrease loneliness. A belief in God might help, or it might not, as a 1990 German study comparing levels of religious feeling and levels of loneliness discovered. Active believers who saw God as abstract and helpful rather than as a wrathful, immediate presence were less lonely. “The mere belief in God,” the researchers concluded, “was relatively independent of loneliness.”


Lanier and Turkle are right, at least in their diagnoses. Self-presentation on Facebook is continuous, intensely mediated, and possessed of a phony nonchalance that eliminates even the potential for spontaneity. (“Look how casually I threw up these three photos from the party at which I took 300 photos!”) Curating the exhibition of the self has become a 24/7 occupation. Perhaps not surprisingly, then, the Australian study “Who Uses Facebook?” found a significant correlation between Facebook use and narcissism: “Facebook users have higher levels of total narcissism, exhibitionism, and leadership than Facebook nonusers,” the study’s authors wrote. “In fact, it could be argued that Facebook specifically gratifies the narcissistic individual’s need to engage in self-promoting and superficial behavior.”

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Rescooped by luiy from Global Brain
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GraphLab - Large-Scale Machine Learning on Graphs

Designing and implementing efficient, bug free parallel and distributed algorithms can be very challenging. To address this challenge high-level data-parallel abstractions like Map-Reduce expose a simple computational pattern that isolates users form the complexities of large-scale parallel and distribute system design. Unfortunately, many important computational tasks are not inherently data-parallel and cannot be efficiently or intuitively expressed in data-parallel abstractions.

 

GraphLab is a high-level graph-parallel abstraction that efficiently and intuitively expresses computational dependencies. Unlike Map-Reduce where computation is applied to independent records, computation in GraphLab is applied to dependent records which are stored as vertices in a large distributed data-graph. Computation in GraphLab is expressed as a vertex-programs which are executed in parallel on each vertex and can interact with neighboring vertices. In contrast to the more general message passing and actor models, GraphLab constrains the interaction of vertex-programs to the graph structure enabling a wide range of system optimizations. GraphLab programs interact by directly reading the state of neighboring vertices and by modifying the state of adjacent edges. In addition, vertex-programs can signal neighboring vertex-programs causing them to be rerun at some point in the future.


Via Ashish Umre, Spaceweaver
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Gary Bamford's comment, January 17, 2013 3:20 AM
I have no idea what this means but it sounds something like how the old mechanical integration engines used to operate!?
Rescooped by luiy from Global Brain
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GraphLab - Large-Scale Machine Learning on Graphs

Designing and implementing efficient, bug free parallel and distributed algorithms can be very challenging. To address this challenge high-level data-parallel abstractions like Map-Reduce expose a simple computational pattern that isolates users form the complexities of large-scale parallel and distribute system design. Unfortunately, many important computational tasks are not inherently data-parallel and cannot be efficiently or intuitively expressed in data-parallel abstractions.

 

GraphLab is a high-level graph-parallel abstraction that efficiently and intuitively expresses computational dependencies. Unlike Map-Reduce where computation is applied to independent records, computation in GraphLab is applied to dependent records which are stored as vertices in a large distributed data-graph. Computation in GraphLab is expressed as a vertex-programs which are executed in parallel on each vertex and can interact with neighboring vertices. In contrast to the more general message passing and actor models, GraphLab constrains the interaction of vertex-programs to the graph structure enabling a wide range of system optimizations. GraphLab programs interact by directly reading the state of neighboring vertices and by modifying the state of adjacent edges. In addition, vertex-programs can signal neighboring vertex-programs causing them to be rerun at some point in the future.


Via Ashish Umre, Spaceweaver
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Gary Bamford's comment, January 17, 2013 3:20 AM
I have no idea what this means but it sounds something like how the old mechanical integration engines used to operate!?
Rescooped by luiy from Augmented Reality Hub
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Augmented Reality: Past, Present and Future - TNW Industry

Augmented Reality: Past, Present and Future - TNW Industry | e-Xploration | Scoop.it

You may have heard about augmented reality before. If you haven't, you'll be hearing a lot about it from now on, with the smartphone and tablet revolution now in full-swing.

Augmented ...


Via Aurélien Fache
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W05 : Linked City par EverydataLab - Des traces et des liens - Christophe Cariou - EverydataLab

W05 : Linked City par EverydataLab - Des traces et des liens - Christophe Cariou - EverydataLab | e-Xploration | Scoop.it
Cette semaine EverydataLab a du faire face au froid extrême, alterte de niveau 3, une partie de l’équipe n’y a pas résisté ! Le cours Economie des Médias du lundi a été consacré à discuter quelques concepts économiques.
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Rescooped by luiy from Big Data Analysis in the Clouds
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Using Big Data To Solve Social Discovery's Relevance Problem

Using Big Data To Solve Social Discovery's Relevance Problem | e-Xploration | Scoop.it
Compared to traditional social networks, social-discovery products face a unique challenge in needing to do more than simply connect folks who are already friends.

Via Pierre Levy
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Pierre Levy's curator insight, January 9, 2013 4:53 PM

Social discovery and collaborative filtering

EmilieSoleil's comment, January 17, 2013 9:52 AM
Interesting article. Does this type of social network encourage homogeneity more than other types of social network? Does this lead to the formation of exclusive networks?
Pierre Levy's comment, January 17, 2013 1:40 PM
@EmilieSoleil I don't think that it encourages homogeneity. Just the opposite: it helps people to find like-minded people instead of forcing them into prededined moulds
Rescooped by luiy from Online Relations & Community management
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Emerging new roles for learning and performance professionals « Learning in the Social Workplace

Emerging new roles for learning and performance professionals « Learning in the Social Workplace | e-Xploration | Scoop.it

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Rescooped by luiy from Media Technology Anthropology
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Concept map: sociotechnical system in an anthropology of technology – Philbu's Blog

Concept map: sociotechnical system in an anthropology of technology – Philbu's Blog | e-Xploration | Scoop.it
This concept map builds on Bryan Pfaffenberger's article...

Via Philipp Budka
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Rescooped by luiy from Social network data analysis
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Valdis Krebs: Connect on your similarities and benefit from your differencies.

Valdis Krebs: Connect on your similarities and benefit from your differencies.


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Rescooped by luiy from Anthropology, communication & technology
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Visualization in the Digital Humanities: Tool or ‘Discipline’? (Part I) | HASTAC

Visualization in the Digital Humanities: Tool or ‘Discipline’? (Part I) | HASTAC | e-Xploration | Scoop.it
Visualization in the Digital Humanities: Tool or ‘Discipline’? (Part I) http://t.co/BLuwLWhx via @zite

Via Andrea Naranjo
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Datavisualization.ch Selected Tools

Datavisualization.ch Selected Tools | e-Xploration | Scoop.it
Datavisualization.ch Selected Tools is a collection of tools that we, the people behind Datavisualization.ch, work with on a daily basis and recommend warmly.
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sigma.js | a lightweight JavaScript graph drawing library

sigma.js is an open-source lightweight JavaScript library to draw graphs, using the HTML canvas element. It has been especially designed to:

Display interactively static graphs exported from a graph visualization software - like GephiDisplay dynamically graphs that are generated on the fly
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NeoSocial: Connecting to Facebook with Neo4j

NeoSocial: Connecting to Facebook with Neo4j | e-Xploration | Scoop.it
Social applications and Graph Databases go together like peanut butter and jelly. I’m going to walk you through the steps of building an application that connects to Facebook, pulls your frie...
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GraphLab - Overview

luiy's insight:

GraphLab is BigLearning on Graphs

 

GraphLab is a graph-based, high performance, distributed computation framework written in C++.  While GraphLab was originally developed for Machine Learning tasks, it has found great success at a broad range of other data-mining tasks; out-performing other abstractions by orders of magnitude.

 

GraphLab Features:

 

A unified multicore and distributed API: write once run efficiently in both shared and distributed memory systemsTuned for performance: optimized C++ execution engine leverages extensive multi-threading and asynchronous IOScalable: GraphLab intelligently places data and computation using sophisticated new algorithmsHDFS Integration: Access your data directly from HDFSPowerful Machine Learning Toolkits: Turn BigData into actionable knowledge with easeGraphLab is Cutting Edge Research

 

GraphLab is the culmination of 4 years of research and development into graph computation, distributed computing, and machine learning. GraphLab scales to graphs with billions of vertices and edges easily, performing orders of magnitude faster than competing systems. GraphLab combines advances in machine learning algorithms, asynchronous distributed graph computation, prioritized scheduling, and graph placement with optimized low-level system design and efficient data-structures to achieve unmatched performance and scalability in challenging machine learning tasks.

 

Not only are we pushing the envelope of large-scale graph computation and BigLearning, we are also exploring the limits of small-scale systems for BigData.  With the new GraphChi project we are enabling a single desktop computer (actually a Mac Mini) to tackle problems that previously demanded an entire cluster.

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Intuition and Big Data: Value of Human Insight

Intuition and Big Data: Value of Human Insight | e-Xploration | Scoop.it

Some of the best uses of advanced databases or data visualizations is in narrowing down what might be thousands or millions of variables into something that can (be) assessed by a person and then acted on. ~ Stacey Gigginbotham, Gigaom

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Rescooped by luiy from Augmented Reality Hub
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Réalité Augmentée : Histoire, usages et perspectives

Une infographie sur la Réalité Augmentée découverte grâce à un tweet de Jean-François Ktichiguine son inspirateur et fondateur du forum ARMTL qui rassemble experts et passionnés de la Réalité Augmentée de tout profil : développeurs, Designer Graphique, marketeurs, entrepreneurs, ...


Via Aurélien Fache
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Analyzing a large user community using the social network analysis framework | Mitacs – Inspiring innovation in Canada

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Deux visualisations présentées au Showroom de Cap’Com - Des traces et des liens - Christophe Cariou - EverydataLab

Deux visualisations présentées au Showroom de Cap’Com - Des traces et des liens - Christophe Cariou - EverydataLab | e-Xploration | Scoop.it
Aujourd'hui, sur l'invitation de Hugues Aubin (merci), nous étions au Showroom de Cap'Com à l'Hôtel de Rennes Métropole pour présenter deux de nos projets récents aux professionnels de la communication des territoires français.
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Colloquium d'informatique UPMC : Stuart Russell

Colloquium d'informatique UPMC : Stuart Russell | e-Xploration | Scoop.it
UPMC Colloquium - Stuart Russell
Unifying logic and probability: A “New Dawn” for Artificial Intelligence? http://t.co/CRt1bzI5
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Big Data to Overtake Hadoop in 2013

Big Data to Overtake Hadoop in 2013 | e-Xploration | Scoop.it
The Big Data catch phrase is set to overtake Hadoop in 2013 for Google searches. (Big Data to Overtake Hadoop in 2013 http://t.co/MiFlu72n)
luiy's insight:

By using Google Trends graphing I have taken a look at the popularity of the phrase “Big Data” in web searches and related terms.  Hadoop and its related technology such as MapReduce have helped create this interest in alternative lower cost technology stacks for storing and analysing very large amounts of unstructured or structured data - especially web based data.  Traditional software vendors like IBM and Oracle jumped on the bandwagon under the banner of “Big Data” – that lets them promote the advantages of Hadoop but also defines a more complete enterprise architecture with databases, data streaming and analytics software.

 

Google Trend reports show that “Big Data” is approaching the popularity of “Hadoop” and is already a more popular search term than “Cognos”.  It is most often searched in South Korea and India.   

 

Big Data Search term popularity is shown in this Google Trends Graph covering 2009 to the start of 2013.  It shows Big Data climbing quickly in 2012 and about to overtake Hadoop in 2013.  Big Data started as a marketing phrase to describe technology that handled data in very large volumes and/or with very large complexity. 

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Rescooped by luiy from Media Technology Anthropology
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Alexander Knorr's webpage on cyberanthropology

Alexander Knorr's webpage on cyberanthropology | e-Xploration | Scoop.it

Introduction to cyberanthropology...


Via Philipp Budka
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