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The History of #Philosophy, from 600 B.C.E. to 1935, Visualized in Two Massive, 44-Foot High Diagrams

The History of #Philosophy, from 600 B.C.E. to 1935, Visualized in Two Massive, 44-Foot High Diagrams | e-Xploration | Scoop.it
The history of philosophy tends to get mightily abbreviated. The few philosophy professors I know don’t have much truck with generalist “history of ideas”-type projects, and the discipline itself encourages, nay, requires, intensive specialization.

Via Rui Guimarães Lima
luiy's insight:

The history of philosophy tends to get mightily abbreviated. The few philosophy professors I know don’t have much truck with generalist “history of ideas”-type projects, and the discipline itself encourages, nay, requires, intensive specialization. Add to this glib comments like Alfred North Whitehead’s on philosophy as a “series of footnotes to Plato,” and the eminent position of the erratic and comparatively philosophically-unschooled autodidact Wittgenstein, and you have, in modern philosophy, a sad neglect of the genealogy of thought.

But take heart, you who, like me, incline toward minor figures and obscure relationships. Ohio State professor of philosophy Kevin Scharp is a Linnaean taxonomist of thought, compiling charts, “Information Boxes,” and hand-drawn diagrams of the “Sociology of Philosophy,” like that above, which covers Western philosophy from 600 B.C.E. to 600 C.E. and shows the myriad complex connections between hundreds of individual philosophers and schools of thought (such as Stoicism, Skepticism, Neo-Platonism, etc.). The second massive diagram covers 600 C.E. to about 1935. Each one is about 4 feet wide and 44 feet tall, with the text at 12-pont font. Both diagrams are based on Sociology of Philosophies by Randall Collins. See more of Professor Scharp’s diagrams here.

Note: to see the diagrams in detail, you will need to click the links above, and then click again on the images that appear on the new web page.

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Clustering Datasets by Complex Networks Analysis | #datascience #complexity

Clustering Datasets by Complex Networks Analysis | #datascience #complexity | e-Xploration | Scoop.it

This paper proposes a method based on complex networks analysis, devised to perform clustering on multidimensional datasets. In particular, the method maps the elements of the dataset in hand to a weighted network according to the similarity that holds among data. Network weights are computed by transforming the Euclidean distances measured between data according to a Gaussian model. Notably, this model depends on a parameter that controls the shape of the actual functions. Running the Gaussian transformation with different values of the parameter allows to perform multiresolution analysis, which gives important information about the number of clusters expected to be optimal or suboptimal.

 

Clustering datasets by complex networks analysis
Giuliano Armano and Marco Alberto Javarone

Complex Adaptive Systems Modeling 2013, 1:5 http://dx.doi.org/10.1186/2194-3206-1-5


Via Complexity Digest
luiy's insight:

The proposed method, called DAN (standing for Datasets as Networks), makes a step forward in the direction of investigating the possibility of using complex network analysis as a proper machine learning tool. The remainder of the paper is structured as follows: Section Methods describes how to model a dataset as complex network and gives details about multiresolution analysis. For the sake of readability, the section briefly recalls also some informative notion about the adopted community detection algorithm. Section Results and discussion illustrates the experiments and analyzes the corresponding results. The section recalls also some relevant notions of clustering, including two well‐known algorithms, used therein for the sake of comparison. Conclusions (i.e. Section Conclusions) end the paper.

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Lisa Barrett: Facing Down Ekman’s Universal Emotions | #Neuroanthropology

Lisa Barrett: Facing Down Ekman’s Universal Emotions | #Neuroanthropology | e-Xploration | Scoop.it
luiy's insight:

Boston Magazine has a fantastic profile of the work by psychologist Lisa Barrett that takes on Paul Ekman’s theories of universal emotion types, with corresponding facial expressions. The article is About Face: New Theory – Emotions and Facial Expressions Not Directly Related.

First excerpt:

“Honestly, this is going to sound terrible,” Lisa Barrett told me when I asked her about Ekman and his original study. “But at first, when I read that work, I thought, Well, nobody can take this seriously. This can’t possibly be right. It’s too cartoonish.”

Barrett is a professor of psychology at Northeastern, and for years she’s been troubled by Ekman’s ideas. People don’t display and recognize emotions in universal ways, she believes, and emotions themselves don’t have their own places in the brain or their own patterns in the body. Instead, her research has led her to conclude that each of us constructs them in our own individual ways, from a diversity of sources: our internal sensations, our reactions to the environments we live in, our ever-evolving bodies of experience and learning, our cultures.

This may seem like nothing more than a semantic distinction. But it’s not. It’s a paradigm shift that has put Barrett on the front lines of one of the fiercest debates in the study of emotion today, because if Barrett is correct, we’ll need to rethink how we interpret mental illness, how we understand the mind and self, and even what psychology as a whole should become in the 21st century.

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Modizy : shopping --> algorithme et intelligence collective | #algorithms

Modizy : shopping --> algorithme et intelligence collective | #algorithms | e-Xploration | Scoop.it
Question pour Benoit Feron co-fondateur de Modizy (incubateur startup42.org) Pouvez vous nous parler de Modizy ? Tout est parti de la technologie choisie.
luiy's insight:

Modizy se différencie de tous ces concurrents grâce à son algorithme de personnalisation unique fonctionnant sur l'intelligence collective, par la place centrale de son espace shopping, son positionnement et surtout son expérience-utilisateur addictive. Le coeur de métier de Modizy est d'offrir une expérience d'achat personnalisée et ludique en fonction des goûts de l'utilisateur, tout en profitant de recommandations sociales pertinentes. La plupart de nos concurrents utilisent uniquement les suggestions manuelles des utilisateurs pour créer des recommandations sociales. Il est alors très difficile de faire participer les internautes et il faut une audience considérable pour animer le site. Un site de social shopping se fonde entièrement sur les recommandations sociales: si personne ne donne son avis ou interagit d'une quelconque façon, le site est inutile.

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How To Implant a Memory? | #neuroscience

How To Implant a Memory? | #neuroscience | e-Xploration | Scoop.it
The movie Inception is getting closer to reality. By planting false memories into the minds of mice, neuroscientists at MIT have created the first artificially implanted memories.
luiy's insight:
How To Implant a Memory

In a study published in the latest issue of Science, a team of researchers led by MIT neuroscientist and Nobel Laureate Susumu Tonegawa demonstrates its ability to isolate and activate engrams in a mouse's memory-rich hippocampus. The researchers go on to implant false memories in the mouse's mind, causing it to recall experiences that have never actually occurred. Here's how they did it.

First, Tonegawa and his team genetically engineered mice capable of expressing a protein called Channelrhodopsin-2 (ChR2). Importantly, the protein was expressed exclusively in the hippocampus, and only in neurons involved in memory formation. This allowed Tonegawa and his team to effectively label only the brain cells encoding for a specific engram. Place a mouse in a safe environment (Chamber A, the blue box above), and the brain cells encoding for the memory of this environment express ChR2 (the white dots).

 

 

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The Anatomy of the Facebook Social Graph | #socialmedia #dataviz

luiy's insight:

We study the structure of the social graph of active Facebook users, the largest social network everanalyzed. We compute numerous features of the graph including the number of users and friendships, thedegree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network
is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the ‘six degrees of separation’ phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense
structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which ‘your friends have more friends than you’. Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.

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Your Body and Big Data --> The "smart_toillet"

Your Body and Big Data --> The "smart_toillet" | e-Xploration | Scoop.it
Granted this data collecting toilet has been around for a few years, but with the emphasis on data collection of every kind become the norm, it only seems natural to revisit this invention, as well as similar technologies, and the benefits they...
luiy's insight:

Favorite data collector?  A toilet!  From the people that brought you the don’t burn yourself with yournoodle soup head and neck protector, the handy dandy who needs a pack of tissues in your pocket when you can keep it on your head device, and the I wish I could see inside my ear, Hey I can!: You can now get data output on your own output. Granted this data collecting toilet has been around for a few years, but with the emphasis on data collection of every kind become the norm, it only seems natural to revisit this invention, as well as similar technologies, and the benefits they present.  

This commode collects vitals such as BMI and blood sugar from the user.  It then alerts him/her to any discovered irregularities.  Meaning, it collects and stores your data, and shows you what’s wrong. 

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Network Analysis of Science #Crowdfunding - Wired | #dataviz #SNA

Network Analysis of Science #Crowdfunding - Wired | #dataviz #SNA | e-Xploration | Scoop.it
luiy's insight:

Readers will remember when I announced Ethan Perlstein‘s plan to crowdfund his scientific research. Well, since then, Ethan has been combining two of my interests: alternative ways of funding science and network science. In his attempt to achieve his goal of raising $25,000, Ethan has been attempting to understand what conditions and connections yield the most money. And network analysis is one component of this.

Some of his analyses have looked at the statistical properties of the donations so far, confirming that donations do not come in at a constant rate (there is often a burst in the beginning and end, with some stagnation in the middle). In addition, Ethan recently emailed me an analysis based on his Facebook friends, and who donated and who did not....

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Theories of Learning: #behaviorism #constructiorism #cognitivism #connectivism

Theories of Learning: #behaviorism #constructiorism #cognitivism #connectivism | e-Xploration | Scoop.it

Via Viktor Markowski, Complexity Digest, Spaceweaver
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Ivon Prefontaine's curator insight, February 10, 2014 2:13 PM

We treat social constructivism as if it is new. Dewey and Montessori wrote about it over a century ago although they did not call it constructivism. The idea of using digital technologies and social media add a new twist to old ideas and it is important to inquire into what that means.

Helen Teague's curator insight, February 11, 2014 1:03 PM

nicely succinct infographic on learning theories

Tom Short's curator insight, February 12, 2014 7:58 PM

Nice overview of various learning theories; positioned against some new thinking about Networked learning theory.

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Distributed Intelligence In Design ( PDF ) Download All You Want ...

Distributed Intelligence In Design ( PDF ) Download All You Want ... | e-Xploration | Scoop.it
Tuba Kocatirk, Dr. Benachir Medjdoub, "Distributed Intelligence In Design Filehost Mirrors: Uploaded.net, Rapidgator.net " English | ISBN: 1444333380 | February 8, 2011 | 280 page.

Via Spaceweaver
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Vintage Data Visualization: 35 examples from before the Digital Era | #dataviz #history

Vintage Data Visualization: 35 examples from before the Digital Era | #dataviz #history | e-Xploration | Scoop.it

Graphics, charts, diagrams and visual data representations have been published on books, newspapers and magazines since they've existed, not to mention old maps and scientific illustrations...

 

Despite the lack of tools such as the ones we have at our disposal nowadays, they are as inspiring and important as the best contemporary visualizations. Visit the article link for a gallery of vintage visualizations...


Via Lauren Moss, Jim Lerman
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Mariana Soffer's comment, July 20, 2013 9:39 AM
my pleasure
Charlley Luz's curator insight, July 20, 2013 10:26 AM

muito legal, os Infográficos antes de existir a internet. 35 exemplos de infográficos no papel :) Achei falta do Marcha para Moscou do Minard http://www.datavis.ca/gallery/re-minard.php ;

Leoncio Lopez-Ocon's curator insight, July 20, 2013 2:57 PM

El brasileño Tiago Veloso, fundador de Visual Loop, nos ofrece 35 interesantísimas representaciones visuales de distintos fenómenos y eventos que permiten hacer un paseo por la historia de la ilustración científica.

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History Of Social Media - Infographic | #cyberculture

History Of Social Media - Infographic | #cyberculture | e-Xploration | Scoop.it

Do you know the history of social media? Think we'll remember Facebook in 20 years? This detailed timeline is a must-see.

 

Social media began decades before the Facebook era. It started, more or less, with CompuServe and Arpanet back in 1969. A couple years later, the first-ever email was sent.

It has evolved over the past few decades into a powerful tool, as seen in this social media history timeline. With so much that’s happened over the past few decades, we can only guess what’s coming next for social media.


Via Lauren Moss, Pascale Mousset
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Charles Rein's comment, July 24, 2013 3:38 PM
From the land of "Wired Telephones" USA, we can now look at how explosive Global growth and the potential 5-7 billion people who will always use a Smart or Cell device
Eleonora Guglielman's curator insight, August 1, 2013 9:18 AM

Nice infographic

lbligen's curator insight, August 4, 2013 5:44 AM

Always new background information.

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Tour the World's Webcams With the Search Engine for the Internet of Things | #surveillance #public

Tour the World's Webcams With the Search Engine for the Internet of Things | #surveillance #public | e-Xploration | Scoop.it
THE INTERNET OF THINGS Shodan A map of the world's publicly available webcams. Image: Shodan
luiy's insight:

When Dan Tentler wants to find something on the internet, he doesn’t use Google or Bing. Tentler, a freelance security consultant, is a road-less-traveled kind of guy. He likes to check out the internet’s alleyways and backroads. And for people like him him, there’s only one search engine. It’s called Shodan.

 

Google has done a masterful job of indexing the human experience — the webpages, books, Word documents, and images and videos that make up our life. But Shodan looks for something simpler. It’s looking for all the stuff that’s connected to the internet, from routers and refrigerators to live webcams that give you a glimpse inside people’s homes to, well, who knows what.

 

These odd little devices, overlooked by Google and Bing, are the things that Tentler finds interesting. Using Shodan, he’s taken a tour of a Scottish country house, explored a stationary GPS receiver in Alaska, and even examined the control panel for a swimming pool. “It’s like looking at a street or a set of the buildings, but not from the front,” he says. “Not from where their marketing department wants you to see it. But from where the shipping and receiving department uses it.”

 

Using a network of 24 computers nested in service providers across the world, Shodan reaches out and methodically probes machines across the globe asking them the simplest of questions: What can you tell me about yourself? And you’d be surprised what it has found.....

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#NSA: THE DECISION PROBLEM | Edge.org | #surveillance #privacy

#NSA: THE DECISION PROBLEM | Edge.org |  #surveillance #privacy | e-Xploration | Scoop.it

Via Pierre Levy
luiy's insight:

GEORGE DYSON, Science Historian, is the author of Turing's Cathedral: The Origins of the Digital Universe, and Darwin Among the Machines.

 

The Corona program, a joint venture between the CIA, the NSA, and the Department of Defense, was coordinated by the Advanced Research Projects Agency (ARPA) and continued, under absolute secrecy, for 12 more years and 126 more missions, becoming the most productive intelligence operation of the Cold War. "It was as if an enormous floodlight had been turned on in a darkened warehouse," observed former CIA program director Albert D. Wheelon, after the operation was declassified by order of President Clinton in 1995. "The Corona data quickly assumed the decisive role that the Enigma intercepts had played in World War II."

The resources and expertise that were gathered to support the Corona program, operating under cover of a number of companies and institutions centered around Sunnyvale, California (including Fairchild, Lockheed, and the Stanford Industrial Park) helped produce the Silicon Valley of today. Google Earth is Corona's direct descendant, and it is a fact as remarkable as the fall of the Berlin wall that anyone, anywhere in the world, can freely access satellite imagery whose very existence was a closely guarded secret only a generation ago.

PRISM, on the contrary, has been kept in the dark. Setting aside the question of whether wholesale, indiscriminate data collection is legal—which, evidently, its proponents believed it was—the presumed reason is that for a surveillance system to be effective against bad actors, the bad actors have to be unaware that they are being watched. Unfortunately, the bad actors to be most worried about are the ones who suspect that they are being watched. The tradecraft goes way back. With the privacy of houses came eavesdropping; with the advent of written communication came secret opening of mail; with the advent of the electric telegraph came secret wiretaps; with the advent of photography came spy cameras; with the advent of orbital rocketry came spy satellites. To effectively spy on the entire Internet you need your own secret Internet—and Edward Snowden has now given us a glimpse into how this was done.

The ultimate goal of signals intelligence and analysis is to learn not only what is being said, and what is being done, but what is being thought. With the proliferation of search engines that directly track the links between individual human minds and the words, images, and ideas that both characterize and increasingly constitute their thoughts, this goal appears within reach at last. "But, how can the machine know what I think?" you ask. It does not need to know what you think—no more than one person ever really knows what another person thinks. A reasonable guess at what you are thinking is good enough.

Data mining, on the scale now practiced by Google and the NSA, is the realization of what Alan Turing was getting at, in 1939, when he wondered "how far it is possible to eliminate intuition, and leave only ingenuity," in postulating what he termed an "Oracle Machine." He had already convinced himself of the possibility of what we now call artificial intelligence (in his more precise terms, mechanical intelligence) and was curious as to whether intuition could be similarly reduced to a mechanical procedure—although it might (indeed should) involve non-deterministic steps. He assumed, for sake of argument, that "we do not mind how much ingenuity is required, and therefore assume it to be available in unlimited supply." 


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Social interactions dominate speed control in driving natural flocks toward criticality | #Complexity #entropy

Flocks of birds exhibit a remarkable degree of coordination and collective response. It is not just that thousands of individuals fly, on average, in the same direction and at the same speed, but that even the fluctuations around the mean velocity are correlated over long distances. Quantitative measurements on flocks of starlings, in particular, show that these fluctuations are scale-free, with effective correlation lengths proportional to the linear size of the flock. Here we construct models for the joint distribution of velocities in the flock that reproduce the observed local correlations between individuals and their neighbors, as well as the variance of flight speeds across individuals, but otherwise have as little structure as possible. These minimally structured, or maximum entropy models provide quantitative, parameter-free predictions for the spread of correlations throughout the flock, and these are in excellent agreement with the data. These models are mathematically equivalent to statistical physics models for ordering in magnets, and the correct prediction of scale-free correlations arises because the parameters - completely determined by the data - are in the critical regime. In biological terms, criticality allows the flock to achieve maximal correlation across long distances with limited speed fluctuations.

Social interactions dominate speed control in driving natural flocks toward criticality
William Bialek, Andrea Cavagna, Irene Giardina, Thierry Mora, Oliver Pohl, Edmondo Silvestri, Massimiliano Viale, Aleksandra Walczak

http://arxiv.org/abs/1307.5563


Via Complexity Digest
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Amazing mind reader reveals his 'gift' | #NSA #privacy #surveillance

Dave is an extremely gifted clairvoyant who finds out specific financial information. This video reveals the magic behind the magic, making people aware of t...
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FTC to Study Privacy Impact of Data Brokers | #privacy #databrokers

FTC to Study Privacy Impact of Data Brokers | #privacy #databrokers | e-Xploration | Scoop.it
The FTC is seeking details about the nature and sources of the data collected, how companies use and disseminate the information.
luiy's insight:

US regulators Tuesday ordered data brokers to turn over information about how they collect and use information about consumers, in a move hailed by Internet privacy activists.

The US Federal Trade Commission said nine firms were ordered to hand over information that will be used to study privacy practices in the data broker industry.

The move drew immediate praise from the Center for Digital Democracy, a Washington-based group which monitors online privacy.

"Today's action by the FTC will unmask this largely stealth consumer surveillance industry," the group said on its blog.

"It will shine a powerful regulatory spotlight on such disturbing practices... Our data is sold to the highest commercial bidder in milliseconds, who can use the information for almost any purpose -- yet it is unavailable so a consumer can review or challenge it."

The FTC said data brokers are companies "that collect personal information about consumers from a variety of public and non-public sources and resell the information to other companies."

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What teens share on social media | #dataviz #analytics #digitalpractices

What teens share on social media | #dataviz #analytics #digitalpractices | e-Xploration | Scoop.it
Among teen social media users, percent who post the following to the profile they use most often

Via Aaron Balick
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Anatomy of a social network Network | #SNA #dataviz

Anatomy of a social network Network | #SNA #dataviz | e-Xploration | Scoop.it
Anatomy of a social network

Network researcher Ron Burt has identified two types of activities that create value in small-world networks: brokerage and…
luiy's insight:

Brokerage is about developing the weak ties: building bridges and relationships between clusters. Brokers are in a position to see the differences between groups, to cross-pollinate ideas, and to develop the differences into new ideas and opportunities.

Closure is about developing the strong ties: building alignment, trust, reputation and community within the clusters. Trust-builders are in a position to understand the deep connections that bond the people together and give them common identity and purpose.

These two kinds of activity, bridging and trust-building, demonstrate two very different ways that people and organizations can bring value to a network: Bridging leads to innovation and trust-building leads to group performance. The value that comes from these activities is known as social capital. Like every other form of capital, social capital represents stored value—in this case, relationship value—that can be translated into meaningful and tangible benefits.
The power of an individual node in any network can be considered along three dimensions: Degree, closeness and betweenness. 

Degree is the number of connections a node has to other nodes; for example the number of people in your family, or on your team at work, or the number of “friends” attached to your Facebook account. For an organization it could be the number of sales affiliates or business partners.

The value of a high degree is potential: the potential to connect and interact with a great number of other nodes in the network.

Closeness is a measure of how easily a node can connect with other nodes. For example you are probably very close to your team at work because it’s easy to connect to them: you can contact any person at any time. But you might be further away from other people in your company. Some you might be able to catch by walking down the hall or popping into their office, while to see others you might need an appointment, or you might need to be introduced by a mutual acquaintance. Anyone who has tried to make a connection on LinkedIn knows that the greater the distance, the harder it is to make a connection.

The value of closeness is ease of connection: The shorter the distance between you and other nodes, the fewer network “hops” you need to make, the easier it is for you to make connections when you need to.......

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Asset Management Tools for #Change: Social Network Analysis | #SNA #KM

Asset Management Tools for #Change: Social Network Analysis | #SNA #KM | e-Xploration | Scoop.it
Asset Management Tools for Change: Social Network Analysis
luiy's insight:

SOCIAL NETWORK ANALYSIS (SNA)


SNA is a methodology for determining and analyzing relationships between people in order to show how information flows and decisions are made, ultimately investigating how work gets done. This enables managers and teams to understand:

 

Who the prominent players are and whom others depend on to solve problems and provide technical information. Who do people turn to for advice? The actual nature of the communication network in reality, demonstrating how communications actually occur regarding work related issues and who is central to these communications. This illustrates both informal collaborative relationships and holes within the structures. Whether subgroups emerged that are disconnected or partially connected to the core. Which individuals are isolated and limited in their roles or, conversely, who faces a situation of overload.  

SNA is a means to analyze the informal organization beyond the organizational chart. The analysis allows managers and teams to visualize and understand the myriad of relationships that can either facilitate or impede information flow, decision processes and knowledge creation. Thus, mapping opportunities and constraints in invoking change within the organization.

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Visualized: The world of verified users | Twitter Blog | #dataviz #clusters

Visualized: The world of verified users | Twitter Blog | #dataviz #clusters | e-Xploration | Scoop.it
How do some of the world’s most famous people follow each other on Twitter?...
luiy's insight:

This beautiful visualization, created during Twitter Hack Week by our very own Isaac Hepworth (@isaach), shows the mutual follows between over 50,000 verified Twitter users — that is, which verified users follow each other.

 

The users in this map are colored by category: blue for news, purple for government and politics, red for music, yellow for sports and green for TV — the five largest categories on Twitter today.

 

One of the many fascinating things about this diagram is that it shows which accounts tend to follow those outside their category. For example, the reason that blue and purple almost seem to merge into one another is that journalists tend to follow politicians, and vice versa. The same is true of TV and music, down in the bottom right, with musicians and TV stars following each other often.

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Jay Ratcliff's comment, July 23, 2013 1:54 PM
While I find the graph interesting, I don't like not being able to interact with it in a more dynamic method.
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Organic Computing – A Paradigm Shift for Complex Systems | #eBook

Organic Computing – A Paradigm Shift for Complex Systems | #eBook | e-Xploration | Scoop.it
Organic Computing has emerged as a challenging vision for future information processing systems.

Via Spaceweaver
luiy's insight:

Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users.

 

These networks of intelligent systems surrounding us open fascinating ap-plication areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the tech-nologically possible seems absolutely central. The technical systems, which can achieve these goals will have to exhibit life-like or “organic” properties. “Organic Computing Systems” adapt dynamically to their current environmental conditions. In order to cope with unexpected or undesired events they are self-organising, self-configuring, self-optimising, self-healing, self-protecting, self-explaining, and context-aware, while offering complementary interfaces for higher-level directives with respect to the desired behaviour. First steps towards adaptive and self-organising computer systems are being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organisation are hot top-ics in a variety of research groups worldwide.

This book summarises the results of a 6-year priority research program (SPP) of the German Research Foundation (DFG) addressing these fundamental challenges in the design of Organic Computing systems. It presents and discusses the theoretical foundations of Organic Computing, basic methods and tools, learning techniques used in this context, architectural patterns and many applications. The final outlook shows that in the mean-time Organic Computing ideas have spawned a variety of promising new projects.

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Building a follower model from scratch | #dataviz #SNA

Building a follower model from scratch | #dataviz #SNA | e-Xploration | Scoop.it
Our follower graph has millions of nodes and billions of edges, making it an interesting challenge to maintain and scale data as we build out the interest graph. The model is similar to those of...

Via M. Edward (Ed) Borasky, ukituki
luiy's insight:

Our follower graph has millions of nodes and billions of edges, making it an interesting challenge to maintain and scale data as we build out the interest graph. The model is similar to those of Twitter or Facebook, but with some key differences based around interests that we account for in the product development and design phases.

 

The final version of the Pinterest follower service was developed, migrated and deployed in about 8 weeks with one full time engineer and 2-3 part time engineers.

 

Here I’ll explain how the service-oriented architecture has helped us develop and maintain the service as a unit of its own.

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Rescooped by luiy from Politique des algorithmes
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Big Data bullshit. by Christian Fauré | #controverses #critics #bigdata

Big Data bullshit. by Christian Fauré | #controverses #critics #bigdata | e-Xploration | Scoop.it
Je suis particulièrement étonné par le discours actuel sur les big data ; discours selon lequel nous serions passé de la causalité à la corrélation.

Via Dominique Cardon
luiy's insight:

Je suis particulièrement étonné par le discours actuel sur les big data ; discours selon lequel nous serions passé de la causalité à la corrélation. Je pense surtout à la thèse de Viktor Mayer-Schönberger et Kenneth Cukier, dans leur livre Big Data : une révolution qui va transformer notre façon de vivre, de travailler et penser. (voir l’excellent article de recension de Hubert Guillaud : Big Data : nouvelle étape de l’informatisation du monde.)

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Rescooped by luiy from artificial intelligence for students
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Internet of Things | Wired | #trends #tendances

Internet of Things | Wired | #trends #tendances | e-Xploration | Scoop.it
THE INTERNET OF THINGS FEATURED 07.08.13 Tour the World's Webcams With the Search Engine for the Internet

Via Scott Turner
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