e-Xploration
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 Rescooped by luiy from Politique des algorithmes onto e-Xploration

# SLAVES TO THE ALGORITHM

luiy's insight:

Worse though, far worse, would be if someone in Hollywood filched his computer. It is here that the iconoclasm happens. When Meaney is given a job by a studio, the first thing he does is quantify thousands of factors, drawn from the script. Are there clear bad guys? How much empathy is there with the protagonist? Is there a sidekick? The complex interplay of these factors is then compared by the computer to their interplay in previous films, with known box-office takings. The last calculation is what it expects the film to make. In 83% of cases, this guess turns out to be within \$10m of the total. Meaney, to all intents and purposes, has an algorithm that judges the value—or at least the earning power—of art.

To explain how, he shows me a two-dimensional representation: a grid in which each column is an input, each row a film. "Curiously," Meaney says, "if we block this column…" With one hand, he obliterates the input labelled "star", casually rendering everyone from Clooney to Cruise, Damon to De Niro, an irrelevancy. "In almost every case, it makes no difference to the money column."

"For me that’s interesting. The first time I saw that I said to the mathematician, ‘You’ve got to change your program—this is wrong.’ He said, ‘I couldn’t care less—it’s the numbers.’" There are four exceptions to his rules. If you hire Will Smith, Brad Pitt or Johnny Depp, you seem to make a return. The fourth? As far as Epagogix can tell, there is an actress, one of the biggest names in the business, who is actually a negative influence on a film. "It’s very sad for her," he says. But hers is a name he cannot reveal.

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# e-Xploration

antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy

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## Oycib ::: Collective Intelligence. "Kaan". Network Visualisation.

luiy's insight:

Beginning with the origins, Oycib means in Mayan language "the place of honey". In this projet, Oycib is an e-Research infrastructure for the Collective Intelligence Analysis.

With Oycib infrastructure we propose an analysis model, based in the digital practices and collaboration profiles for the development of Social Learning and the Context Awareness in the Collective Intelligence process.

The infrastructure design and the profiles proposed here, are based on historical studies about social organization glyphs in Mayan culture made by Montgomery (2002) and Calvin (2012).

Initially we worked with four collaboration profiles: the "Itzaat", the "Pitziil", the "Ayuxul" and the "Sajal" (profiles), but we can find others depending of the organization context. Thus, it's important to mention that each profile is found based on the e-Xploración model and they are the qualitative and quantitative interpretation of the collaborative practices. In this way, we propose methods based on Social Network Analysis for the learning and knowledge management.

Thus, the network in Oycib is called "Kaan" (sky or network in Mayan Lenguage). In the "Kaan" we present the visualization of the subjects and objects, such as persons, forums, blogs, files, groups and all the interactions among them. Additionally, each profile and their interactions is presented.

... you can interact with "Kaan" here.

http://viz.oycib.org/net_all_3/network/index.html

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## Creating Data Visualizations | NodeBox #dataviz #tools

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## dc.js - Dimensional Charting Javascript Library

dc.js is a javascript charting library with native crossfilter support, allowing highly efficient exploration on large multi-dimensional datasets (inspired by crossfilter's demo). It leverages d3 to render charts in CSS-friendly SVG format. Charts rendered using dc.js are data driven and reactive and therefore provide instant feedback to user interaction.

dc.js is an easy yet powerful javascript library for data visualization and analysis in the browser and on mobile devices.
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## A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)

This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models.
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## Cognitive Bias Infographic

Via Claude Emond
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## Top-Secret NSA Report Details Russian Hacking Effort Days Before 2016 Election

The spear-phishing email contained a link directing the employees to a malicious, faux-Google website that would request their login credentials and then hand them over to the hackers. The NSA identified seven “potential victims” at the company. While malicious emails targeting three of the potential victims were rejected by an email server, at least one of the employee accounts was likely compromised, the agency concluded. The NSA notes in its report that it is “unknown whether the aforementioned spear-phishing deployment successfully compromised all the intended victims, and what potential data from the victim could have been exfiltrated.”

VR Systems declined to respond to a request for comment on the specific hacking operation outlined in the NSA document. Chief Operating Officer Ben Martin replied by email to The Intercept’s request for comment with the following statement:

Phishing and spear-phishing are not uncommon in our industry. We regularly participate in cyber alliances with state officials and members of the law enforcement community in an effort to address these types of threats. We have policies and procedures in effect to protect our customers and our company.
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## Graph Commons – Map networks together

Graph Commons is a collaborative platform for making, analyzing and publishing network maps.
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## Recherche Ina : Logo recognition and retrieval... (VF) - vidéo Dailymotion

Axe de recherche "Visualisation, Indexation & Fouille de données"
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## Tommaso Venturini / we love #networks, mais pourquoi ? I #SNA

SÉMINAIRE 2012 DE L’ERG : ENTRETIEN INFINI - ART ET SCIENCES (QUELS DIALOGUES ?) les 5, 6 et 7 mars 2012 / de 10h à 23h Halles de Schaerbeek,…
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## Fighting #Censorship with #ProtonMail Encrypted Email Over Tor - ProtonMail Blog - #privacy

In the past two years, ProtonMail has grown enormously, especially after the recent US election, and today we are the world’s largest encrypted email service with over 2 million users. We have come a long way since our user community initially crowdfunded the project.

ProtonMail today is much larger in scope than what was originally envisioned when our founding team met at CERN in 2013. As ProtonMail has evolved, the world has also been changing around us.

Civil liberties have been increasingly restricted in all corners of the globe. Even Western democracies such as the US have not been immune to this trend, which is most starkly illustrated by the forced enlistment of US tech companies into the US surveillance apparatus.

In fact, we have reached the point where it simply not possible to run a privacy and security focused service in the US or in the UK. At the same time, the stakes are also higher than ever before. As ProtonMail has grown, we have become increasingly aware of our role as a tool for freedom of speech, and in particular for investigative journalism.

Last fall, we were invited to the 2nd Asian Investigative Journalism Conference and were able to get a firsthand look at the importance of tools like ProtonMail in the field.

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## Sciences du Design : #Algorithmes - Éditorial - Cairn.info I #cartographie #dataviz

dans le cadre de ce numéro 04, la rubrique Visualisation accueille trois réalisations. La première, proposée par Sonia Pelloux, s’intitule « Cartographie des données : donner à voir l’open data SNCF ». Elle rend compte, notamment, des possibilités et avantages multiples d’une navigation par graphe au sein de la structure des données (de l’exploration visuelle de concepts et bases de données diverses). La deuxième réalisation titrée « Cloud Map » est de Ianis Lallemand. Partant de l’analyse de 2998 coordonnées GPS de centres de données, celle-ci situe la distribution géographique mondiale des infrastructures matérielles allouées au « Cloud Computing ». Il en résulte une représentation cartographique « en volume » offrant de voir autant que de sentir le poids, l’imposante présence d’une implantation dès lors massive de ce type d’installation. Enfin, la troisième réalisation fut composée en 2014 par Yann Le Guennec † – celle-ci vient en complément ou « résonance » avec le texte publié à titre posthume dans la rubrique Supplément. Intitulée « Les Paysages des erreurs », il s’agit d’une création artistique avec les données (Data Art) lesquelles deviennent ici le matériau brut d’une « activation » picturale/plastique programmée modifiant, altérant l’image « support » et sa signification. Ces trois projets sont à retrouver également sur le mini-site Visualisation de la revue à l’adresse : visu.sciences-du-design.org – à noter la publication d’une vidéo de présentation de l’application logicielle ici introduite par Sonia Pelloux.
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## SORTING - Visualizing sorting #algorithms | #dataviz #datascience

A visualization of the most famous Sorting Algorithms.
luiy's insight:

Sorting a sequence of items is one of the pillar of Computer Science.

A sorting algorithm is an algorithm that organizes elements of a sequence in a certain order. Since the early days of computing, the sorting problem has been one of the main battlefields for researchers. The reason behind this is not only the need of solving a very common task but also the challenge of solving a complex problem in the most efficient way.

SORTING is an attempt to visualize and help to understand how some of the most famous sorting algorithms work. This project provides two standpoints to look at algorithms, one is more artistic (apologies to any real artist out there), the other is more analytical aiming at explaining algorithm step by step.

This project does not want to teach the theory of sorting algorithms, there are amazing resources, books and courses for this purpose. SORTING is for the ones who want to see these algorithms under a different ligth and hopefully appreciate the processing and brain power behind these piece of genius that in many ways have changed the way we live.

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## #Python Packages For #DataMining

Just because you have a “hammer”, doesn’t mean that every problem you come across will be a “nail”.

The intelligent key thing is when you use  the same hammer to solve what ever problem
luiy's insight:

Topics:

- why pyton?

- Librerias -- > NumPy, SciPy, Pandas, Matlotlib, Ipython, scikit-learn

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## - Topogram - Visualize how networks change over time and space

"Topogram"

Topogram is a web-based app to visualize the evolution of networks over time and space.

Try it online at app.topogram.io or install your own.

Features
Network layouts + geographic data
Online/real-time data update via API

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## Data trails: “The comparative machine” (1836)

This post is part of a blog series on historical data vis titled “Data Trails. Snapshots from the history of data visualisation” and originally appeared over at my friends’ from Idalab, a Berlin-based specialist on data science and machine learning.

There is a very basic joy in roaming through atlases and in looking at maps. Atlases are rich collections of places, and if there is one thing they can do it is making you travel around the world, to places near and far. Cartography – an age-old discipline combining science and art – has created a unique way of looking at our planet, of flying over continuous landscapes and endless oceans. The 19th century was a golden age of atlases. Beautiful pieces for the educated household were printed in many different countries. Many of them feature one very special type of map: the comparative tableau showing the longest rivers and highest mountains of the world.
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## Interactive flow visualization in R

Exploring flows between origins and destinations visually is a common task, but can be difficult to get right. In R, there are many tutorials on the web that show how to produce static flow maps (see here, here, here, and here, among others).

Over the past couple years, R developers have created an infrastructure to bridge R with JavaScript using the htmlwidgets package, allowing for the generation of interactive web visualizations straight from R. I’d like to demonstrate here a few examples for exploratory interactive flow graphics that use this infrastructure.
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## Learning Analytics : quelles sont les données du problème ? | LINC

L’éducation et la formation tout au long de la vie constituent l’un des nouveaux eldorados du numérique. Les méthodes d’apprentissage en ligne se développent sous la forme de Moocs, de plateformes d’e-learning, passent par les réseaux sociaux, sont disponibles sur ordinateurs, tablettes, et smartphones.
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## 50 Research Methods for Innovation Infographic

Via Claude Emond
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## Interactive timeline of the PRISM scandal - Virostatiq

Constructing the visualization.

The visualization was constructed entirely in HTML5 and JavaScript. Four major libraries were used:

-  Sigma.js for displaying the networks. The latest version does not contain some key functionality for dynamically and additively loading and unloading of subgraphs into the main graph, so the source code was updated with required methods. Separate article on that topic is upcoming.

- Three.js for rotating Earth and all geographically-related work. Simile Timeline for the timeline.

- Flot for the bar graph.

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## 56Kast #94 – Archiver les réactions numériques aux attentats

► Abonnez-vous : http://bit.ly/abo56kast La chercheuse Valérie Schafer coordonne le projet Asap, qui veut analyser les réactions des internautes au
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## Recherche Ina - Exploration vidéo (VF) - vidéo Dailymotion

Axe de recherche "Visualisation, Indexation & Fouille de données"
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## #Visualizing the network of Donald #Trump - @Linkurious - Understand the connections in your data I #publicpolicy #SNA

On January 15, BuzzFeed released a large dataset of Donald Trump’s connections, including people, organizations and the nature of their relationships.

In their article “Help Us Map TrumpWorld”, the four authors of the investigation, John Templon, Anthony Cormier, Alex Campbell and Jeremy Singer-Vine, asked the public to help them understand and analyse the data.

Now we are asking the public to use our data to find connections we may have missed, and to give us context we don’t currently understand. We hope you will help us — and the public — learn more about TrumpWorld and how this unprecedented array of businesses might affect public policy.”

So we decided to see what it would looks like in Linkurious, our graph analysis and visualization tool.

The dataset is publicly available in a Google spreadsheet. We imported it in a Neo4j graph database using the following script inspired by Michael Hunger’s work:

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## Spatial History Project I #dataviz #cartographie

This research measures and maps the production of nineteenth-century space using the tools of the digital age. Computational analysis allowed me to quantify how late nineteenth-century newspapers crafted a view of the world for their readers. Specifically, I examine the Houston Daily Post from 1894 to 1901 to study how late nineteenth-century America appeared from a specific vantage point in time and space. What places loomed large in the paper’s imagined geography? How did large-scale processes of incorporation, standardization, and nationalization shape the paper’s production of space and place? What was the relationship between region and nation? To answer these questions I combine traditional historical research with digital analysis of the paper.

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## Predict Crime | Predictive Policing Software | PredPol

PredPol, "Predictive Policing Software", is an policing technology that helps law enforcement predict and prevent crime.

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## Mapzen Search | Resources | Data Driven Journalism l #ddj #dataviz

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## Travail et travailleurs de la donnée - #Algopol | #datascience #methods

Le questionnement scientifique qui anime le projet ALGOPOL voudrait comprendre la structure des liens sociaux existant au sein de réseaux égocentrés à partir du contenu des échanges et des liens partagés sur Facebook. Les interactions sur cette plateforme se déploient-elles différemment, avec une énonciation différente, autour de contenus partagés différents, selon les segments du réseau social mobilisés ? A-t-on des conversations différentes avec les liens « forts » et les liens « faibles » ? Les objets informationnels mis en partage sont-ils les mêmes selon la forme et la structure de la sociabilité numérique des individus ? Chercher à répondre à ces questions requiert des données fines et précises que les méthodes d’enquête traditionnelle ont beaucoup de difficulté à fournir [11].
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