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The Secret to Bats' Super-Efficient Flight Could Help Make Better Military Drones

The Secret to Bats' Super-Efficient Flight Could Help Make Better Military Drones | Social Foraging | Scoop.it
When a team of biologists, physicists, and engineers at Brown University put their heads together to look at batwings, they discovered how wings on everything from military vehicles to batman could become 35 percent more efficient.
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Social Foraging
Dynamics of Social Interaction
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How Movies Manipulate Your Brain to Keep You Entertained

How Movies Manipulate Your Brain to Keep You Entertained | Social Foraging | Scoop.it

There’s a crazy action sequence near the beginning ofIron Man 2 in which Tony Stark first meets Ivan Vanko, a rogue Russian scientist wearing a robotic suit and wielding electrified whips. It takes place at the Monaco Grand Prix, where Stark is competing, and Vanko slices up Formula 1 cars like so much toast and puts the hurt on Stark, even after he dons his Iron Man suit. For a minute there, it looks like the supercharged Russian might prevail.

 

For viewers, it’s quintessential, over-the-top Hollywood action. For scientists, it’s a window into the human brain. At a recent event here hosted by the Academy of Motion Picture Arts and Sciences, neuroscientists and cognitive psychologists got together with filmmakers to discuss what both groups have learned—the scientists through painstaking experiments and analysis, the filmmakers by intuition and experience—about the mechanisms of attention and perception.

 

In the Iron Man 2 sequence, for example, people are remarkably consistent in where they direct their gaze. Tim Smith, a vision scientist at the University of London, presented eye tracking data collected from 75 people as they watched the clip on a flatscreen. A camera tracked their eye movements, and software created a frame-by-frame heat map. When Smith played the clip with the eye-tracking heat map overlaid, the red hot spot tightly followed the action—people focused on the dueling superheroes, especially their weapons and faces, and on the car parts bouncing around (to see the clip, scroll down to the first video here).

 

Jon Favreau, who directed Iron Man 2, was onstage with Smith as he presented the clip, and seemed fascinated by it. “Everything you’re looking at is real, and everything you’re not looking at is fake,” he said.

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Anthropology of the Brain: Consciousness, Culture, and Free Will (by Roger Bartra)

Anthropology of the Brain: Consciousness, Culture, and Free Will

~ Roger Bartra (author) More about this product
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In this unique exploration of the mysteries of the human brain, Roger Bartra shows that consciousness is a phenomenon that occurs not only in the mind but also in an external network, a symbolic system. He argues that the symbolic systems created by humans in art, language, in cooking or in dress, are the key to understanding human consciousness. Placing culture at the centre of his analysis, Bartra brings together findings from anthropology and cognitive science and offers an original vision of the continuity between the brain and its symbolic environment. The book is essential reading for neurologists, cognitive scientists and anthropologists alike.

 

 


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Teaching machines to read between the lines (and a new corpus with entity salience annotations)

Teaching machines to read between the lines (and a new corpus with entity salience annotations) | Social Foraging | Scoop.it
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Facebook’s Quest to Build an Artificial Brain Depends on This Guy

Facebook’s Quest to Build an Artificial Brain Depends on This Guy | Social Foraging | Scoop.it

It’s good to be Yann LeCun.

 

Mark Zuckerberg recently handpicked the longtime NYU professor to run Facebook’s new artificial intelligence lab. The IEEE Computational Society just gave him its prestigious Neural Network Pioneer Award, in honor of his work on deep learning, a form of artificial intelligence meant to more closely mimic the human brain. And, perhaps most of all, deep learning has suddenly spread across the commercial tech world, from Google to Microsoft to Baidu to Twitter, just a few years after most AI researchers openly scoffed at it.

 

All of these tech companies are now exploring a particular type of deep learning called convolutional neural networks, aiming to build web services that can do things like automatically understand natural language and recognize images. At Google, “convnets” power the voice recognition system available on Android phones. At China’s Baidu, they drive a new visual search engine. This kind of deep learning has many fathers, but its success should resonate with LeCun more than anyone. “Convolutional neural nets for vision—that’s what he pushed more than anybody else,” says Microsoft’s Leon Bottou, one of LeCun’s earliest collaborators.

 

He pushed it in the face of enormous skepticism. In the ’80s, when LeCun first got behind the idea of convnets—an approximation of the networks of neurons in the brain—the powerful computers and enormous data sets needed to make them work just didn’t exist. The very notion of a neural network had fallen into disrepute after it failed to deliver on the promises of scientists who first dreamed of artificial intelligence at the dawn of the computer age. It was hard to publish anything related to neural nets in the major academic journals, and this would remain the case in the ’90s and on into the aughts.

But LeCun persisted. “He kind of carried the torch through the dark ages,” says Geoffrey Hinton, the central figure in the deep learning movement. And eventually, computer power caught up with the remarkable technology.

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Discovering Social Events through Online Attention

Discovering Social Events through Online Attention | Social Foraging | Scoop.it

Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

 
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Reward-Priming of Location in Visual Search

Reward-Priming of Location in Visual Search | Social Foraging | Scoop.it

Existing visual search research has demonstrated that the receipt of reward will be beneficial for subsequent perceptual and attentional processing of features that have characterized targets, but detrimental for processing of features that have characterized irrelevant distractors. Here we report a similar effect of reward on location. Observers completed a visual search task in which they selected a target, ignored a salient distractor, and received random-magnitude reward for correct performance. Results show that when target selection garnered rewarding outcome attention is subsequently a.) primed to return to the target location, and b.) biased away from the location that was occupied by the salient, task-irrelevant distractor. These results suggest that in addition to priming features, reward acts to guide visual search by priming contextual locations of visual stimuli.

 
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Quantifying the semantics of search behavior before stock market moves

Quantifying the semantics of search behavior before stock market moves | Social Foraging | Scoop.it

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

 
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AleksBlumentals's curator insight, August 14, 4:52 AM

Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk.


We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

 

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#freeBook: Social Media Mining | #datascience #SNA #influence

#freeBook: Social Media Mining | #datascience #SNA #influence | Social Foraging | Scoop.it

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luiy's curator insight, August 10, 11:46 AM

The Social Media Mining book is published by Cambridge University Press in 2014. Please see Cambridge’s page for the book for more information or if you are interested in obtaining an examination copy.

 

Download a complete pre-publicaiton draft of the Social Media Mining book in PDF format. The reader is allowed to take one copy for personal use but not for further distribution (either print or electronically). The book is available for purchase from Cambridge University Press and other distribution channels.

 

You can also download each chapter below:

 

• Chapter 1. Introduction to social media mining

 

Part I: Essentials
• Chapter 2. Graph essentials
• Chapter 3. Network measures
• Chapter 4. Network models
• Chapter 5. Data mining essentials

 

Part II: Communities and Interactions
• Chapter 6. Community analysis
• Chapter 7. Information diffusion in Social Media

 

Part III: Applications
• Chapter 8. Influence and homophily
• Chapter 9. Recommendation in social media
• Chapter 10. Behavior analytics

 

Download the Bibliography

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Memory Capacity of Networks with Stochastic Binary Synapses

Memory Capacity of Networks with Stochastic Binary Synapses | Social Foraging | Scoop.it

In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level , in the large  and sparse coding limits (). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex.

 

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Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images | Social Foraging | Scoop.it

Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.

 

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An Infographic That Maps 2,000 Years of Cultural History in 5 Minutes

An Infographic That Maps 2,000 Years of Cultural History in 5 Minutes | Social Foraging | Scoop.it

Ah, Hollywood. Our glowing beacon of modern hope and dreams. But before Hollywood, there was New York, and before New York there was Berlin, Paris, Rome and Greece. History’s most creative people have always flocked to cultural and intellectual hubs, and now, thanks to an amazing visualization from researchers at the University of Texas at Dallas, we can see how that migration has changed over time.

 

Last week in the journal Science, the researchers (led by University of Texas art historian Maximilian Schich) published a study that looked at the cultural history of Europe and North America by mapping the birth and deaths of more than 150,000 notable figures—including everyone from Leonardo Da Vinci to Ernest Hemingway. That data was turned into an amazing animated infographic that looks strikingly similar to the illustrated flight paths you find in the back of your inflight magazine. Blue dots indicate a birth, red ones means death.

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Academic’s signal-boosting app heads to more football stadiums

Academic’s signal-boosting app heads to more football stadiums | Social Foraging | Scoop.it
A smartphone app – developed by a University of Sussex academic - that boosts phone signal in big stadiums is heading to more football grounds around the country.

 

With the new football season getting underway this weekend, thousands of fans will be able to give the red card to poor wi-fi and phone signal during matches by downloading the free digitalStadium app, which creates a network between phones in the stadium to share bandwidth.

 

The digitalStadium technology enables fans and the club to communicate with each other during a match, providing real-time information on other key games, league table stats and travel information.

 

Fans can also take part in Twitter debates and competitions such as Rate the Ref while watching the game, while a live ticker feed delivers the latest news, views and special offers from the club.

 

The technology was developed by a team led by Dr Ian Wakeman, Senior Lecturer in Software Systems, and has been on trial for over a year at Brighton and Hove Albion FC, whose stadium is just across the road from the University.

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Computational Linguistics of Twitter Reveals the Existence of Global Superdialects

Computational Linguistics of Twitter Reveals the Existence of Global Superdialects | Social Foraging | Scoop.it

A dialect is a particular form of language limited to a specific region or social group. Linguists are fascinated by dialects because they reveal social classes, patterns of immigration and how groups have influenced each other in the past.

 

But studying dialects is hard work. Traditionally, linguists do this by interviewing a relatively small number of people, typically a few hundred, and asking them to fill out questionnaires. Researchers then use the results to create linguistic atlases but these are naturally limited by the choice of the locations and individuals who have been studied.

 

Today, Bruno Gonçalves at the University of Toulon in France and David Sánchez at the Institute for Cross-Disciplinary Physics and Complex Systems on the island of Majorca, Spain, say they have found a new way to study dialects on a global scale using messages posted on Twitter. The results reveal a major surprise about the way dialects are distributed around the world and provide a fascinating snapshot of how they are evolving under various new pressures, such as global communication mechanisms like Twitter.

 

Gonçalves and Sánchez begin by sampling all of the tweets written in Spanish over two years and that also contain geolocation information. That gave them a database of 50 million geolocated tweets, with most from Spain, Spanish America, and the United States.

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38 maps that explain the global economy

38 maps that explain the global economy | Social Foraging | Scoop.it

Commerce knits the modern world together in a way that nothing else quite does. Almost anything you own these days is the result of a complicated web of global interactions. And there's no better way to depict those interactions and the social and political circumstances that give rise to them than with a map or two. Or in our case, 38. These maps are our favorite way to illustrate the major economic themes facing the world today. Some of them focus on the big picture while others illustrate finer details. The overall portrait that emerges is of a world that's more closely linked than ever before, but still riven by enormous geography-driven differences.

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The Ultimate Challenge For Recommendation Engines

The Ultimate Challenge For Recommendation Engines | Social Foraging | Scoop.it
If you share an on-line move account with other people in your household, you probably receive some inappropriate recommendations. That may soon change.

 

The phrase “People who bought X, also bought Y” has become one of the celebrated monikers of the internet era. This particular form of words comes from recommendation engines that analyse the products you have bought in the past to suggest products you might like in future, usually based on the choices made by other people with similar tastes.

 

Good recommendation engines can increase sales by several percent. Which is why they have become one of the must-have features for online shops and services.

 

So it is not hard to understand why there is considerable interest in improving the performance of recommendation engines. Indeed, in 2006, the online movie provider, Netflix, offered a prize of $1 million to anybody who could improve their recommendation algorithm by more than 10 percent. The prize was duly snapped up a mere three years later.

 

So where might the next improvements come from?

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Origin of symbol-using systems: speech, but not sign, without the semantic urge

Natural language—spoken and signed—is a multichannel phenomenon, involving facial and body expression, and voice and visual intonation that is often used in the service of a social urge to communicate meaning. Given that iconicity seems easier and less abstract than making arbitrary connections between sound and meaning, iconicity and gesture have often been invoked in the origin of language alongside the urge to convey meaning. To get a fresh perspective, we critically distinguish the origin of a system capable of evolution from the subsequent evolution that system becomes capable of. Human language arose on a substrate of a system already capable of Darwinian evolution; the genetically supported uniquely human ability to learn a language reflects a key contact point between Darwinian evolution and language. Though implemented in brains generated by DNA symbols coding for protein meaning, the second higher-level symbol-using system of language now operates in a world mostly decoupled from Darwinian evolutionary constraints. Examination of Darwinian evolution of vocal learning in other animals suggests that the initial fixation of a key prerequisite to language into the human genome may actually have required initially side-stepping not only iconicity, but the urge to mean itself. If sign languages came later, they would not have faced this constraint.


Origin of symbol-using systems: speech, but not sign, without the semantic urge
Martin I. Sereno

http://dx.doi.org/10.1098/rstb.2013.0303
Phil. Trans. R. Soc. B 19 September 2014 vol. 369 no. 1651 20130303


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Twitter Launches Data Visualization Tool for Its Everyday Moments in the UK

Twitter Launches Data Visualization Tool for Its Everyday Moments in the UK | Social Foraging | Scoop.it

Twitter has launched a tool for brands and advertisers in the UK that makes use of its collated ‘Everyday Moments’ data to show exactly how often, where and when people interact when talking about certain topics.

 

For example, you might want to check out the UK’s ongoing war between tea and coffee drinkers or you might want to see how many people around the nation are grumbling about traffic delays.

 

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Online collaboration: Scientists and the social network

Online collaboration: Scientists and the social network | Social Foraging | Scoop.it

In 2011, Emmanuel Nnaemeka Nnadi needed help to sequence some drug-resistant fungal pathogens. A PhD student studying microbiology in Nigeria, he did not have the expertise and equipment he needed. So he turned to ResearchGate, a free social-networking site for academics, and fired off a few e-mails. When he got a reply from Italian geneticist Orazio Romeo, an inter­national collaboration was born. Over the past three years, the two scientists have worked together on fungal infections in Africa, with Nnadi, now at Plateau State University in Bokkos, shipping his samples to Romeo at the University of Messina for analysis. “It has been a fruitful relationship,” says Nnadi — and they have never even met.

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Natural selection drives the evolution of ant life cycles

Natural selection drives the evolution of ant life cycles | Social Foraging | Scoop.it

The genetic origin of advanced social organization has long been one of the outstanding problems of evolutionary biology. Here we present an analysis of the major steps in ant evolution, based for the first time, to our knowledge, on combined recent advances in paleontology, phylogeny, and the study of contemporary life histories. We provide evidence of the causal forces of natural selection shaping several key phenomena: (i) the relative lateness and rarity in geological time of the emergence of eusociality in ants and other animal phylads; (ii) the prevalence of monogamy at the time of evolutionary origin; and (iii) the female-biased sex allocation observed in many ant species. We argue that a clear understanding of the evolution of social insects can emerge if, in addition to relatedness-based arguments, we take into account key factors of natural history and study how natural selection acts on alleles that modify social behavior.

 
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Experimental evolution of prepared learning

Experimental evolution of prepared learning | Social Foraging | Scoop.it

Animals learn some things more easily than others. To explain this so-called prepared learning, investigators commonly appeal to the evolutionary history of stimulus–consequence relationships experienced by a population or species. We offer a simple model that formalizes this long-standing hypothesis. The key variable in our model is the statistical reliability of the association between stimulus, action, and consequence. We use experimental evolution to test this hypothesis in populations ofDrosophila. We systematically manipulated the reliability of two types of experience (the pairing of the aversive chemical quinine with color or with odor). Following 40 generations of evolution, data from learning assays support our basic prediction: Changes in learning abilities track the reliability of associations during a population’s selective history. In populations where, for example, quinine–color pairings were unreliable but quinine–odor pairings were reliable, we find increased sensitivity to learning the quinine–odor experience and reduced sensitivity to learning quinine–color. To the best of our knowledge this is the first experimental demonstration of the evolution of prepared learning.

 

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EU-funded tool to help our brain deal with big data

EU-funded tool to help our brain deal with big data | Social Foraging | Scoop.it
With data generation in the quadrillions of bytes every minute, the European Union joins with researchers to find a way to process all this information

 

Every single minute, the world generates 1.7 million billion bytes of data, equal to 360,000 DVDs. How can our brain deal with increasingly big and complex data sets? European Union researchers are developing an interactive system that not only presents data the way we want but also changes the presentation constantly in order to prevent brain overload. The project could enable students to study more efficiently or journalists to cross-check sources more quickly. Several museums in Germany, the Netherlands, the United Kingdom and the United States have already showed interest in the new technology.

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Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Collective Learning and Optimal Consensus Decisions in Social Animal Groups | Social Foraging | Scoop.it

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

 
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Sproutling Opens Pre-Orders For Wearable Baby Monitor That Makes Your Life Less Stressful

Sproutling Opens Pre-Orders For Wearable Baby Monitor That Makes Your Life Less Stressful | Social Foraging | Scoop.it

Calling itself the “world’s smartest baby monitor” Sproutling is launching its pre-ordercampaign today for its machine-learning monitor that predicts a baby’s sleeping patterns, mood and room conditions to provide personalized insights for parents via an iOS app.

 

Last year, Sproutling raised $2.6 million and an additional $100,000 this year to design the wearable aimed at raising parenting IQ and relieving stress.

 

Smart baby monitors aren’t new. Withings’ Smart Baby Monitor and Mimo both have features such as notifying parents if something is wrong with their baby, and wearable boot Owlet measures a baby’s heart rate. But Sproutling uses its machine-learning technology to send parents a push notification when they cannot pay full attention, such as in the shower or when taking a nap. It pairs a wearable band with a mobile app to deliver the results of 16 measurements in real time such as:

Heart ratePredicting when the baby will wake upIf the baby rolled overThe baby’s mood when he or she wakes upThe brightness and noise levels of the room, in case the baby has a hard time sleepingThe room temperature

 

The app doesn’t simply provide the heart rate as a number, as co-founder Chris Bruce says it might cause a parent, who doesn’t really know the difference between 130 and 120 beats per minute, to worry. Instead, the wearable learns the baby’s patterns, whether it is the heart rate or sleep cycle, and averages it out to provide insights if an irregular change occurs.

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Top Ten #ddj: The Week’s Most Popular Data Journalism Links

Top Ten #ddj: The Week’s Most Popular Data Journalism Links | Social Foraging | Scoop.it
What's the data driven journalism (#ddj) crowd tweeting about? Here are the week's Top Data Journalism Links on Twitter (for July 23-August 7), including items from Mother Jones, The Economist, and The New York Times, among others.
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Browser Fingerprinting and the Online-Tracking Arms Race

Browser Fingerprinting and the Online-Tracking Arms Race | Social Foraging | Scoop.it
Web advertisers are stealthily monitoring our browsing habits—even when we tell them not to

 

In July 1993, The New Yorker published a cartoon by Peter Steiner that depicted a Labrador retriever sitting on a chair in front of a computer, paw on the keyboard, as he turns to his beagle companion and says, “On the Internet, nobody knows you’re a dog.” Two decades later, interested parties not only know you’re a dog, they also have a pretty good idea of the color of your fur, how often you visit the vet, and what your favorite doggy treat is.

 

How do they get all that information? In a nutshell: Online advertisers collaborate with websites to gather your browsing data, eventually building up a detailed profile of your interests and activities. These browsing profiles can be so specific that they allow advertisers to target populations as narrow as mothers with teenage children or people who require allergy-relief products. When this tracking of our browsing habits is combined with our self-revelations on social media, merchants’ records of our off-line purchases, and logs of our physical whereabouts derived from our mobile phones, the information that commercial organizations, much less government snoops, can compile about us becomes shockingly revealing.

 

Here we examine the history of such tracking on the Web, paying particular attention to a recent phenomenon called fingerprinting, which enables companies to spy on people even when they configure their browsers to avoid being tracked.


The earliest approach to online tracking made use of cookies, a feature added to the pioneering Web browser Netscape Navigator a little over a year after Steiner’s cartoon hit newsstands. Other browsers eventually followed suit.

 

Cookies are small pieces of text that websites cause the user’s browser to store. They are then made available to the website during subsequent visits, allowing those sites to recognize returning customers or to keep track of the state of a given session, such as the items placed in an online shopping cart. Cookies also enable sites to remember that users are logged in, freeing them of the need to repeatedly provide their user names and passwords for each protected page they access.

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