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Dynamics of Social Interaction
Curated by Ashish Umre
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Eavesdropping on Endangered Species With iPods and Machine Learning

Eavesdropping on Endangered Species With iPods and Machine Learning | Social Foraging | Scoop.it

Somewhere in Puerto Rico, a small yellow frog is chirping into a microphone attached to an iPod. Several kilometers away, a computer is listening. Within a minute, that song will be posted online, and the species of the frog will be identified — all without scientists lifting a finger.

 

This wildlife recording studio is part of a new project to study biodiversity using automated hardware and software. ARBIMON, which stands for automated remote biodiversity monitoring network, was developed by Mitchell Aide and Carlos Corrada-Bravo from the University of Puerto Rico, who report their new work this week in the journal PeerJ. They teamed up to apply 21st century technology to the problem of species monitoring, combining readily available parts with advanced machine-learning algorithms to analyze thousands of hours of wildlife audio in real time.

 

Scientists have long used automated technology to track deforestation, but they haven’t had nearly as much success in developing similar techniques to monitor the effects of climate change and habitat loss on fauna. “We don’t have good, long-term data on how these pressures are affecting the abundance or distribution of species,” says Aide. The challenge is that human researchers can only be in so many places at once, and only for so long. And even when they deploy automated recorders, thousands of skilled man-hours are required to sift through the resulting data.

 

That’s where ARBIMON’s new software comes in handy.

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MIT Whiz Wants to Turn Your Skin Into a Computer Interface

MIT Whiz Wants to Turn Your Skin Into a Computer Interface | Social Foraging | Scoop.it

According to Lynette Jones, a senior research scientist in MIT’s Department of Mechanical Engineering, your skin has about as many sensory receptors as your eyeballs, making it a hugely underutilized medium for receiving information. The problem with skin, though, is that those receptors are spread out over 1.8 square meters, and we don’t currently have a very good idea of how sensitive a given patch of epidermis is going to be. We can feel a phone vibrating through our pants, sure. But could we tell if it was buzzing in a particular pattern? Or just vibrating its left side, as opposed to the right? These are the questions Jones is trying to answer, with an eye towards next-gen devices that don’t just pump info into our eyes and ears, but directly onto our hides, too.

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Were cave-painters on DRUGS? New study claims paintings show prehistoric man was 'high' on psychedelic plants

Were cave-painters on DRUGS? New study claims paintings show prehistoric man was 'high' on psychedelic plants | Social Foraging | Scoop.it

Cave paintings made by prehistoric man, which were previously thought to accurately represent the world around them, may have been hallucinations drawn while the painters were on drugs.


Researchers from Tokyo studied pictures of cave markings from around the world and concluded that the patterns made by the early artists resemble those created during tests of modern-day humans when they were under the influence of drugs.


Plants that the cavemen would have eaten during spiritual rituals could have contained these hallucinogens that caused this mind-altering state.

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A latent capacity for evolutionary innovation through exaptation in metabolic systems

Some evolutionary innovations may originate non-adaptively as exaptations, or pre-adaptations, which are by-products of other adaptive traits. Examples include feathers, which originated before they were used in flight, and lens crystallins, which are light-refracting proteins that originated as enzymes. The question of how often adaptive traits have non-adaptive origins has profound implications for evolutionary biology, but is difficult to address systematically. Here we consider this issue in metabolism, one of the most ancient biological systems that is central to all life. We analyse a metabolic trait of great adaptive importance: the ability of a metabolic reaction network to synthesize all biomass from a single source of carbon and energy. We use novel computational methods to sample randomly many metabolic networks that can sustain life on any given carbon source but contain an otherwise random set of known biochemical reactions. We show that when we require such networks to be viable on one particular carbon source, they are typically also viable on multiple other carbon sources that were not targets of selection. For example, viability on glucose may entail viability on up to 44 other sole carbon sources. Any one adaptation in these metabolic systems typically entails multiple potential exaptations. Metabolic systems thus contain a latent potential for evolutionary innovations with non-adaptive origins. Our observations suggest that many more metabolic traits may have non-adaptive origins than is appreciated at present. They also challenge our ability to distinguish adaptive from non-adaptive traits.

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Complexity Economics: A Different Framework for Economic Thought

This paper provides a logical framework for complexity economics. Complexity economics builds from the proposition that the economy is not necessarily in equilibrium: economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually create. This further changes the outcome, which requires them to adjust afresh. Agents thus live in a world where their beliefs and strategies are constantly being “tested” for survival within an outcome or “ecology” these beliefs and strategies together create. Economics has largely avoided this nonequilibrium view in the past, but if we allow it, we see patterns or phenomena not visible to equilibrium analysis. These emerge probabilistically, last for some time and dissipate, and they correspond to complex structures in other fields. We also see the economy not as something given and existing but forming from a constantly developing set of technological innovations, institutions, and arrangements that draw forth further innovations, institutions and arrangements.(...) 

 

Complexity Economics: A Different Framework for Economic Thought
W. Brian Arthur
SFI WP 13-04-012

http://www.santafe.edu/research/working-papers/abstract/36df2f7d8ecd8941d8fab92ded2c4547/


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Bill Aukett's curator insight, July 16, 2013 10:24 PM

If you've read Waldrop's account of the development of the complexity paradigm at the Sante Fe Institute (Waldrop, M, (1992) Complexity: The Emerging Science at the Edge of Chaos, Simon & Schuster, New York), the name Brian Arthur will be familiar.

Betty Cares's curator insight, July 17, 2013 9:39 AM

Another interesting paper from one of our great complexity thinkers, Brian Arthur, author of the El Farol Problem.  I will publish that here soon too!

Luciano Lampi's curator insight, July 18, 2013 8:11 AM

does democracy represent the best tool to face non-equilibrium states and emergence? 

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MIT develops system to write computer code using ordinary language

MIT develops system to write computer code using ordinary language | Social Foraging | Scoop.it

In a pair of recent papers, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have demonstrated that it is possible to write computer programs using ordinary language rather than special-purpose programming languages. A new algorithm can automatically convert natural-language specifications into "regular expressions" — special-purpose combinations of symbols that allow very flexible searches of digital files.

The work may be of some help to programmers, and it could let nonprogrammers manipulate common types of files — like word-processing documents and spreadsheets — in ways that previously required familiarity with programming languages. But the researchers’ methods could also prove applicable to other programming tasks, expanding the range of contexts in which programmers can specify functions using ordinary language.

“I don’t think that we will be able to do this for everything in programming, but there are areas where there are a lot of examples of how humans have done translation,” says Regina Barzilay, an associate professor of computer science and electrical engineering and a co-author on both papers. “If the information is available, you may be able to learn how to translate this language to code.”


Via Dr. Stefan Gruenwald
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Skip Stein's curator insight, July 14, 2013 9:12 AM

This used to be called COBOL!  Now they are trying to 'invent' English language programming AGAIN?  There was also a 'language' called 'ENGLISH' by MicroData decades ago (early precursor to SQL).  If COBOL would have been allowed to progress, we wouldn't be coding in C+ and other low level languages.  Thanks to Microsoft who torpedoed the entire computer language development. (IMHO)

Miro Svetlik's curator insight, July 15, 2013 7:13 AM

I am really wondering how would my daily vocal output look like in form of RegEx. However it is a nice achievement that we can map human language to regular expression formula. Still I personally think that successful implementation of AI which will be able to foresee human mistakes will be necessary before such a conversions can take place in daily life.

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Optimal Dynamics for Quality Control in Spatially Distributed Mitochondrial Networks

Optimal Dynamics for Quality Control in Spatially Distributed Mitochondrial Networks | Social Foraging | Scoop.it

Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles.

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Uncover Consumer Sentiment with Hadoop - Sentiment Data

Uncover Consumer Sentiment with Hadoop - Sentiment Data | Social Foraging | Scoop.it

How can you understand what your customers are thinking, and how can you respond to those sentiments?

 

Unstructured data from multiple sources such as social media, blogs, customer support transcripts and online reviews contain many data points related to consumer sentiment around products, services, brand and competitive position. Combining and analyzing these data sources uncovers sentiment information that, when understood, can assist with business decisions such as marketing and promotion spend.

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From Technology-Driven Society to Socially Oriented Technology-The Future of Information Society - Alternatives to Surveillance

From Technology-Driven Society to Socially Oriented Technology-The Future of Information Society - Alternatives to Surveillance | Social Foraging | Scoop.it
Our society is changing. Almost nothing these days works without a computer chip; computing power doubles every 18 months, and in ten years it will probably exceed the capabilities of a human brain. Computers perform approximately 70 percent of all financial transactions today and IBM's Watson now seems to give better customer advise than some human telephone hotlines.
The forthcoming economic and social transformation might be more fundamental than the one resulting from the invention of the steam engine. Meanwhile, the storage capacity of data grows even faster than the computational capacity. Within a few years, we will generate more data than in the entire history of humankind. The "Internet of Things" will soon network trillions of sensors together - fridges, coffee machines, electric toothbrushes and even our clothes. Vast amounts of data will be collected. Already, Big Data is being heralded as the oil of the 21st Century. 

But this situation will also make us vulnerable. Exploding cyber-crime, economic crises and social protests show that our hyper-connected world is destabilizing. However, is a Surveillance Society the right answer? When all our Internet queries are stored, when our purchases and social contacts are evaluated, when our emails and files are scanned for search terms, and when countless innocent citizens are classified as potential future terrorists, we must ask: Where will this lead to? And where will it end?

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Do Honeybees Shape the Bacterial Community Composition in Floral Nectar?

Floral nectar is considered the most important reward animal-pollinated plants offer to attract pollinators. Here we explore whether honeybees, which act as pollinators, affect the composition of bacterial communities in the nectar. Nectar and honeybees were sampled from two plant species: Amygdalus communis and Citrus paradisi. To prevent the contact of nectar with pollinators, C. paradisi flowers were covered with net bags before blooming (covered flowers). Comparative analysis of bacterial communities in the nectar and on the honeybees was performed by the 454-pyrosequencing technique. No significant differences were found among bacterial communities in honeybees captured on the two different plant species. This resemblance may be due to the presence of dominant bacterial OTUs, closely related to the Arsenophonus genus. The bacterial communities of the nectar from the covered and uncovered C. paradisi flowers differed significantly; the bacterial communities on the honeybees differed significantly from those in the covered flowers’ nectar, but not from those in the uncovered flowers’ nectar. We conclude that the honeybees may introduce bacteria into the nectar and/or may be contaminated by bacteria introduced into the nectar by other sources such as other pollinators and nectar thieves.

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Testing Whether and When Abstract Symmetric Patterns Produce Affective Responses

Testing Whether and When Abstract Symmetric Patterns Produce Affective Responses | Social Foraging | Scoop.it

Symmetry has a central role in visual art, it is often linked to beauty, and observers can detect it efficiently in the lab. We studied what kind of fast and automatic responses are generated by visual presentation of symmetrical patterns. Specifically, we tested whether a brief presentation of novel symmetrical patterns engenders positive affect using a priming paradigm. The abstract patterns were used as primes in a pattern-word interference task. To ensure that familiarity was not a factor, no pattern and no word was ever repeated within each experiment. The task was to classify words that were selected to have either positive or negative valence. We tested irregular patterns, patterns containing vertical and horizontal reflectional symmetry, and patterns containing a 90 deg rotation. In a series of 7 experiments we found that the effect of affective congruence was present for both types of regularity but only when observers had to classify the regularity of the pattern after responding to the word. The findings show that processing abstract symmetrical shapes or random pattern can engender positive or negative affect as long as the regularity of the pattern is a feature that observers have to attend to and classify.

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Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality

Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality | Social Foraging | Scoop.it

Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called ‘NetworGame’, which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as ‘game centrality’. The efficiency of this measure was verified on well-known social networks, and was extended to ‘protein games’, i.e. the simulation of cooperation between proteins, or their amino acids. Hubs and in particular, party hubs of yeast protein-protein interaction networks had a large influence to convert the cooperation of other nodes to defection. Simulations on methionyl-tRNA synthetase protein structure network indicated an increased influence of nodes belonging to intra-protein signaling pathways on breaking cooperation. The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks.

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A Simulation Optimization Approach to Epidemic Forecasting

A Simulation Optimization Approach to Epidemic Forecasting | Social Foraging | Scoop.it

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.

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GitHub Helps Clueless Coders Go Open Source

GitHub Helps Clueless Coders Go Open Source | Social Foraging | Scoop.it

GitHub has become one of the most important places for open source software developers to publish code and collaborate on projects. But, ironically, most projects hosted publicly on GitHub aren’t open source, at least according to the letter of open source law.

 

Aaron Williamson, a lawyer specializing in open source issues, analyzed over 1.7 million public GitHub code repositories earlier this year, and of these, only 14.9 percent had clearly specified an open source license, as reported by The Register.

 

Developers sharing code publicly on GitHub agree to a terms of service that allows other users to view and copy code, but if a license isn’t explicitly chosen, other developers won’t have the right to actually change or redistribute the code. According to the definition set by the Open Source Initiative (OSI), a license isn’t considered open source unless it grants users permission to not just view source code but also modify code and distribute their changes.

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Self-organization versus top-down planning in the evolution of a city

Interventions of central, top-down planning are serious limitations to the possibility of modelling the dynamics of cities. An example is the city of Paris (France), which during the 19th century experienced large modifications supervised by a central authority, the `Haussmann period'. In this article, we report an empirical analysis of more than 200 years (1789-2010) of the evolution of the street network of Paris. We show that the usual network measures display a smooth behavior and that the most important quantitative signatures of central planning is the spatial reorganization of centrality and the modification of the block shape distribution. Such effects can only be obtained by structural modifications at a large-scale level, with the creation of new roads not constrained by the existing geometry. The evolution of a city thus seems to result from the superimposition of continuous, local growth processes and punctual changes operating at large spatial scales.

 

Self-organization versus top-down planning in the evolution of a city
Marc Barthelemy, Patricia Bordin, Henri Berestycki, Maurizio Gribaudi

http://arxiv.org/abs/1307.2203


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Bian Wu's curator insight, October 12, 2014 7:46 PM

now bring the self-organization and control to city planning

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BCBT13- Barcelona Cognition, Brain and Technology summer school

BCBT13- Barcelona Cognition, Brain and Technology summer school | Social Foraging | Scoop.it

BCBT is an annual international summer school that promotes a shared systems-level understanding of the functional architecture of the brain and its possible emulation in artificial systems. BCBT addresses students and researchers involved in research at the interface between brains and technology such as in the ambit of  “Bio-ICT convergence” "Brain Inspired ICT" and “cognitive systems and robotics”.

 

The Barcelona Cognition, Brain and Technology summer school. Barcelona, Spain, Sept 2-13, 2013

http://bcbt.upf.edu/bcbt13/


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The Impact of Human-Robot Interfaces on the Learning of Visual Objects

This paper studies the impact of interfaces allowing non-expert users to efficiently and intuitively teach a robot to recognize new visual objects. We present challenges that need to be addressed for real-world deployment of robots capable of learning new visual ¡objects in interaction with everyday users. We argue that in addition to robust machine learning and computer vision methods, well-designed interfaces are crucial for learning efficiency. In particular, we argue that interfaces can be key in helping non-expert users to collect good learning examples and thus improve the performance of the overall learning system. Then, we present four alternative human-robot interfaces: three are based on the use of a mediating artifact (smartphone, wiimote, wiimote and laser), and one is based on natural human gestures (with a Wizard-of-Oz recognition system).

 

These interfaces mainly vary in the kind of feedback provided to the user, allowing him to understand more or less easily what the robot is perceiving, and thus guide his way of providing training examples differently. We then evaluate the impact of these interfaces, in terms of learning efficiency, usability and user's experience, through a real world and large scale user study. In this experiment, we asked participants to teach a robot twelve different new visual objects in the context of a robotic game. This game happens in a home-like environment and was designed to motivate and engage users in an interaction where using the system was meaningful. We then discuss results that show significant differences among interfaces. In particular, we show that interfaces such as the smartphone interface allows non-expert users to intuitively provide much better training examples to the robot, almost as good as expert users who are trained for this task and aware of the different visual perception and machine learning issues. We also show that artifact-mediated teaching is significantly more efficient for robot learning, and equally good in terms of usability and user's experience, than teaching thanks to a gesture-based human-like interaction.

 

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From Strange Simplicity to Complex Familiarity: A Treatise on Matter, Information, Life and Thought (by Manfred Eigen)

From Strange Simplicity to Complex Familiarity: A Treatise on Matter, Information, Life and Thought

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This book presents a vivid argument for the almost lost idea of a unity of all natural sciences. It starts with the "strange" physics of matter, including particle physics, atomic physics and quantum mechanics, cosmology, relativity and their consequences (Chapter I), and it continues by describing the properties of material systems that are best understood by statistical and phase-space concepts (Chapter II). These lead to entropy and to the classical picture of quantitative information, initially devoid of value and meaning (Chapter III). Finally, "information space" and dynamics within it are introduced as a basis for semantics (Chapter IV), leading to an exploration of life and thought as new problems in physics (Chapter V).

Dynamic equations - again of a strange (but very general) nature - bring about the complex familiarity of the world we live in. Surprising new results in the life sciences open our eyes to the richness of physical thought, and they show us what can and what cannot be explained by a Darwinian approach. The abstract physical approach is applicable to the origins of life, of meaningful information and even of our universe.

 

 


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The Minimal Complexity of Adapting Agents Increases with Fitness

The Minimal Complexity of Adapting Agents Increases with Fitness | Social Foraging | Scoop.it

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness.

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Arjen ten Have's curator insight, July 14, 2013 8:25 AM

Very interesting this measure of irreducible entity, minimal complexity.

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IJSIR 2014 : IJSIR Special Issue on Swarm Intelligence in Big Data Analytics

Important Dates:
January 1, 2014: Submission deadline.
March 1, 2014: Notice of the first round review.
April 1, 2014: Revision due
May 1, 2014: Final notice of acceptance/reject
June 1, 2014: Final manuscript due.

 

Nowadays, the big data has attracted attentions from more and more researchers. The big data is defined as the dataset whose size is beyond the processing ability of typical database or computers. The big data analytics is to automatically extract knowledge from large amounts of data. It can be seen as mining or processing of massive data, and “useful” information could be retrieved from large dataset. The properties of big data analytics can be concentrated in three parts: large volume, variety of different sources, and fast increasing speed, i.e., velocity. The algorithms should be effective to solve large-scale, dynamic the big data analytics problems.

Swarm intelligence (SI), which is based on a population of individuals, is a collection of nature-inspired searching techniques. To search a problem domain, a swarm intelligence algorithm processes a population of individuals. Each individual represents a potential solution of the problem being optimized. In swarm intelligence, an algorithm maintains and successively improves a population of potential solutions until some stopping condition is met. The solutions are initialized randomly in the search space, and are guided toward the better and better areas through the interaction among solutions over iterations.

The swarm intelligence algorithms have shown significant achievements on solving large scale, dynamical, and multi-objective problems. With the application of the swarm intelligence, more rapid and effective methods can be designed to solve big data analytics problems.

This special issue aims at fostering the latest development of Swarm Intelligence Techniques for Big Data analytics problems. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of Big Data analytics are very welcome for this Special Issue.

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Sensor Data for Your “Internet of Things” - Hadoop Tutorial

Sensor Data for Your “Internet of Things” - Hadoop Tutorial | Social Foraging | Scoop.it
What can sensor data tell you about your assets and environment? 

 

A sensor is a converter that measures a physical quantity and converts that to a digital signal. From your refrigerator and coffee maker, to your athletic watch, to the smart meters on the back of our homes, your personal machines create sensor data. Assembly lines, office buildings, cell towers and jet engines also stream data critical to managing an enterprise.

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What's Your Social Media Genotype?

What's Your Social Media Genotype? | Social Foraging | Scoop.it
Your pattern of behaviour on Twitter can be defined by a simple “genotype” and used to predict your future behaviour, say network researchers

 

One of the curious features of human behaviour is that it is predictable in certain circumstances but not in others. Knowing the difference is a fantastically valuable skill.

 

That’s why social media researchers the world over are scrutinising social networks for clues they can use to predict people’s behaviour on scales that have never before been achieved. On this blog, we’ve looked at various attempts to show social media can be used to predict, with varying degrees of success, people’s buying habits, movie tastes and even their future stock market purchases.

 

Today, Petko Bogdanov at the University of California Santa Barbara and a few pals take a new, genetically-inspired approach to this task. They say every person has a fixed set of interests, called their social media genotype, which determines their pattern of behaviour on networks such as Twitter. What’s more, they say that these genotypes have been discovered, can be used to predict an individual’s future behaviour.

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Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface

Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface | Social Foraging | Scoop.it

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

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Human Decision Making Based on Variations in Internal Noise: An EEG Study

Human Decision Making Based on Variations in Internal Noise: An EEG Study | Social Foraging | Scoop.it

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or ‘internal’ noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.

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Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks | Social Foraging | Scoop.it

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

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