OpenRaiser/Intern-Atlas huggingface dataset:
Influence Process Structural Learning and the Emergence of Collective Intelligence
Venue: arXiv cs.SI 2014
Year: 2014

Recent work [Hazy 2012] has demonstrated computationally that collectives that are organized into networks which govern the flow of resources can learn to recognize newly emerging opportunities distributed in the environment. This paper argues that the system does this through a process analogous to neural network learning with relative status playing the role of synaptic weights. Hazy showed computationally that learning of this type can occur even when resource allocation decision makers have no direct visibility into the environment, have no direct understanding of the opportunity, and are not involved in their exploitation except to the extent that they evaluate the success or failure of funded projects. Effectively, the system of interactions learns which individuals have the best access to information and other resources within the ecosystem. Hazy [2012] calls this previously unidentified emergence phenomenon: Influence Process Structural Learning (IPSL). In the prior model of IPSL, a three-tiered organizational structure was predetermined in the model design [Hazy 2012]. These initial conditions delimit the extent to which the emergence of collective intelligence can be posited because the model itself assumes a defined structure. This work contributes to the field by extending the IPSL argument for collective intelligence to a holistic emergence argument. It begins by briefly reviewing previously published work. It continues the conversation by adding two additional steps: Firstly, it shows how a three-tier organizing structure might emerge through known complexity mechanisms. In this case the mechanism identified is preferential attachment [Barabasi 2002]. Secondly, the paper shows how collective intelligence can emerge within a system of agents when the influence structure among these agents is treated as a the genetic algorithm.
Architectures of Virtual Decision-Making: The Emergence of Gender Discrimination on a Crowdfunding Website
Venue: arXiv cs.SI 2014
Year: 2014

The increasing relevance of Internet-based markets requires a sustained investigation into the relationship between design and user behavior. This research begins within the sociology of quantification and markets to investigate the impacts of basic design decisions on user behavior and individual success on a widely used crowdfunding website. This study looks at one common design feature, publishing recipients' sex, on the probability of receiving funding. Following research in the sociology of gender, these effects are defined along individual, behavioral, and structural dimensions. The results reveal that before teachers' sex was published, gender discrimination was weak and inconsistent. However, afterward gender discrimination increases by an order of magnitude and becomes systematized. Contrary to expectation, donors did not discriminate by sex category, but by teachers' structural position and the kinds of language they used. Implications for research on gender discrimination, priming, and online behavior are discussed.
Human Communication Systems Evolve by Cultural Selection
Venue: arXiv cs.SI 2014
Year: 2014

Human communication systems, such as language, evolve culturally; their components undergo reproduction and variation. However, a role for selection in cultural evolutionary dynamics is less clear. Often neutral evolution (also known as 'drift') models, are used to explain the evolution of human communication systems, and cultural evolution more generally. Under this account, cultural change is unbiased: for instance, vocabulary, baby names and pottery designs have been found to spread through random copying. While drift is the null hypothesis for models of cultural evolution it does not always adequately explain empirical results. Alternative models include cultural selection, which assumes variant adoption is biased. Theoretical models of human communication argue that during conversation interlocutors are biased to adopt the same labels and other aspects of linguistic representation (including prosody and syntax). This basic alignment mechanism has been extended by computer simulation to account for the emergence of linguistic conventions. When agents are biased to match the linguistic behavior of their interlocutor, a single variant can propagate across an entire population of interacting computer agents. This behavior-matching account operates at the level of the individual. We call it the Conformity-biased model. Under a different selection account, called content-biased selection, functional selection or replicator selection, variant adoption depends upon the intrinsic value of the particular variant (e.g., ease of learning or use). This second alternative account operates at the level of the cultural variant. Following Boyd and Richerson we call it the Content-biased model. The present paper tests the drift model and the two biased selection models' ability to explain the spread of communicative signal variants in an experimental micro-society.
Collective intelligence in Massive Online Dialogues
Venue: arXiv cs.SI 2014
Year: 2014

The emergence and ongoing development of Web 2.0 technologies have enabled new and advanced forms of collective intelligence at unprecedented scales, allowing large numbers of individuals to act collectively and create high quality intellectual artifacts. However, little is known about how and when they indeed promote collective intelligence. In this manuscript, we provide a survey of the automated tools developed to analyze discourse-centric collective intelligence. By conducting a thematic analysis of the current research direction, a set of gaps and limitations are identified.
New measures for evaluating creativity in scientific publications
Venue: arXiv cs.SI 2014
Year: 2014

The goal of our research is to understand how ideas propagate, combine and are created in large social networks. In this work, we look at a sample of relevant scientific publications in the area of high-frequency analog circuit design and their citation distribution. A novel aspect of our work is the way in which we categorize citations based on the reason and place of it in a publication. We created seven citation categories from general domain references, references to specific methods used in the same domain problem, references to an analysis method, references for experimental comparison and so on. This added information allows us to define two new measures to characterize the creativity (novelty and usefulness) of a publication based on its pattern of citations clustered by reason, place and citing scientific group. We analyzed 30 publications in relevant journals since 2000 and their about 300 citations, all in the area of high-frequency analog circuit design. We observed that the number of citations a publication receives from different scientific groups matches a Levy type distribution: with a large number of groups citing a publication relatively few times, and a very small number of groups citing a publication a large number of times. We looked at the motifs a publication is cited differently by different scientific groups.
Efficiency Analysis of Materialized views in DataWarehouse Using selfmaintenance
Venue: arXiv cs.DB 2014
Year: 2014

A data warehouse is a large data repository for the purpose of analysis and decision making in organizations. To improve the query performance and to get fast access to the data, data is stored as materialized views (MV) in the data warehouse. When data at source gets updated, the materialized views also need to be updated. In this paper, we focus on the problem of maintenance of these materialized views and address the issue of finding such auxiliary views (AV) that together with the materialized views make the data self-maintainable and take minimal space. We propose an algorithm that uses key and referential constraints which reduces the total number of tuples in auxiliary views and uses idea of information sharing between these auxiliary views to further reduce number of auxiliary views.
Capturing collective conflict dynamics with sparse social circuits
Venue: arXiv cs.SI 2014
Year: 2014

We discuss a set of computational techniques, called Inductive Game Theory, for extracting strategic decision-making rules from time series data and constructing probabilistic social circuits. We construct these circuits by connecting component individuals and groups with strategies in a game and propose an inductive approach to reconstructing the edges. We demonstrate this approach with conflict behavior in a society of pigtailed macaques by identifying significant patterns in decision-making by individuals. With the constructed circuit, we then capture macroscopic features of the system that were not specified in the construction of the initial circuit, providing a mapping between individual level behaviors to collective behaviors over the scale of the group. We extend on previous work in Inductive Game Theory by more efficiently searching the space of possible strategies by grouping individuals into socially relevant sets to produce a more efficient, parsimonious specification of the underlying interactions between components. We discuss how we reduce the dimensionality of these circuits using coarse-graining or compression to build cognitive effective theories for collective behavior.
WeDo: Exploring Participatory, End-To-End Collective Action
Venue: arXiv cs.CY 2014
Year: 2014

Many celebrate the Internet's ability to connect individuals and facilitate collective action toward a common goal. While numerous systems have been designed to support particular aspects of collective action, few systems support participatory, end-to-end collective action in which a crowd or community identifies opportunities, formulates goals, brainstorms ideas, develops plans, mobilizes, and takes action. To explore the possibilities and barriers in supporting such interactions, we have developed WeDo, a system aimed at promoting simple forms of participatory, end-to-end collective action. Pilot deployments of WeDo illustrate that sociotechnical systems can support automated transitions through different phases of end-to-end collective action, but that challenges, such as the elicitation of leadership and the accommodation of existing group norms, remain.
Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Venue: arXiv cs.IR 2014
Year: 2014

Searching for concepts in science and technology is often a difficult task. To facilitate concept search, different types of human-generated metadata have been created to define the content of scientific and technical disclosures. Classification schemes such as the International Patent Classification (IPC) and MEDLINE's MeSH are structured and controlled, but require trained experts and central management to restrict ambiguity (Mork, 2013). While unstructured tags of folksonomies can be processed to produce a degree of structure (Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom enjoyed by the crowd typically results in less precision (Stock 2007). Existing classification schemes suffer from inflexibility and ambiguity. Since humans understand language, inference, implication, abstraction and hence concepts better than computers, we propose to harness the collective wisdom of the crowd. To do so, we propose a novel classification scheme that is sufficiently intuitive for the crowd to use, yet powerful enough to facilitate search by analogy, and flexible enough to deal with ambiguity. The system will enhance existing classification information. Linking up with the semantic web and computer intelligence, a Citizen Science effort (Good, 2013) would support innovation by improving the quality of granted patents, reducing duplicitous research, and stimulating problem-oriented solution design. A prototype of our design is in preparation. A crowd-sourced fuzzy and faceted classification scheme will allow for better concept search and improved access to prior art in science and technology.
On Minimal Corrections in ASP
Venue: arXiv cs.LO 2014
Year: 2014

As a programming paradigm, answer set programming (ASP) brings about the usual issue of the human error. Hence, it is desirable to provide automated techniques that could help the programmer to find the error. This paper addresses the question of computing a subset-minimal correction of a contradictory ASP program. A contradictory ASP program is often undesirable and we wish to provide an automated way of fixing it. We consider a minimal correction set of a contradictory program to be an irreducible set of rules whose removal makes the program consistent. In contrast to propositional logic, corrections of ASP programs behave non-monotonically. Nevertheless, we show that a variety of algorithms for correction set computation in propositional logic can be ported to ASP. An experimental evaluation was carried showing that having a portfolio of such algorithms is indeed of benefit.
Design of Power System Stabilizer using Fuzzy Based Sliding Mode Control Technique
Venue: arXiv cs.SY 2014
Year: 2014

Power systems are usually large non-linear systems, which are often subjected to low frequency electromechanical oscillations. Power System Stabilizers are often used as effective and economic means for damping the generator's electromechanical oscillations and enhance the overall stability of power systems. Power system stabilizers have been applied for several decades in utilities and they can extend power transfer stability limits by adding modulation signal through excitation control system. Sliding mode control is one of the main methods employed to overcome the uncertainty of the system. This controller can be applied very well in presence of both parameter uncertainties and unknown nonlinear function such as disturbance. To enhance stability and improve dynamic response of the system operating in faulty conditions a Fuzzy based Sliding Mode Control Power System Stabiliser is developed for a multimachine system with two generators and it is compared with the Conventional Power System Stabiliser, Fuzzy based Power System Stabiliser, Sliding Mode based Power System Stabiliser.
Low-cost commercial LEGO platform for mobile robotics
Venue: arXiv cs.RO 2014
Year: 2014

This paper shows the potential of a Lego\c{opyright} based low-cost commercial robotic platform for learning and testing prototypes in higher education and research. The overall setup aims to explain mobile robotic issues strongly related to several fields such as Mechatronics, Robotics, and Automatic Control theory. The capabilities and limitations of LEGO robots are studied within two projects. The first one involves a robotic vehicle which is able to follow several predefined paths. The second project concerns to the classical problem of position control. Algorithms and additional tools have been fully designed, applied and documented with results shown throughout the paper. The platform is found to be suitable for teaching and researching on key issues related to the aforementioned fields.
Performance Advancement of Wireless Sensor Networks using Low Power Techniques and Efficient Placement of Nodes
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we present optimization techniques for WSNs. Our main goal is to minimize the power consumption and latency. We address the problem of minimizing the energy consumption in WSNs including hardware. ZigBee protocol is used to design nodes on WSN to achieve a very low power consumption rate. Furthermore, we propose to use IRS protocol in WSN within a ZigBee technique to discover information from unaware locations and achieve efficiency of energy and sacrifices latency. Our main idea is to support WSNs with both ZigBee technique and IRS protocol. In addition, we address the problem of efficient node placement for congestion control in WSNs. Thus, we evaluate the performance of specific routing and some algorithms of congestion control when wireless sensor nodes are deployed under different placements of network. To demonstrate the strength of the used algorithms, our simulation in C# proves that ZigBee-IRS- ESRT-Flooding approaches reduce the power consumption from 10% to 19% when compared to existing techniques of low Power and node placement.
Randomized Block Coordinate Descent for Online and Stochastic Optimization
Venue: arXiv cs.LG 2014
Year: 2014

Two types of low cost-per-iteration gradient descent methods have been extensively studied in parallel. One is online or stochastic gradient descent (OGD/SGD), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types of methods together and propose online randomized block coordinate descent (ORBCD). At each iteration, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of the losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can converge at a geometric rate in expectation, matching the convergence rate of SGD with variance reduction and RBCD.
A Characterization of the Minimal Average Data Rate that Guarantees a Given Closed-Loop Performance Level
Venue: arXiv cs.SY 2014
Year: 2014

This paper studies networked control systems closed over noiseless digital channels. By focusing on noisy LTI plants with scalar-valued control inputs and sensor outputs, we derive an absolute lower bound on the minimal average data rate that allows one to achieve a prescribed level of stationary performance under Gaussianity assumptions. We also present a simple coding scheme that allows one to achieve average data rates that are at most 1.254 bits away from the derived lower bound, while satisfying the performance constraint. Our results are given in terms of the solution to a stationary signal-to-noise ratio minimization problem and builds upon a recently proposed framework to deal with average data rate constraints in feedback systems. A numerical example is presented to illustrate our findings.
Simultaneous Wireless Information and Power Transfer for Two-hop OFDM Relay System
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the simultaneous wireless information and power transfer (SWIPT) for two-hop orthogonal frequency division multiplexing (OFDM) decode-and-forward (DF) relay communication system, where a relay harvests energy from radio frequency signals transmitted by the source and then uses the harvested energy to assist the information transmission from the source to its destination. The power splitting receiver is considered at the relay, which splits the received signal into two power streams to perform information decoding (ID) and energy harvesting (EH) respectively. For better understanding the behavior and exploring the performance limit of such a system, resource allocation is studied to maximize the total achievable transmission rate. An optimization problem, which jointly takes into account the power allocation, the subcarrier pairing and the power splitting, is formulated. Due to its non-convexity, a resource allocation policy with low complexity based on separation principle is designed. Simulation results show that the system performance can be significantly improved by using our proposed policy. Moreover, the system performance behavior to the relay position is also discussed, and results show that in the two-hop OFDM system with EH relay, the relay should be deployed near the source, while in that with conventional non-EH relay, it should be deployed at the middle between the source and the destination.
DC approximation approaches for sparse optimization
Venue: arXiv cs.NA 2014
Year: 2014

Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an $\ell _{1}$-perturbed algorithm / reweighted-$\ell _{1}$ algorithm / reweighted-$\ell _{1}$ algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions.
Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
Venue: arXiv cs.IT 2014
Year: 2014

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance; our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix -- as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance, and also investigate extensions to settings where the data are noisy, or possibly incomplete.
Laboratories of Oligarchy? How the Iron Law Extends to Peer Production
Venue: arXiv cs.CY 2014
Year: 2014

Peer production projects like Wikipedia have inspired voluntary associations, collectives, social movements, and scholars to embrace open online collaboration as a model of democratic organization. However, many peer production projects exhibit entrenched leadership and deep inequalities, suggesting that they may not fulfill democratic ideals. Instead, peer production projects may conform to Robert Michels' "iron law of oligarchy," which proposes that democratic membership organizations become increasingly oligarchic as they grow. Using exhaustive data of internal processes from a sample of 683 wikis, we construct empirical measures of participation and test for increases in oligarchy associated with growth. In contrast to previous studies, we find support for Michels' iron law and conclude that peer production entails oligarchic organizational forms.