OpenRaiser/Intern-Atlas huggingface dataset:
Efficiently decoding strings from their shingles
Venue: arXiv cs.DM 2012
Year: 2012

Determining whether an unordered collection of overlapping substrings (called shingles) can be uniquely decoded into a consistent string is a problem that lies within the foundation of a broad assortment of disciplines ranging from networking and information theory through cryptography and even genetic engineering and linguistics. We present three perspectives on this problem: a graph theoretic framework due to Pevzner, an automata theoretic approach from our previous work, and a new insight that yields a time-optimal streaming algorithm for determining whether a string of $n$ characters over the alphabet $Σ$ can be uniquely decoded from its two-character shingles. Our algorithm achieves an overall time complexity $Θ(n)$ and space complexity $O(|Σ|)$. As an application, we demonstrate how this algorithm can be extended to larger shingles for efficient string reconciliation.
Social Aspects of Virtual Teams
Venue: arXiv cs.SI 2012
Year: 2012

There has been a transformation from individual work to team work in the last few decades (Ilgen, 1999), and many organizations use teams for many activities done by individuals in the past (Boyett & Conn, 1992 ; Katzenbach & Smith, 1993). In recent years, there has been a renewed interest in computer-mediated groups because of the increases in globalization of business operations leading to geographically dispersed executives and decision makers. However, what seems to be lacking is some focus in terms of problem settings and corresponding tools to support collaborative decision making. The research question of this study deals with the dynamics of virtual teams' members. A model, suggesting that team dynamics can increase the teams' output, is presented, and a methodology to examine the model is illustrated. An experiment was performed, in which subjects, who were grouped into teams, had to share information in order to complete a task. The findings indicate that the social aspect of the virtual team's discussion is negative than the social aspect of the face-to-face team's discussion, and that the virtual team's output is inferior to the face-to-face team's output. The virtual team is a common way of working nowadays, and with the growing use of Internet applications and firms' globalization it will expand in the future. Thus, the importance of the theoretical and practical implementation of the research will be discussed.
Galaxysearch - Discovering the Knowledge of Many by Using Wikipedia as a Meta-Searchindex
Venue: arXiv cs.SI 2012
Year: 2012

We propose a dynamic map of knowledge generated from Wikipedia pages and the Web URLs contained therein. GalaxySearch provides answers to the questions we don't know how to ask, by constructing a semantic network of the most relevant pages in Wikipedia related to a search term. This search graph is constructed based on the Wikipedia bidirectional link structure, the most recent edits on the pages, the importance of the page, and the article quality; search results are then ranked by the centrality of their network position. GalaxySearch provides the results in three related ways: (1) WikiSearch - identifying the most prominent Wikipedia pages and Weblinks for a chosen topic, (2) WikiMap - creating a visual temporal map of the changes in the semantic network generated by the search results over the lifetime of the returned Wikipedia articles, and (3) WikiPulse - finding the most recent and most relevant changes and updates about a topic.
Airborne software tests on a fully virtual platform
Venue: arXiv cs.SE 2012
Year: 2012

This paper presents the early deployment of a fully virtual platform to perform the tests of certified airborne software. This is an alternative to the current approach based on the use of dedicated hardware platforms.
Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis
Venue: arXiv cs.NE 2012
Year: 2012

The hyperclimbing hypothesis is a hypothetical explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an intuitive, general-purpose, non-local search heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lie in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e. by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis holds that UGAs work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of a novel analytic technique involving the exploitation of algorithmic symmetry. We have also obtained experimental results that show that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.
The Effects of Prediction Market Design and Price Elasticity on Trading Performance of Users: An Experimental Analysis
Venue: arXiv cs.SI 2012
Year: 2012

We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to significantly higher trading performance than 'single-markets' that handle all contracts one on PM. Price elasticity has no direct effect on trading performance, but a significant interaction effect with market design implies that the performance difference between the market designs is highest in settings of moderate price elasticity. We contribute to the emerging research stream of PM design through an unprecedented experiment which compares current market designs.
Crowdsourcing Collective Emotional Intelligence
Venue: arXiv cs.SI 2012
Year: 2012

One of the hallmarks of emotional intelligence is the ability to regulate emotions. Research suggests that cognitive reappraisal - a technique that involves reinterpreting the meaning of a thought or situation - can down-regulate negative emotions, without incurring significant psychological or physiological costs. Habitual use of this strategy is also linked to many key indices of physical and emotional health. Unfortunately, this technique is not always easy to apply. Thinking flexibly about stressful thoughts and situations requires creativity and poise, faculties that often elude us when we need them the most. In this paper, we propose an assistive technology that coordinates collective intelligence on demand, to help individuals reappraise stressful thoughts and situations. In two experiments, we assess key features of our design and we demonstrate the feasibility of crowdsourcing empathetic reappraisals with on demand workforces, such as Amazon's Mechanical Turk.
Rationale awareness for quality assurance in iterative human computation processes
Venue: arXiv cs.HC 2012
Year: 2012

Human computation refers to the outsourcing of computation tasks to human workers. It offers a new direction for solving a variety of problems and calls for innovative ways of managing human computation processes. The majority of human computation tasks take a parallel approach, whereas the potential of an iterative approach, i.e., having workers iteratively build on each other's work, has not been sufficiently explored. This study investigates whether and how human workers' awareness of previous workers' rationales affects the performance of the iterative approach in a brainstorming task and a rating task. Rather than viewing this work as a conclusive piece, the author believes that this research endeavor is just the beginning of a new research focus that examines and supports meta-cognitive processes in crowdsourcing activities.
Optimizing Scrip Systems: Crashes, Altruists, Hoarders, Sybils and Collusion
Venue: arXiv cs.GT 2012
Year: 2012

Scrip, or artificial currency, is a useful tool for designing systems that are robust to selfish behavior by users. However, it also introduces problems for a system designer, such as how the amount of money in the system should be set. In this paper, the effect of varying the total amount of money in a scrip system on efficiency (i.e., social welfare---the total utility of all the agents in the system) is analyzed, and it is shown that by maintaining the appropriate ratio between the total amount of money and the number of agents, efficiency is maximized. This ratio can be found by increasing the money supply to just below the point that the system would experience a "monetary crash," where money is sufficiently devalued that no agent is willing to perform a service. The implications of the presence of altruists, hoarders, sybils, and collusion on the performance of the system are examined. Approaches are discussed to identify the strategies and types of agents.
A Computational Analysis of Collective Discourse
Venue: arXiv cs.SI 2012
Year: 2012

This paper is focused on the computational analysis of collective discourse, a collective behavior seen in non-expert content contributions in online social media. We collect and analyze a wide range of real-world collective discourse datasets from movie user reviews to microblogs and news headlines to scientific citations. We show that all these datasets exhibit diversity of perspective, a property seen in other collective systems and a criterion in wise crowds. Our experiments also confirm that the network of different perspective co-occurrences exhibits the small-world property with high clustering of different perspectives. Finally, we show that non-expert contributions in collective discourse can be used to answer simple questions that are otherwise hard to answer.
Crowd & Prejudice: An Impossibility Theorem for Crowd Labelling without a Gold Standard
Venue: arXiv cs.SI 2012
Year: 2012

A common use of crowd sourcing is to obtain labels for a dataset. Several algorithms have been proposed to identify uninformative members of the crowd so that their labels can be disregarded and the cost of paying them avoided. One common motivation of these algorithms is to try and do without any initial set of trusted labeled data. We analyse this class of algorithms as mechanisms in a game-theoretic setting to understand the incentives they create for workers. We find an impossibility result that without any ground truth, and when workers have access to commonly shared 'prejudices' upon which they agree but are not informative of true labels, there is always equilibria where all agents report the prejudice. A small amount amount of gold standard data is found to be sufficient to rule out these equilibria.
Robust stability and stabilization of uncertain linear positive systems via Integral Linear Constraints: L1- and Linfinity-gains characterization
Venue: arXiv cs.SY 2012
Year: 2012

Copositive linear Lyapunov functions are used along with dissipativity theory for stability analysis and control of uncertain linear positive systems. Unlike usual results on linear systems, linear supply-rates are employed here for robustness and performance analysis using L1- and Linfinity-gains. Robust stability analysis is performed using Integral Linear Constraints (ILCs) for which several classes of uncertainties are discussed. The approach is then extended to robust stabilization and performance optimization. The obtained results are expressed in terms of robust linear programming problems that are equivalently turned into finite dimensional ones using Handelman's Theorem. Several examples are provided for illustration.
Crowd Memory: Learning in the Collective
Venue: arXiv cs.SI 2012
Year: 2012

Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper explores how the crowd learns and remembers over time in the context of human computation, and how more realistic assumptions of worker experience may be used when designing new systems. We first demonstrate that the crowd can recall information over time and discuss possible implications of crowd memory in the design of crowd algorithms. We then explore crowd learning during a continuous control task. Recent systems are able to disguise dynamic groups of workers as crowd agents to support continuous tasks, but have not yet considered how such agents are able to learn over time. We show, using a real-time gaming setting, that crowd agents can learn over time, and `remember' by passing strategies from one generation of workers to the next, despite high turnover rates in the workers comprising them. We conclude with a discussion of future research directions for crowd memory and learning.
Leading the Collective: Social Capital and the Development of Leaders in Core-Periphery Organizations
Venue: arXiv cs.SI 2012
Year: 2012

Wikipedia and open source software projects have been cited as canonical examples of collectively intelligent organizations. Both organizations rely on large crowds of contributors to create knowledge goods. The crowds that emerge in both cases are not flat, but form a core-periphery network in which a few leaders contribute a large portion of the production and coordination work. This paper explores the social network processes by which leaders emerge from crowd-based organizations.
Evaluating CRM Implementation in Healthcare Organization
Venue: arXiv cs.SE 2012
Year: 2012

Recently, many healthcare organizations are adopting CRM as a strategy, which involves using technology to organize, automate, and coordinate business processes, in managing interactions with their patients. CRM with the Web technology provides healthcare providers the ability to broaden their services beyond usual practices, and thus offers suitable environment using latest technology to achieve superb patient care. This paper discusses and demonstrates how a new approach in CRM based on Web 2.0 will help the healthcare providers improving their customer support, avoiding conflict, and promoting better health to patient. With this new approach patients will benefit from the customized personal service with full information access to perform self managed their own health. It also helps healthcare providers retaining the right customer. A conceptual framework of the new approach will be discussed.
Large-System Analysis of Joint User Selection and Vector Precoding for Multiuser MIMO Downlink
Venue: arXiv cs.IT 2012
Year: 2012

Joint user selection (US) and vector precoding (US-VP) is proposed for multiuser multiple-input multiple-output (MU-MIMO) downlink. The main difference between joint US-VP and conventional US is that US depends on data symbols for joint US-VP, whereas conventional US is independent of data symbols. The replica method is used to analyze the performance of joint US-VP in the large-system limit, where the numbers of transmit antennas, users, and selected users tend to infinity while their ratios are kept constant. The analysis under the assumptions of replica symmetry (RS) and 1-step replica symmetry breaking (1RSB) implies that optimal data-independent US provides nothing but the same performance as random US in the large-system limit, whereas data-independent US is capacity-achieving as only the number of users tends to infinity. It is shown that joint US-VP can provide a substantial reduction of the energy penalty in the large-system limit. Consequently, joint US-VP outperforms separate US-VP in terms of the achievable sum rate, which consists of a combination of vector precoding (VP) and data-independent US. In particular, data-dependent US can be applied to general modulation, and implemented with a greedy algorithm.
Edited Volumes, Monographs, and Book Chapters in the Book Citation Index (BKCI) and Science Citation Index (SCI, SoSCI, A&HCI)
Venue: arXiv cs.DL 2012
Year: 2012

In 2011, Thomson-Reuters introduced the Book Citation Index (BKCI) as part of the Science Citation Index (SCI). The interface of the Web of Science version 5 enables users to search for both "Books" and "Book Chapters" as new categories. Books and book chapters, however, were always among the cited references, and book chapters have been included in the database since 2005. We explore the two categories with both BKCI and SCI, and in the sister social sciences (SoSCI) and the arts & humanities (A&HCI) databases. Book chapters in edited volumes can be highly cited. Books contain many citing references but are relatively less cited. This may find its origin in the slower circulation of books than of journal articles. It is possible to distinguish between monographs and edited volumes among the "Books" scientometrically. Monographs may be underrated in terms of citation impact or overrated using publication performance indicators because individual chapters are counted as contributions separately in terms of articles, reviews, and/or book chapters.
The evolution of classification systems: Ontogeny of the UDC
Venue: arXiv cs.DL 2012
Year: 2012

To classify is to put things in meaningful groups, but the criteria for doing so can be problematic. Study of evolution of classification includes ontogenetic analysis of change in classification over time. We present an empirical analysis of the UDC over the entire period of its development. We demonstrate stability in main classes, with major change driven by 20th century scientific developments. But we also demonstrate a vast increase in the complexity of auxiliaries. This study illustrates an alternative to Tennis' "scheme-versioning" method.
Approximately Optimal Auctions for Selling Privacy when Costs are Correlated with Data
Venue: arXiv cs.GT 2012
Year: 2012

We consider a scenario in which a database stores sensitive data of users and an analyst wants to estimate statistics of the data. The users may suffer a cost when their data are used in which case they should be compensated. The analyst wishes to get an accurate estimate, while the users want to maximize their utility. We want to design a mechanism that can estimate statistics accurately without compromising users' privacy. Since users' costs and sensitive data may be correlated, it is important to protect the privacy of both data and cost. We model this correlation by assuming that a user's unknown sensitive data determines a distribution from a set of publicly known distributions and a user's cost is drawn from that distribution. We propose a stronger model of privacy preserving mechanism where users are compensated whenever they reveal information about their data to the mechanism. In this model, we design a Bayesian incentive compatible and privacy preserving mechanism that guarantees accuracy and protects the privacy of both cost and data.
Access Graphs Results for LRU versus FIFO under Relative Worst Order Analysis
Venue: arXiv cs.DS 2012
Year: 2012

Access graphs, which have been used previously in connection with competitive analysis to model locality of reference in paging, are considered in connection with relative worst order analysis. In this model, FWF is shown to be strictly worse than both LRU and FIFO on any access graph. LRU is shown to be strictly better than FIFO on paths and cycles, but they are incomparable on some families of graphs which grow with the length of the sequences.
Analysis of Sparse Representations Using Bi-Orthogonal Dictionaries
Venue: arXiv cs.IT 2012
Year: 2012

The sparse representation problem of recovering an N dimensional sparse vector x from M < N linear observations y = Dx given dictionary D is considered. The standard approach is to let the elements of the dictionary be independent and identically distributed (IID) zero-mean Gaussian and minimize the l1-norm of x under the constraint y = Dx. In this paper, the performance of l1-reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O1 O2], where O1,O2 are independent and drawn uniformly according to the Haar measure on the group of orthogonal M x M matrices. By an application of the replica method, we obtain the critical conditions under which perfect l1-recovery is possible with bi-orthogonal dictionaries.
Motivations for Participation in Socially Networked Collective Intelligence Systems
Venue: arXiv cs.SI 2012
Year: 2012

One of the most significant challenges facing systems of collective intelligence is how to encourage participation on the scale required to produce high quality data. This paper details ongoing work with Phrase Detectives, an online game-with-a-purpose deployed on Facebook, and investigates user motivations for participation in social network gaming where the wisdom of crowds produces useful data.
Conception of a management tool of Technology Enhanced Learning Environments
Venue: arXiv cs.CY 2012
Year: 2012

This paper describes the process of the conception of a software tool of TELE management. The proposed management tool combines information from two sources: i) the automatic reports produced by the Learning Content Management System (LCMS) Blackboard and ii) the views of students and teachers on the use of the LCMS in the process of teaching and learning. The results show that the architecture of the proposed management tool has the features of a management tool, since its potential to control, to reset and to enhance the use of an LCMS in the process of teaching and learning and teacher training, is shown.
Speech Recognition: Increasing Efficiency of Support Vector Machines
Venue: arXiv cs.CV 2012
Year: 2012

With the advancement of communication and security technologies, it has become crucial to have robustness of embedded biometric systems. This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional feature space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Support Vector Machines(SVMs) and Lindear Discriminant Analysis(LDA) with MFCCs and implementing such system in real-time using SignalWAVE.
Design and Implementation of the End System to Intermediate System (ES-IS) Routing Information Exchange Protocol as a Loadable Kernel Module in Linux Kernel 2.6
Venue: arXiv cs.NI 2012
Year: 2012

This paper presents a partial implementation of the ES-IS Routing Information Exchange Protocol packet processing in Linux Kernel 2.6, which is for use in conjunction with the Connectionless Network Protocol (CLNP) in Aeronautical Telecommunication Network (ATN). First, we show the data structures involved in the protocol operation. Second, we describe the map of the packet processing whose design has been developed in the research. Third, we explain how the protocol is implemented as a loadable kernel module. Finally, we conclude the implementation result based on performed tests.