OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive texture energy measure method
Venue: arXiv cs.CV 2014
Year: 2014

Recent developments in image quality, data storage, and computational capacity have heightened the need for texture analysis in image process. To date various methods have been developed and introduced for assessing textures in images. One of the most popular texture analysis methods is the Texture Energy Measure (TEM) and it has been used for detecting edges, levels, waves, spots and ripples by employing predefined TEM masks to images. Despite several success- ful studies, TEM has a number of serious weaknesses in use. The major drawback is; the masks are predefined therefore they cannot be adapted to image. A new method, Adaptive Texture Energy Measure Method (aTEM), was offered to over- come this disadvantage of TEM by using adaptive masks by adjusting the contrast, sharpening and orientation angle of the mask. To assess the applicability of aTEM, it is compared with TEM. The accuracy of the classification of butterfly, flower seed and Brodatz datasets are 0.08, 0.3292 and 0.3343, respectively by TEM and 0.0053, 0.2417 and 0.3153, respectively by aTEM. The results of this study indicate that aTEM is a successful method for texture analysis.
Close to Uniform Prime Number Generation With Fewer Random Bits
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, we analyze several variants of a simple method for generating prime numbers with fewer random bits. To generate a prime $p$ less than $x$, the basic idea is to fix a constant $q\propto x^{1-\varepsilon}$, pick a uniformly random $a<q$ coprime to $q$, and choose $p$ of the form $a+t\cdot q$, where only $t$ is updated if the primality test fails. We prove that variants of this approach provide prime generation algorithms requiring few random bits and whose output distribution is close to uniform, under less and less expensive assumptions: first a relatively strong conjecture by H.L. Montgomery, made precise by Friedlander and Granville; then the Extended Riemann Hypothesis; and finally fully unconditionally using the Barban-Davenport-Halberstam theorem. We argue that this approach has a number of desirable properties compared to previous algorithms.
Template Matching based Object Detection Using HOG Feature Pyramid
Venue: arXiv cs.CV 2014
Year: 2014

This article provides a step by step development of designing a Object Detection scheme using the HOG based Feature Pyramid aligned with the concept of Template Matching.
Co-occurrence matrices of time series applied to literary works
Venue: arXiv cs.CY 2014
Year: 2014

Recently, it has been proposed to analyse the literary works, plays or novels, using graphs to display the social network of their interacting characters. In this approach, the timeline of the literary work is lost, because the storyline is projected on a planar graph. However, timelines can be used to build some time series and analyse the work by means of vectors and matrices. These series can be used to describe the presence and relevance, not only of words in the text, but also of persons and places portrayed in the drama or novel. In this framework, we discuss here an approach with co-occurrence matrices plotted over time, concerning the presence of characters in the pages of a novel. These matrices are similar to those appearing in recurrence plots.
Linearized and Single-Pass Belief Propagation
Venue: arXiv cs.DB 2014
Year: 2014

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract"). One of the most widely used methods for this kind of inference is Belief Propagation (BP) which iteratively propagates the information from a few nodes with explicit labels throughout a network until convergence. One main problem with BP, however, is that there are no known exact guarantees of convergence in graphs with loops. This paper introduces Linearized Belief Propagation (LinBP), a linearization of BP that allows a closed-form solution via intuitive matrix equations and, thus, comes with convergence guarantees. It handles homophily, heterophily, and more general cases that arise in multi-class settings. Plus, it allows a compact implementation in SQL. The paper also introduces Single-pass Belief Propagation (SBP), a "localized" version of LinBP that propagates information across every edge at most once and for which the final class assignments depend only on the nearest labeled neighbors. In addition, SBP allows fast incremental updates in dynamic networks. Our runtime experiments show that LinBP and SBP are orders of magnitude faster than standard
Encouraging Knowledge Sharing Using Web 2.0 Technologies In Higher Education: A Survey
Venue: arXiv cs.CY 2014
Year: 2014

As the technology continuous to advance new technologies have emerged with the capability to revolutionize knowledge sharing practices. Web 2.0 exemplifies such new technologies which provides dynamic way of interactions of people and businesses. In learning environment Web 2.0 technologies support and enhance teaching and learning of students. Therefore the main aim of this study focuses on the determining the ways to encourage knowledge sharing through web 2.0 technologies from students' point of views. A total of 287 students responded to the online questionnaire in International Islamic University Malaysia. Descriptive statistics was used in data analysis. The results show that students used web 2.0 technologies in learning and sharing knowledge among them. In addition the study found eight items on ways to encourage and enhance knowledge sharing among students in the University. These items include Create Awareness Provide facilities Internet Accessibility Ease of use Encourage Teamwork Materials Availability Improved Responses and Motivation.
Does Offline Political Segregation Affect the Filter Bubble? An Empirical Analysis of Information Diversity for Dutch and Turkish Twitter Users
Venue: arXiv cs.SI 2014
Year: 2014

From a liberal perspective, pluralism and viewpoint diversity are seen as a necessary condition for a well-functioning democracy. Recently, there have been claims that viewpoint diversity is diminishing in online social networks, putting users in a "bubble", where they receive political information which they agree with. The contributions from our investigations are fivefold: (1) we introduce different dimensions of the highly complex value viewpoint diversity using political theory; (2) we provide an overview of the metrics used in the literature of viewpoint diversity analysis; (3) we operationalize new metrics using the theory and provide a framework to analyze viewpoint diversity in Twitter for different political cultures; (4) we share our results for a case study on minorities we performed for Turkish and Dutch Twitter users; (5) we show that minority users cannot reach a large percentage of Turkish Twitter users. With the last of these contributions, using theory from communication scholars and philosophers, we show how minority access is missing from the typical dimensions of viewpoint diversity studied by computer scientists and the impact it has on viewpoint diversity analysis.
Effect of Group Means on the Probability of Consensus
Venue: arXiv cs.SI 2014
Year: 2014

In this study, groups who could not reach a consensus were investigated using the group polarization paradigm. The purpose was to explore the conditions leading to intragroup disagreement and attitude change following disagreement among 269 participants. Analysis indicated that the probability of consensus was low when the group means differed from the grand mean of the entire sample. When small differences among group members were found, depolarization (reverse direction of polarization) followed disagreement. These results suggested the groups which deviated most from the population tendency were the most likely to cause within-group disagreement, while within-group variances determined the direction of attitude change following disagreement within the group.
Information Spread in a Connected World
Venue: arXiv cs.SI 2014
Year: 2014

In the following work, we compare the spread of information by word-of-mouth (WOM) to the spread of information through search engines. We assume that the initial acknowledgement of new information derives from social interactions but that solid opinions are only formed after further evaluation through search engines. Search engines can be viewed as central hubs that connect information presented in relevant websites to searchers. Since they construct new connections between searchers and information in every query performed, the network structure is less relevant. Although models of viral spread of ideas have been inspected in many previous works [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], only few assume the acceptance of a novel concept to be solely based on the evaluation of the opinions of others [8], [5]. Following this approach, combined with that of models of information spread with threshold [1] that claim the propagation in a network to occur only if a threshold of neighbors hold an opinion, the proposed work adds a new theoretical perspective that is relevant to the daily use of search engines as a major information search tool. We continue by presenting some justifications based on experimentations. Last we discuss possible outcomes of over use of search engines vs. WOM, and suggest an hypothesis that such overuse might actually narrow the collective information set.
Weighted Fair Multicast Multigroup Beamforming under Per-antenna Power Constraints
Venue: arXiv cs.IT 2014
Year: 2014

A multi-antenna transmitter that conveys independent sets of common data to distinct groups of users is considered. This model is known as physical layer multicasting to multiple co-channel groups. In this context, the practical constraint of a maximum permitted power level radiated by each antenna is addressed. The per-antenna power constrained system is optimized in a maximum fairness sense with respect to predetermined quality of service weights. In other words, the worst scaled user is boosted by maximizing its weighted signal-to-interference plus noise ratio. A detailed solution to tackle the weighted max-min fair multigroup multicast problem under per-antenna power constraints is therefore derived. The implications of the novel constraints are investigated via prominent applications and paradigms. What is more, robust per-antenna constrained multigroup multicast beamforming solutions are proposed. Finally, an extensive performance evaluation quantifies the gains of the proposed algorithm over existing solutions and exhibits its accuracy over per-antenna power constrained systems.
Less-is-more in a 5-star rating system: an experimental study of human combined decisions in a multi-armed bandit problem
Venue: arXiv cs.SI 2014
Year: 2014

Given the rapid proliferation of advanced information technologies, including the Internet, modern humans can easily access vast amount of socially transmitted information. Intuitively, this situation is isomorphic to some eusocial insects that are known to solve the exploration-exploitation dilemma collectively through information transfer (e.g., honeybees [Seeley et al., 1991]; and ants [Shaffer, Sasaki & Pratt, 2013]). Yet, in contrast from the eusocial insects, whose colonies are composed of kin, human collective performance may be affected by an inherent free-rider problem [Bolton & Harris, 1999; Kameda, Tsukasaki, Hastie & Berg, 2011]. Specifically, in groups involving non-kin members, it is expected that free-riders, who allow others to search for better alternatives and then exploit their findings through social learning ("information scroungers"), will frequently appear, and consequently undermine the advantage of collective intelligence [Rogers, 1998; Kameda & Nakanishi, 2003].
Analytical reasoning task reveals limits of social learning in networks
Venue: arXiv cs.SI 2014
Year: 2014

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias,' which limits their social learning to the output, rather than the process, of their peers' reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.
Stochastic stability and stabilization of a class of state-dependent jump linear systems
Venue: arXiv cs.SY 2014
Year: 2014

This paper deals a continuous-time state-dependent jump linear system, a particular kind of stochastic switching system. In particular, we consider a situation when the transition rate of the random jump process depends on the state variable, and addressed the problem of stochastic stability and stabilization analysis for the proposed system. Numerically solvable sufficient conditions for the stochastic stability and stabilization of the proposed system is established in terms of linear matrix inequalities. The obtained results are illustrated in numerical examples.
Reductions to the set of random strings: The resource-bounded case
Venue: arXiv cs.CC 2014
Year: 2014

This paper is motivated by a conjecture that BPP can be characterized in terms of polynomial-time nonadaptive reductions to the set of Kolmogorov-random strings. In this paper we show that an approach laid out in [Allender et al] to settle this conjecture cannot succeed without significant alteration, but that it does bear fruit if we consider time-bounded Kolmogorov complexity instead. We show that if a set A is reducible in polynomial time to the set of time-t-bounded Kolmogorov random strings (for all large enough time bounds t), then A is in P/poly, and that if in addition such a reduction exists for any universal Turing machine one uses in the definition of Kolmogorov complexity, then A is in PSPACE.
Decidability Results for the Boundedness Problem
Venue: arXiv cs.LO 2014
Year: 2014

We prove decidability of the boundedness problem for monadic least fixed-point recursion based on positive monadic second-order (MSO) formulae over trees. Given an MSO-formula phi(X,x) that is positive in X, it is decidable whether the fixed-point recursion based on phi is spurious over the class of all trees in the sense that there is some uniform finite bound for the number of iterations phi takes to reach its least fixed point, uniformly across all trees. We also identify the exact complexity of this problem. The proof uses automata-theoretic techniques. This key result extends, by means of model-theoretic interpretations, to show decidability of the boundedness problem for MSO and guarded second-order logic (GSO) over the classes of structures of fixed finite tree-width. Further model-theoretic transfer arguments allow us to derive major known decidability results for boundedness for fragments of first-order logic as well as new ones.
Weighted ancestors in suffix trees
Venue: arXiv cs.DS 2014
Year: 2014

The classical, ubiquitous, predecessor problem is to construct a data structure for a set of integers that supports fast predecessor queries. Its generalization to weighted trees, a.k.a. the weighted ancestor problem, has been extensively explored and successfully reduced to the predecessor problem. It is known that any solution for both problems with an input set from a polynomially bounded universe that preprocesses a weighted tree in O(n polylog(n)) space requires Ω(loglogn) query time. Perhaps the most important and frequent application of the weighted ancestors problem is for suffix trees. It has been a long-standing open question whether the weighted ancestors problem has better bounds for suffix trees. We answer this question positively: we show that a suffix tree built for a text w[1..n] can be preprocessed using O(n) extra space, so that queries can be answered in O(1) time. Thus we improve the running times of several applications. Our improvement is based on a number of data structure tools and a periodicity-based insight into the combinatorial structure of a suffix tree.
Home Is Where the Up-Votes Are: Behavior Changes in Response to Feedback in Social Media
Venue: arXiv cs.SI 2014
Year: 2014

Recent research shows that humans are heavily influenced by online social interactions: We are more likely to perform actions which, in the past, have led to positive social feedback. We introduce a quantitative model of behavior changes in response to such feedback, drawing on inverse reinforcement learning and studies of human game playing. The model allows us to make predictions, particularly in the context of social media, about which community a user will select, and to quantify how future selections change based on the feedback a user receives. We show that our model predicts real-world changes in behavior on a dataset gathered from reddit. We also explore how this relatively simple model of individual behavior can lead to complex collective dynamics when there is a population of users, each individual learning in response to feedback and in turn providing feedback to others.
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation
Venue: arXiv cs.RO 2014
Year: 2014

Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds. Method: This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars. Results: A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions. Conclusion: A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale test track.
First-order definable string transformations
Venue: arXiv cs.LO 2014
Year: 2014

The connection between languages defined by computational models and logic for languages is well-studied. Monadic second-order logic and finite automata are shown to closely correspond to each-other for the languages of strings, trees, and partial-orders. Similar connections are shown for first-order logic and finite automata with certain aperiodicity restriction. Courcelle in 1994 proposed a way to use logic to define functions over structures where the output structure is defined using logical formulas interpreted over the input structure. Engelfriet and Hoogeboom discovered the corresponding "automata connection" by showing that two-way generalised sequential machines capture the class of monadic-second order definable transformations. Alur and Cerny further refined the result by proposing a one-way deterministic transducer model with string variables---called the streaming string transducers---to capture the same class of transformations. In this paper we establish a transducer-logic correspondence for Courcelle's first-order definable string transformations. We propose a new notion of transition monoid for streaming string transducers that involves structural properties of both underlying input automata and variable dependencies. By putting an aperiodicity restriction on the transition monoids, we define a class of streaming string transducers that captures exactly the class of first-order definable transformations.
A note on hierarchical hubbing for a generalization of the VPN problem
Venue: arXiv cs.DS 2014
Year: 2014

Robust network design refers to a class of optimization problems that occur when designing networks to efficiently handle variable demands. The notion of "hierarchical hubbing" was introduced (in the narrow context of a specific robust network design question), by Olver and Shepherd [2010]. Hierarchical hubbing allows for routings with a multiplicity of "hubs" which are connected to the terminals and to each other in a treelike fashion. Recently, Fréchette et al. [2013] explored this notion much more generally, focusing on its applicability to an extension of the well-studied hose model that allows for upper bounds on individual point-to-point demands. In this paper, we consider hierarchical hubbing in the context of a previously studied (and extremely natural) generalization of the hose model, and prove that the optimal hierarchical hubbing solution can be found efficiently. This result is relevant to a recently proposed generalization of the "VPN Conjecture".
Secrecy Outage Probability of Network-Coded Cooperative Communication
Venue: arXiv cs.IT 2014
Year: 2014

We evaluate the secrecy performance of a multiple access cooperative network where the destination node is wiretapped by a malicious and passive eavesdropper. We propose the application of the network coding technique as an alternative to increase the secrecy at the destination node, on the top of improving the error performance of the legitimate communication, already demonstrated in the literature. Network coding is leveraged by assuming that the legitime cooperative nodes are able to perform non-binary linear combinations of different frames before the transmission. Different scenarios with and without channel state information (CSI) at the transmitter side are evaluated. The effectiveness of the proposed schemes is evaluated in terms of secrecy outage probability through theoretic and numerical analyses. It is shown that, even when the legitimate transmitters do not have any CSI, the secrecy can be increased through the use of network coding when compared to the direct transmission and traditional cooperative techniques.