Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards expert finding by leveraging relevant categories in authority ranking

How to improve authority ranking is a crucial research problem for expert finding. In this paper, we propose a novel framework for expert finding based on the authority information in the target category as well as the relevant categories. First, we develop a scalable method for measuring the relevancy between categories through topic models. Then, we provide a link analysis approach for ranking user authority by considering the information in both the target category and the relevant categories. Finally, the extensive experiments on two large-scale real-world Q&A data sets clearly show that the proposed method outperforms the baseline methods with a significant margin.
Bias of the SIR filter in estimation of the state transition noise

This Note investigates the bias of the sampling importance resampling (SIR) filter in estimation of the state transition noise in the state space model. The SIR filter may suffer from sample impoverishment that is caused by the resampling and therefore will benefit from a sampling proposal that has a heavier tail, e.g. the state transition noise simulated for particle preparation is bigger than the true noise involved with the state dynamics. This is because a comparably big transition noise used for particle propagation can spread overlapped particles to counteract impoverishment, giving better approximation of the posterior. As such, the SIR filter tends to yield a biased (bigger-than-the-truth) estimate of the transition noise if it is unknown and needs to be estimated, at least, in the forward-only filtering estimation. The bias is elaborated via the direct roughening approach by means of both qualitative logical deduction and quantitative numerical simulation.
AAO: Autoimmune and Autoinflammatory (Disease) in Otology: What is New in Immune‐Mediated Hearing Loss

Autoinflammatory diseases are a family of immune‐mediated, rare diseases, some of which, exhibit sensorineural hearing loss (SNHL), suggesting potentially similar mechanisms of molecular pathogenesis between autoinflammatory‐mediated hearing loss and autoimmune inner ear disease (AIED) may exist. The purpose of this review is to compare the clinical features of autoimmune and autoinflammatory diseases that affect hearing, discuss the limitations of our knowledge, and highlight potential new disease mechanisms and therapeutics.
Shape Index SIFT: Range Image Recognition Using Local Features

Range image recognition gains importance in the recent years due to the developments in acquiring, displaying, and storing such data. In this paper, we present a novel method for matching range surfaces. Our method utilizes local surface properties and represents the geometry of local regions efficiently. Integrating the Scale Invariant Feature Transform (SIFT) with the shape index (SI) representation of the range images allows matching of surfaces with different scales and orientations. We apply the method for scaled, rotated, and occluded range images and demonstrate the effectiveness it by comparing the previous studies.
Rate-Optimized Power Allocation for DF-Relayed OFDM Transmission under Sum and Individual Power Constraints

We consider an OFDM (orthogonal frequency division multiplexing) point-to-point transmission scheme which is enhanced by means of a relay. Symbols sent by the source during a first time slot may be (but are not necessarily) retransmitted by the relay during a second time slot. The relay is assumed to be of the DF (decode-and-forward) type. For each relayed carrier, the destination implements maximum ratio combining. Two protocols are considered. Assuming perfect CSI (channel state information), the paper investigates the power allocation problem so as to maximize the rate offered by the scheme for two types of power constraints. Both cases of sum power constraint and individual power constraints at the source and at the relay are addressed. The theoretical analysis is illustrated through numerical results for the two protocols and both types of constraints.
Hierarchical Structures: Conducive or Barrier to Knowledge Management?

This study examines managers‟ perceptions of Knowledge Management (KM) prior to implementation of KM-systems in a global insurance company and investigates whether Hierarchical structures are conducive to KM. Mixed methods are used, combining large scale surveying and case study using content analysis to organize the data into themes that provide the basis for arguments. Evidence suggests that managers strongly align their perception of KM with communication. Despite a multi-layered, hierarchical structure and strong middle management presence, organizational structure was not viewed as an issue. These factors are usually barriers to communication and organizational flexibility, yet managers believe that they may not inhibit KM becoming fully embedded. This evidence is contradicted by the results of a global KM study where silos, stovepipe and hierarchical structures were commonly cited as barriers. This contributes to the understanding of managerial mis-conceptions of knowledge as opposed to communication, and how organizations effectively share knowledge.
Detecting Brain Activations in Functional Magnetic Resonance Imaging (fMRI) Experiments with a Maximum Cross-Correlation Statistic

Various statistical models have been proposed to analyze fMRI data. The usual goal is to make inferences about the eects that are related to an external stimulus. The primary focus of this paper is on those statisti- cal methods that enable one to detect 'signicantly activated' regions of the brain due to event-related stimuli. Most of these methods share a common property, requiring estimation of the hemodynamic response function (HRF) as part of the deterministic component of the statistical model. We propose and investigate a new approach that does not require HRF ts to detect 'activated' voxels. We argue that the method not only avoids tting a specic HRF, but still takes into account that the unknown response is delayed and smeared in time. This method also adapts to dierential re- sponses of the BOLD response across dierent brain regions and experimen- tal sessions. The maximum cross-correlation between the kernel-smoothed stimulus sequence and shifted (lagged) values of the observed response is the proposed test statistic. Using our recommended approach we show through realistic simulations and with real data that we obtain better sensitivity than simple correlation methods using default values of SPM2. The simulation experiment incor- porates dierent HRFs empirically determined from real data. The noise models are also dierent AR(3) ts and fractional Gaussians estimated from real data. We conclude that our proposed method is more powerful than simple correlation procedures, because of its robustness to variation in the HRF.
of Spanish word order Lexical and discourse factors in the second language acquisition

can be found at: Second Language Research Additional services and information for http://slr.sagepub. com/cgi/alerts Email Alerts: http://slr.sagepub. com/subscriptions Subscriptions: http://www.sagepub. com/journalsReprints.nav Reprints: http://www.sagepub. com/journalsPermissions.nav Permissions: http://slr.sagepub. com/cgi/content/refs/19/4/273 SAGE Journals Online and HighWire Press platforms): (this article cites 24 articles hosted on the Citations
Active learning for sensorimotor coordinations of autonomous robots

For a complex autonomous robotic system such as a humanoid robot, motor-babbling-based sensorimotor learning is considered an effective method to develop an internal model of the self-body and the environment autonomously. However, learning process requires much time for exploration and computation. In this paper, we propose a method of sensorimotor learning which explores the learning domain actively. Our approach discovers that the embodied learning system can design its own learning process actively, which is different from the conventional passive data-access machine learning. The proposed model is characterized by a function we call the “confidence”, and is a measure of the reliability of state control. The confidence for the state can be a good measure to bias the exploration strategy of data sampling, and to direct its attention to areas of learning interest. We consider the confidence function to be a first step toward an active behavior design for autonomous environment adaptation. The approach was experimentally validated in typical sensorimotor coordination such as arm reaching and object fixation, using the humanoid robot James and the iCub simulator.
Decreasing the cost of mutation testing with second‐order mutants

Although powerful, mutation is a computationally very expensive testing technique. In fact, its three main stages (mutant generation, mutant execution and result analysis) require many resources to be successfully accomplished. Thus, researchers have made important efforts to reduce its costs. This paper represents an additional effort in this sense. It describes the results of two experiments in which, by means of combining the original set of mutants and therefore obtaining a new set of mutants—each one with two faults—the number of mutants used is reduced to half. Results lead to believe that mutant combination does not decrease the quality of the test suite, whereas it supposes important savings in mutant execution and result analysis. Copyright © 2008 John Wiley & Sons, Ltd.
UNIFORMIZATION THEORY AND 2D GRAVITY I: LIOUVILLE ACTION AND INTERSECTION NUMBERS

This is the first part of an investigation concerning the formulation of 2D gravity in the framework of the uniformization theory of Riemann surfaces. As a first step in this direction we show that the classical Liouville action appears in the expression for the correlators of topological gravity. Next we derive an inequality involving the cutoff of 2D gravity and the background geometry. Another result, still related to uniformization theory, concerns a relation between the higher genus normal ordering and the Liouville action. We introduce operators covariantized by means of the inverse map of uniformization. These operators have interesting properties, including holomorphicity. In particular, they are crucial for showing that the chirally split anomaly of CFT is equivalent to the Krichever-Novikov cocycle and vanishes for deformation of the complex structure induced by the harmonic Beltrami differentials. By means of the inverse map we propose a realization of the Virasoro algebra on arbitrary Riemann surfaces and find the eigenfunctions for the holomorphic covariant operators defining higher order cocycles and anomalies which are related to W algebras. Finally we face the problem of considering the positivity of eσ, with σ the Liouville field, by proposing an explicit construction for the Fourier modes on compact Riemann surfaces. These functions, whose underlying number-theoretic structure seems related to Fuchsian groups and to the eigenvalues of the Laplacian, are quite basic and may provide the building blocks for properly investigating the long-standing uniformization problem posed by Klein, Koebe and Poincare.
Is Dark Energy Decaying?

We explore the fate of the universe given the possibility that the density associated with 'dark energy' may decay slowly with time. We model decaying dark energy by a homogeneous scalar field which couples minimally to gravity and whose potential has at least one local maximum. Dark energy decays as the scalar field rolls down its potential and this allows for several interesting possibilities including: (i) the current acceleration epoch can be a transient, (ii) a spatially flat universe ultimately collapses under the influence of a growing negative potential. (The second possibility is realized by the cosine potential.) We examine both possibilities by comparing our model universe with observations of high redshift type Ia supernovae. A maximum likelihood analysis reveals that such models are viable if the effective mass squared near the potential maximum is small (m ∼ H 0) and if the initial field displacement is not very large φ/M P < ∼ 1. The time left until the universe collapses is always larger than ∼ 22 Gyrs for Ω 0m = 0.3 and H 0 ≃ 70 km/sec/Mpc (at the 95.4% confidence level).
Una aproximación a la indexación semántica y a la recuperación de información

Este articulo presenta un acercamiento a los indices semanticos de topicos particulares asi como los mas importantes modelos para la recuperacion de informacion. Estos modelos seran construidos dinamicamente a travesde anotaciones identificadas en recursos de la web y en las definiciones de anotaciones ontologicas de los terminos utilizados. Los indices estan proyectados a sus agentes activos que ‘conocen' que topicos pueden manejar (e.j; encontrar el contenido para.) con base en sus propias ontologias.
Geometric rectification of high resolution satellite images using mathematical intelligent & classical modelling

In the recent decades, the determination and evaluation of geometrical correction models as well as georeferencing satellite images have been of great consideration due to their frequent use in various fields, and are regarded a leading topic in photogrammetry and remote sensing. This paper is about the geometric correction of the Worldview-2 satellite image using different modeling methods and tries to give an overall evaluation of strength of various possible modeling for a prototype image of an urban area like Tehran. The distribution and number of control points with regard to their effects in each modeling method were examined which resulted in a high precision of a final geometry correction about 0.36 meter using rational functions. For more optimization artificial intelligent methods like genetic algorithms and neural networks were used. With the use of perceptron network, a result of 0.84 pixels with 4 neurons in middle layer was gained and the final conclusion was that with these algorithms it is possible to optimize the existing models and have better results than usual ones.
Responses of Neurons in Cat Primary Auditory Cortex to Bird Chirps: Effects of Temporal and Spectral Context

The responses of neurons to natural sounds and simplified natural sounds were recorded in the primary auditory cortex (AI) of halothane-anesthetized cats. Bird chirps were used as the base natural stimuli. They were first presented within the original acoustic context (at least 250 msec of sounds before and after each chirp). The first simplification step consisted of extracting a short segment containing just the chirp from the longer segment. For the second step, the chirp was cleaned of its accompanying background noise. Finally, each chirp was replaced by an artificial version that had approximately the same frequency trajectory but with constant amplitude. Neurons had a wide range of different response patterns to these stimuli, and many neurons had late response components in addition, or instead of, their onset responses. In general, every simplification step had a substantial influence on the responses. Neither the extracted chirp nor the clean chirp evoked a similar response to the chirp presented within its acoustic context. The extracted chirp evoked different responses than its clean version. The artificial chirps evoked stronger responses with a shorter latency than the corresponding clean chirp because of envelope differences. These results illustrate the sensitivity of neurons in AI to small perturbations of their acoustic input. In particular, they pose a challenge to models based on linear summation of energy within a spectrotemporal receptive field.
Fast Mining of a Network of Coevolving Time Series

Coevolving multiple time series are ubiquitous and naturally appear in a variety of high-impact applications, ranging from environmental monitoring, computer network traffic monitoring, motion capture, to physiological signal in health care and many more. In many scenarios, the multiple time series data is often accompanied by some contextual information in the form of networks. In this paper, we refer to such multiple time series, together with its embedded network as a network of coevolving time series. In order to unveil the underlying patterns of a network of coevolving time series, we propose DCMF, a dynamic contextual matrix factorization algorithm. The key idea is to find the latent factor representation of the input time series and that of its embedded network simultaneously. Our experimental results on several real datasets demonstrate that our method (1) outperforms its competitors, especially when there are lots of missing values; and (2) enjoys a linear scalability w.r. t. the length of time series.
The Effects of Expressive Writing on Stress, Mood, and Perception of Self-Efficacy and of Instructor

THE EFFECTS OF EXPRESSIVE WRITING ON STRESS, MOOD, PERCEPTION OF SELF-EFFICACY AND INSTRUCTOR Sophia S. Tailor Old Dominion University, 2010 Director: Dr. Nina W. Brown The purpose of this study was to determine if emotional disclosure through expressive writing would have an effect on stress, mood, self-efficacy, and perception of instructor in a population of undergraduate human services students. This study used a randomized control-group pretest-posttest design with three experimental conditions. There were 32 participants with (N = 10) emotional disclosure group, (N = 11) factual disclosure group, and (N = 11) the control group. The study was conducted over three consecutive days following a variation of Pennebaker's (1986) expressive writing protocol. Study measures and writing samples were collected via a web-based interface. The data were analyzed using paired t-tests and a series of one-way Analysis of Variance for within group pretest differences on the study measures and Analysis of Covariance for the between group differences on the posttest measures with the pretest scores as the covariate. Within group, comparisons were conducted to evaluate if there was a significant difference between the pre-test and posttest scores on the dependent variables stress, mood, self-efficacy, and perception of instructor within each experimental group. The results of the paired t-test indicate there was no significant difference among the three groups on the pretest and posttest measures on the dependent variables. Between group comparisons were conducted to determine if there was a difference among the experimental groups on the mean scores of the pretest. No significant difference was found on the pretest measures of stress, mood, and perception of instructor. However, there was a significant difference on the pretest measure of self-efficacy. The post hoc analyses indicate that the significant difference was between the factual disclosure group and the control group. Finally, a series of ANCOVAS were conducted to explore the effect of expressive writing on the posttest scores of the dependent variables stress, mood, self-efficacy, and perception of instructor, while controlling for the pretest scores. The pretest scores were used as covariates in the analysis. The results of the ANCOVAS indicate there was no significant difference among the three groups on the posttest scores on the dependent variables stress, mood, self-efficacy, and perception of instructor.
Final Report 2015 National Summit: Early Childhood Special Education Inclusion

On October 7, 2015, 28 individuals representing 20 different U.S. states participated in a national, early childhood special education inclusion summit held in Atlanta, GA at DEC’s 31st Annual International Conference on Young Children with Disabilities and Their Families. Summit participants included researchers, faculty members, administrators, teachers, related services personnel, and families of children with special needs who have demonstrated leadership related to advancing preschool inclusion on the local, state, or national levels. The participants represented public and private programs and services. The summit format was a facilitated, focus group discussion in which participants shared their perceptions of the state of and future needs for leadership and action regarding preschool inclusion. This report summarizes the background and rationale, methods, and results. 1 Citation: LaRocco, D. J., & Barton, E. E. (2016, July). Final report 2015 national summit: Early childhood special education inclusion final report.
Anticipated regret in time-based work-family conflict

Purpose – The primary purpose of this research was to investigate the role of anticipated regret in time-based work-family conflict decisions. Design/methodology/approach – A total of 90 working parents responded to a decision making problem describing a time-based conflict between a work event and a family event. Participants' preference for which event to attend constituted the dependent variable. Independent variables were participants' work and family centralities. Anticipated regret for choosing the work option and anticipated regret for choosing the family option were measured as hypothesized mediators. Findings – Structural equation modeling revealed that anticipated regret for choosing the family option mediated the relationship between work centrality and preference for the family option. Similarly, it was found that anticipated regret for choosing the work option mediated the relationship between family centrality and preference for the family option. Originality/value – This article contributes.
Software architecture recovery and re-documentation tool of a Hospital Information System

The fact that the maintenance cost occupies almost two-thirds of the total cost for a software system is reported repeatedly in research literatures. The high cost of software maintenance can be attributed to some common maintenance problems such as outdated software documents and obscured software architecture. This paper proposes a method undertaken by a medical center located in southern Taiwan to recover software architecture and rebuild documentation of its Hospital Information System. The architecture and documentation will serve as the foundation of migrating applications from legacy HIS systems to a component-based architecture. Our research starts with defining maintenance-oriented software architecture by studying system documents and interviewing experienced software analysts. The architectural information is then complemented by analyzing source code to address the incorrect system document issues. The recovered architectural information is stored into a repository in a top-down fashion. As we reconstruct the architecture, software developers need to provide additional descriptions to complete the documentation. A software architecture management system is developed to assist software developers in querying and maintaining software system documentation. The recovered architectural information could be used to evaluate change impacts, to extract activities of specific function such as database access and program linking, to discover relationship between user roles and functional items, and to estimate maintenance cost in the future.
Three components for large scale image retrieval

This paper introduces a novel framework for large-scale image retrieval. This work is distinguished by three major contributions. The first is a new global image descriptor called Block HSV Histogram (BHSVH), which captures both the color statistics and color space distribution information. The second is that we propose an efficient filtering process that uses a hashing algorithm to index the original features. Using a hashing algorithm is indeed a promising method for large-scale retrieval. The third is that we propose a new distance measure for re-ranking retrieved images. We conducted extensive experiments to show that the proposed image retrieval framework can scale to very large-scale image database.