Miracle-dz/UniFAR-FTUs huggingface dataset:
DISABILITIES 1 Speech Perception in Children with Reading Disabilities : Phonetic Processing is the Problem

Purpose: Reading disability (RD) is a key obstacle in the development of literacy, and studies show that 15-20% of grade-school students have an RD. The current study examines one potential source of RD in young children (8-12 years old): inadequate open set, non-categorical speech processing abilities, which may be related to early language development. Method: We present data from two tasks: (1) A 3-interval forced choice procedure called the Syllable Confusion Oddball (SCO) task, which examines the listener’s ability to identify different syllables (CV/VC) from a string of three such syllables, spoken by three different talkers, selected from a database of 18 adult mixed-gender talkers. (2) A single CV/VC presentation task with a verbal response, denoted the Nonsense Syllable Confusion Matrix (NSCM) task, where the listener labels individual utterances of CV and VC sounds. Ten children having well-documented RDs, normal hearing, and normal language function completed the tasks, and their performance was compared to that of six reading control (RC) children with no RD. The consonants and vowels each had 4.2 bits of entropy (19 vowels and 19 consonants), providing a sufficient range of responses to investigate perceptual confusions and differences in error rates. Results: For the SCO task, the proportion of errors was significantly higher for the RD listeners compared with the RC listeners; the RD listeners had, on average, three to five times as many errors as the RC listeners. These errors were also highly idiosyncratic, with differences between individual subjects in the errors they made as a function of phone type (consonant vs. vowel) and syllable position (initial vs. final). Results from the NSCM task revealed individual differences in the confusion patterns for these errors. Conclusions: Three main conclusions can be drawn from the results:(1) RD children have a significant speech perception problem in identifying open set syllables, despite normal pure-tone hearing and language processing abilities. (2) For the NSCM task, the errors were somewhat reduced, indicating that the single-interval task was easier than the three-interval task. (3) These results are at odds with previous work which shows no indication of phoneme identification impairment in RD children. However, previous studies were significantly limited in the number of trials, the number of speech sounds that were tested, and the type and extent of trials used.
Human Face Recognition System Using PCA

Face detection is the process of identifying a human face where as face recognition is the process of verification of any known person’s face from the system’s database.Thus human face detection and recognition is the process of identification and verification of a known person’s face by providing input of that person’s facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification, terrorist detection and so on. Human face is a complex multidimensional structure and needs a good computing techniques for the recognition.Our approach treats face recognition as a two dimensional recognition problem.In this research paper face recognition is done by Principal Component Analysis (PCA) algorithm. This is a personal identification system that uses personal characteristics of a person’s face to identify that person’s face.In this paper the implementation is also done.
Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting

We propose an automatic approach to anatomy partitioning on three-dimensional (3D) computed tomography (CT) images that divides the human torso into several volumes of interest (VOIs) according to anatomical definition. In the proposed approach, a deep convolutional neural network (CNN) is trained to automatically detect the bounding boxes of organs on two-dimensional (2D) sections of CT images. The coordinates of those boxes are then grouped so that a vote on a 3D VOI (called localization) for each organ can be obtained separately. We applied this approach to localize the 3D VOIs of 17 types of organs in the human torso and then evaluated the performance of the approach by conducting a four-fold crossvalidation using a dataset consisting of 240 3D CT scans with the human-annotated ground truth for each organ region. The preliminary results showed that 86.7% of the 3D VOIs of the 3177 organs in the 240 test CT images were localized with acceptable accuracy (mean of Jaccard indexes was 72.8%) compared to that of the human annotations. This performance was better than that of the state-of-the-art method reported recently. The experimental results demonstrated that using a deep CNN for anatomy partitioning on 3D CT images was more efficient and useful compared to the method used in our previous work.
Modelling and Forecasting Fiscal Policy and Economic Growth in Nepal

This paper develops a macroeconomic forecasting model focusing on fiscal policy and economic growth in Nepal. The structure of the model, which comprises a total of 14 equations, allows alternative policy options for maintaining fiscal stability and promoting economic growth as well as switching deficit financing between domestic and foreign loans. We use annual data from 1992/93 to 2009/10 to estimate the model and provide out-sample forecasts for 2010/11 to 2012/13, consistent with the current Three Year Plan period, in order to evaluate the plan performance. The empirical evidence suggests that fiscal policy, particularly governments' capital expenditure affects economic growth positively and also crowds-in private investment. However, there exists a trade-off between fiscal stability and high level of economic growth as the policy goal of achieving both objectives seems to be unattainable. Finally, the out-sample forecast suggests that it is unlikely to attain the targeted economic growth in the Three Year Plan period from the planned fiscal outlay even if it is realized.
An Analysis of the Motivation of Customer Participation Value Co-Creation in the We-Media: A Study Based on Content Marketing

The value co-creation of customer participation is the prerequisite for the company to achieve commercial value from the we-media. This paper starts from the content of the we-media environment elements to reveal the mechanism of value co-creation of customers’ participation in we-media, and helps we-media managers to deliver the content customers need scientifically and efficiently, to increase their willingness to participate, and to realize value co-creation. Based on 227 valid questionnaires, this paper uses SPSS 22.0 and AMOS17.0 to analyze the data and test the hypothesis. The empirical results show that: Practical Content and Interesting Content significantly positively affect the value co-creation of autonomous customer participation; Practical Content and Personalized Content significantly positively influence the Sponsored Value Co-creation. At the same time, Autonomous Value Co-creation also has a significant positive impact on the Sponsored Value Co-creation, and Autonomous Value Co-creation plays a role as a partial intermediary in the Sponsored Value Co-creation by Practical Content, and it is initiated in Interesting Content, the role of co-creation of value plays a role of a complete intermediary.
Bandits in the Plackett-Luce Model

We introduce the probably approximately correct (PAC) Battling-Bandit problem with the PlackettLuce (PL) subset choice model–an online learning framework where at each trial the learner chooses a subset of k arms from a fixed set of n arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top m most preferred items etc. The objective is to identify a near-best item in the underlying PL model with high confidence. This generalizes the well-studied PAC Dueling-Bandit problem over n arms, which aims to recover the best-arm from pairwise preference information, and is known to require O( n 2 ln 1 δ ) sample complexity (Szörényi et al., 2015; Busa-Fekete et al., 2013). We study the sample complexity of this problem under various feedback models: (1) Winner of the subset (WI), and (2) Ranking of top-m items (TR) for 2 ≤ m ≤ k. We show, surprisingly, that with winner information (WI) feedback over subsets of size 2 ≤ k ≤ n, the best achievable sample complexity is still O ( n 2 ln 1 δ ) , independent of k, and the same as that in the Dueling Bandit setting (k = 2). For the more general top-m ranking (TR) feedback model, we show a significantly smaller lower bound on sample complexity of Ω ( n m 2 ln 1 δ ) , which suggests a multiplicative reduction by a factor m owing to the additional information revealed from preferences among m items instead of just 1. We also propose two algorithms for the PAC problem with the TR feedback model with optimal (upto logarithmic factors) sample complexity guarantees, establishing the increase in statistical efficiency from exploiting rank-ordered feedback.
Asymptotic statistical properties of communication-efficient quickest detection schemes in sensor networks

Abstract The quickest change detection problem is studied in a general context of monitoring a large number K of data streams in sensor networks when the “trigger event” may affect different sensors differently. In particular, the occurring event might affect some unknown, but not necessarily all, sensors and also could have an immediate or delayed impact on those affected sensors. Motivated by censoring sensor networks, we develop scalable communication-efficient schemes based on the sum of those local cumulative sum (CUSUM) statistics that are “large” under either hard, soft, or order thresholding rules. Moreover, we provide the detection delay analysis of these communication-efficient schemes in the context of monitoring K independent data streams and establish their asymptotic statistical properties under two regimes: one is the classical asymptotic regime when the dimension K is fixed, and the other is the modern asymptotic regime when the dimension K goes to Our theoretical results illustrate the deep connections between communication efficiency and statistical efficiency.
Self-ligation shortens chair time and compounds savings, with external bracket hygiene compared to conventional ligation: Systematic review with meta-analysis of randomized controlled trials

Objective: To test if there are significant evidence-based differences in effectiveness between self-ligation (SL) and conventional-ligation (CL) brackets. Materials and Methods: Popular clinical claims of SL were identified through a literature overview of PubMed, EMBASE, Cochrane Library, and Web of Science for the period 1965-2017. Additional hand searching of the references from retrieved articles was completed. The articles containing the inclusion criteria were qualitatively analyzed using the Cochrane risk of bias tool, and one other scale. Applicable RCTs were statistically analyzed with weighted means calculations and forest plots. RCT data that could not be synthesized with one other RCT at this time were reserved for discussion. Results: The inclusion criteria were satisfied by a total of ten RCT studies, six of which were matched for meta-analysis of three popular clinical claims. Space closure rate, reduced incisor proclination, and the rate of mandibular alignment for SL compared to CL were not statistically significant with confidence intervals of 95%. The remaining four RCTs were collectively analyzed and found no statistically significant difference in discomfort between SL and CL. Conclusion: The null hypothesis that there are no differences between SL and CL, was not rejected due to statistically insignificant results. Additional active SL studies, and well-designed RCTs for MA are needed that includes overall treatment time. SL chair time efficiency was consistently higher versus CL. *Correspondence to: John Voudours (Hon) DDS, DOrth, MSc(D) University of Toronto, Discipline of Orthodontics New York University, Division of Biological Sciences AAO Milo Hellman Research Award Recipient 1988 Member Edward H Angle East Society of Orthodontists, Canada, E-mail: jvoud@me.com; jvoud@pathcom.com
DIFF: a relational interface for large-scale data explanation

A range of explanation engines assist data analysts by performing feature selection over increasingly high-volume and high-dimensional data, grouping and highlighting commonalities among data points. While useful in diverse tasks such as user behavior analytics, operational event processing, and root-cause analysis, today’s explanation engines are designed as stand-alone data processing tools that do not interoperate with traditional, SQL-based analytics workflows; this limits the applicability and extensibility of these engines. In response, we propose the DIFF operator, a relational aggregation operator that unifies the core functionality of these engines with declarative relational query processing. We implement both single-node and distributed versions of the DIFF operator in MB SQL, an extension of MacroBase, and demonstrate how DIFF can provide the same semantics as existing explanation engines while capturing a broad set of production use cases in industry, including at Microsoft and Facebook. Additionally, we illustrate how this declarative approach to data explanation enables new logical and physical query optimizations. We evaluate these optimizations on several real-world production applications and find that DIFF in MB SQL can outperform state-of-the-art engines by up to an order of magnitude.
IL-1 RI deficiency ameliorates early experimental renal interstitial fibrosis

Background. IL-1β has the potential to promote progressive renal disease by effects on macrophage recruitment and activation or by effects mediated through tubular cell transforming growth factor (TGF)-β production, previously demonstrated in vitro. Methods. The in vivo roles of endogenous IL-1β and its type I receptor (IL-1RI) in renal fibrosis were studied using wild-type C57BL/6 mice, IL-1β−/− and IL-1RI−/− mice with unilateral ureteric obstruction. Results. After 7 days, IL-1RI−/− mice (IL-1α and IL-1β deficient) were protected from injury and collagen accumulation. IL-1β−/− mice demonstrated some histological protection, but no reduction in α1(1) procollagen mRNA or biochemically measured collagen accumulation. Compared with obstructed kidneys from wild-type mice, TGFβ1 mRNA was reduced in IL-1RI−/− mice (with trends to reduced TGF-β2 and TGF-β3). Expression of a downstream TGF-β effector, connective tissue growth factor, was decreased in IL-1RI−/− mice. IL-1RI−/− mice exhibited less tubulointerstitial apoptosis compared with wild-type mice. Macrophage infiltration and adhesion molecule mRNA expression was unchanged in IL-1β−/− or IL-1RI−/− mice. While TNF expression was similar to wild-type mice, IFNγ expression was reduced in both IL-1β−/− and IL-1RI−/− mice. IL-1RI−/− mice at 14 days showed a catch-up in fibrosis compared with wild-type mice. Conclusion. IL-1/IL-1RI interactions are profibrotic in renal fibrosis. IL-1RI−/− mice were more protected at an early stage, associated with changes in TGF-β and downstream mediators of fibrosis, but independent of the presence of infiltrating macrophages.
Classification des fractures des os longs : concepts et utilité

Le traitement des fractures des os longs fait partie integrante des tâches quotidiennes d'un service d'orthopedie et peut representer une part importante de son activite. Le choix de la strategie therapeutique est influence par de nombreux facteurs plus ou moins objectifs allant de l'experience de l'operateur aux recommandations basees sur des preuves scientifiques, en incluant des facteurs lies au patient et aux ressources du service. Cependant, la decision part toujours initialement de la fracture, qui est decrite, puis categorisee et incluse dans une classification. L'objectif de ce travail est de developper le concept de classification des fractures en soi, d'en identifier les caracteristiques, l'utilite et les limitations, et d'evoquer les pistes de developpements futurs qui devraient permettre d'ameliorer la prise en charge des patients victimes de fracture.
Competitive ratio vs regret minimization: achieving the best of both worlds

We consider online algorithms under both the competitive ratio criteria and the regret minimization one. Our main goal is to build a unified methodology that would be able to guarantee both criteria simultaneously. For a general class of online algorithms, namely any Metrical Task System (MTS), we show that one can simultaneously guarantee the best known competitive ratio and a natural regret bound. For the paging problem we further show an efficient online algorithm (polynomial in the number of pages) with this guarantee. To this end, we extend an existing regret minimization algorithm (specifically, Kapralov and Panigrahy (2011)) to handle movement cost (the cost of switching between states of the online system). We then show how to use the extended regret minimization algorithm to combine multiple online algorithms. Our end result is an online algorithm that can combine a “base” online algorithm, having a guaranteed competitive ratio, with a range of online algorithms that guarantee a small regret over any interval of time. The combined algorithm guarantees both that the competitive ratio matches that of the base algorithm and a low regret over any time interval. As a by product, we obtain an expert algorithm with close to optimal regret bound on every time interval, even in the presence of switching costs. This result is of independent interest.
Explaining informal entrepreneurship in South-East Europe: a tax morale approach

Conventionally, entrepreneurs participating wholly or partially in the informal economy were explained as rational economic actors doing so when the benefits outweigh the costs. However, the finding that many entrepreneurs do not operate in the informal economy even when the pay-off is greater than the costs has led to the emergence of a new tax morale approach. Grounded in institutional theory, this asserts that formal institutional problems lead to an asymmetry between the laws and regulations, and entrepreneurs' views on the acceptability of informality. The greater this asymmetry (measured by the level of tax morale), the greater is the prevalence of informal entrepreneurship. To evaluate this, interviews with a nationally representative sample of 1,430 entrepreneurs in Bulgaria, Croatia and FYR Macedonia are reported. Using an ordered logit model, the contribution of this paper is to reveal that entrepreneurs operating in the informal economy have significantly lower tax morale, and that the formal institutional problems associated with lower tax morale include a lack of tax fairness, corruption and political instability. The theoretical and policy implications are then discussed.
’ s repository of research publications and other research outputs What ’ s going on in my city ? Recommender systems and electronic participatory budgeting Conference or Workshop Item

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area.
Topic based engagement analysis of an online social service

The content on the web is increasing at an explosive rate. This makes it important to generate interpretations of web content in a way that could facilitate building of relevant systems. Keeping this notion in focus, an analysis is carried out to understand and generate interpretations of engagement of users with respect to topics discussed on an online social service [5] (here a hacker forum). Since this could be the first research of its kind, results are presented in a way that may be easily interpreted and used for carrying out further research. A number of existing machine learning techniques and models are used in this research. There is a section that points out the usefulness of these results in encouraging user participation.
Interpreting variation in fish-based food web indicators: the importance of “bottom-up limitation” and “top-down control” processes

Proposed indicators for the Marine Strategy Framework Directive (MSFD) food webs Descriptor focus on structural elements of food webs, and in particular on the abundance and productivity of top predators. However, the inferences that can be drawn from such indicators depend on whether or not the predators are “bottom-up limited” by the availability of their prey. Many seabird populations appear to be “bottom-up limited” so that variation in their reproductive success and/or abundance reflects changes in lower trophic levels. Here we find that gadoid fish predators off the Firth of Forth, southeast Scotland, do not appear to be “bottom-up limited” by the biomass of their main prey, 0-group sandeels; gadoid biomass and feeding performance was independent of sandeel biomass. Variability in food web indicators based on these gadoid predators seems to impart little insight into underlying processes occurring at lower trophic levels in the local food web. The implications of this in terms of how the currently proposed MSFD food web indicators should be used and interpreted are considered, and the ramifications in terms of setting targets representing good environmental status for both fish and seabird communities are discussed.
Uniform Sampling of SAT Solutions for Configurable Systems: Are We There Yet?

Uniform or near-uniform generation of solutions for large satisfiability formulas is a problem of theoretical and practical interest for the testing community. Recent works proposed two algorithms (namely UniGen and QuickSampler) for reaching a good compromise between execution time and uniformity guarantees, with empirical evidence on SAT benchmarks. In the context of highly-configurable software systems (e.g., Linux), it is unclear whether UniGen and QuickSampler can scale and sample uniform software configurations. In this paper, we perform a thorough experiment on 128 real-world feature models. We find that UniGen is unable to produce SAT solutions out of such feature models. Furthermore, we show that QuickSampler does not generate uniform samples and that some features are either never part of the sample or too frequently present. Finally, using a case study, we characterize the impacts of these results on the ability to find bugs in a configurable system. Overall, our results suggest that we are not there: more research is needed to explore the cost-effectiveness of uniform sampling when testing large configurable systems.