Miracle-dz/UniFAR-FTUs huggingface dataset:
A NON-INTRUSIVE SPEECH QUALITY ASSESSMENT MODEL BASED ON DNN

Objective speech quality assessment tools are vital for evaluating speech processing devices and algorithms. It is well-known that most of the existing objective speech quality assessment tools, such as perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio, and log-spectral distance, rely on the knowledge of the clean speech, which is not always available. In this paper, we study to predict the PESQ score without clean speech as the reference by using Deep Neural Network (DNN). Both the regression-based and classiﬁcation-based methods are considered in training the DNN model. In the regression training, all frame-based features extracted from each sentence are mapped to only one PESQ score, which is estimated by the PESQ tool recommended by ITU-T. In the classiﬁcation training, the PESQ score ranging from -0.5 to 4.5 is equally divided into 100 subintervals, and then all frame-based features extracted from one sentence are mapped to one part of these subintervals. In the training phase, the TIMIT database are chosen as the clean speech signals, which are mixed with different types of noises at different levels of SNR to cover all the range of PESQ score. Experimental results show that the proposed DNN-based PESQ prediction is promising to provide a non-intrusive speech quality assessment for practical applications.
Time-gating technique for a single-photon detection-based solid-state time-of-flight 3D range imager

Solid-state 3D range imagers have attracted interest in recent years due to their robustness and the possibilities for achieving small and inexpensive realization, which is often required in fields of technology such as robotics, gaming, vehicle safety and hand-held devices, for example. Time-offlight (TOF) techniques that illuminate the scene with a laser source and detect the backscattered light by means of a single-photon avalanche diode (SPAD) array implemented in complementary metal oxide semiconductor (CMOS) technology have the potential to achieve these requirements with low optical illumination power. This thesis is concerned with the development of a solid-state 3D range imager based on use of the sliding time-gate technique in a SPAD array and short (~200 ps), intensive laser pulses. The area of the in-pixel electronics needed in time-gated imagers is small, which leads to a high fill factor and a possibility for implementing large arrays. The use of short laser pulses increases the precision and frame rate, since depth measurement can be limited to the range of interest, e.g. around the surface of the target. To increase the frame rate further, the array can be divided into subarrays with independently defined ranges. Tolerance of high background light is achieved by using sub-ns time-gate widths. A SPAD array of 80 x 25 pixels is developed and realized here. The array is divided into 40 subarrays, the narrow (<0.8 ns) time-gating positions for which can be set independently. The time-gating for each of the subarrays is selected separately with an on-chip DLL block that has 240 outputs and a delay grid of ~100 ps. The fill factor of the sensor area is 32%. A 3D range image measurement at ~10 frames per second with centimetre-level precision is demonstrated for the case of passive targets within a range of ~4 metres and a field of view of 18 × 28 degrees, requiring an average active illumination power of only 0.1 mW. A frame rate of 70 range images per second was achieved with a higher laser average illumination power (~5 mW) and pulsing rate (700 kHz) when limiting the scanning range for each subarray to 30 cm around the surfaces of the targets. An FPGA-based algorithm which controls the time-gating of the SPAD array and produces the range images in real time was also developed and realized.
MODERN STATISTICAL ANALYSIS OF FORAGE QUALITY ASSESSMENT WITH NIR SPECTROSCOPY

Recently, the statisticians have developed a new approach called functional Statistics to treat the data as curves or images. In parallel, the Near-Infrared Reflectance (NIR) spectroscopy approach has been used in modern Chemistry being a fast, inexpensive and accurate procedure to characterize chemical properties for an object. In this paper, we study the forage quality by analysing the spectroscopy procedure with some modern statistical models. Our contribution leads to the prediction of chemical components of Chinese ryegrass forage by analysing its spectral data using some functional models. Precisely, the functional linear quantile regression (FLQR), the functional nonparametric quantile regression (FNQR), the functional local linear quantile regression (FLLQR) and the functional local linear model regression (FLLMR) are implemented to predict the quantities of acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP) contents. The choice of these functional models is motivated by the fact that they can construct a predictive region with a given confidence level. We show that the considered models improve the prediction results significantly as compared to conventional models such as the classical partial least squares regression (PLSR) and the principal component regression (PCR). Moreover, we also show that the proposed models are more robust than their competitive models like PLSR and PCR in the sense that their efficiency is not much affected by non-homogeneity of the data.
Adaptive Learning and the Transmission of Government Spending Shocks in the Euro Area

This paper analyses the transmission of government spending shocks in an estimated dynamic stochastic general equilibrium model for the euro area when agents use forecasting models updated by the Kalman filter to form expectations. Based on the marginal likelihood criterion, there is evidence in favour of this Kalman filter learning mechanism relative to rational expectations. Moreover, under Kalman filter learning, the transmission of government spending shocks varies over time. This variation stems from the adjustment of the beliefs of the agents on their forecasting model. Hence, this adjustment process provides an endogenous explanation for time-varying government spending multipliers. We find that, in contrast to rational expectations, the responses of private consumption to a government spending shock is positive for most periods in the sample. Moreover, the government spending multiplier for output is substantially larger under learning than under rational expectations.
Electrical Load Forecasting in R

Due to the liberalization of the European energy markets, electrical load forecasting became very important. To achieve accurate medium term forecasting on an hourly basis, forecast models that integrate previous consumptions as well as exogenous variables (like temperature) are needed. Currently used approach is a ’Two stage modelling in electrical load forecasting, with application to customer management by power distribution utilities’ (Amberti et al, 2006) that uses an autoregressive model with external regressors and a day types approach. This work focuses on model selection especially in terms of forecasting performance as a requirement for usability in a production environment. Solution’s implementation is done through time series modelling and forecasting libraries, clustering and model selection R’s features as well as original contributions in R.
IMPROVED LOSSLESS DATA HIDING SCHEME USING HISTOGRAM OF ADJACENCY PIXEL DIFFERENCE

There have been discussed several data hiding techniques in an image which can embed the data in secret manner. However, after extracting the data from animage, theoriginal image information cannot be recovered. It is an important issue to have a lossless data hiding scheme which can recover the original image without any loss after extracting the secret data. In this paper, a novel based idea is introduced for lossless data hiding using adjacencypixel difference.In this method, we can obtain good amount of security and good amount of data hiding capacity.
Forms of Self-Criticizing and Self-Reassuring Scale: Adaptation and Validation in a Sample of Portuguese Children

Self-criticism may be understood as a harmful self-evaluation that implies feelings of uselessness and guilt towards perceptions of failure. Goals: to explore the latent structure and other psychometric properties of the Forms of Self-Criticizing and Reassuring Scale for Children (FSCRS -C) of the Portuguese population. Method: the FSCRS-C was administered to 249 Azorean children. Results: a two-dimensional model was confirmed including the Self-criticism and Self-reassuring subscale, which presented good internal consistency. The FSCRS -C presented good divergent and convergent validity. Conclusion: 1 Faculty of Social and Human Sciences, University of Azores, Portugal / Center for Research in Neuropsychology and Cognitive Behavioral Intervention (CINEICC), Faculty of Psychology and Education Sciences, University of Coimbra, Portugal. Email: celia.mo. carvalho@uac.pt. ORCID ID: https://orcid.org/0000-0003-4453-8139 2 Cognitive Behavioral Intervention (CINEICC), Faculty of Psychology and Education Sciences, University of Coimbra, Portugal. Email: joana.benevides@hotmail.com. ORCID ID: https://orcid.org/0000-0002-6865-3302 3 Faculty of Social and Human Sciences, University of Azores, Portugal / Cognitive Behavioral Intervention (CINEICC), Faculty of Psychology and Education Sciences, University of Coimbra, Portugal. Email: carolina.d. motta@uac.pt. ORCID ID: https://orcid.org/0000-0001-8421-2956 4 Faculty of Social and Human Sciences, University of Azores, Portugal. Email: joana.m. cabral@uac.pt. ORCID ID: https:// orcid.org/0000-0003-1717-0965 5 Faculty of Social and Human Sciences, University of Azores, Portugal. Email: carolinapereira_1993@hotmail.com. ORCID ID: https://orcid.org/0000-0003-4519-8308 6 Faculty of Social and Human Sciences, University of Azores, Portugal. Email: as3639712@sapo.pt. ORCID ID: https:// orcid.org/0000-0003-0501-0593 7 Faculty of Social and Human Sciences, University of Azores, Portugal. Email: ermelindo.mb. peixoto@uac.pt. ORCID ID: https://orcid.org/0000-0001-5768-9556 Artigo recebido a 13-06-2018 e aprovado a 21-08-2018 Forms of Self-Criticizing and Self-Reassuring Scale: Adaptation and Validation in a Sample of Portuguese Children • pág. 61-74 DOI: https://doi.org/10.14195/1647-8606_62-2_4 62 Célia Barreto Carvalho, Joana Benevides, Carolina da Motta, Joana Cabral, Carolina Pereira, Marco Teixeira & Ermelindo Peixoto the adapted version for children provides a robust measure to be used in studies aiming at the study of the development of this construct.
Optimizing Search Relevance for Enterprise Recruiting

Workday’s recruiting product is used by recruiters to ﬁnd candidates that best match a given job requisition and by candidates to ﬁnd jobs that match their proﬁles. Surfacing precise results, in addition to good ranking, is a critical product need. We have integrated several custom–built Named Entity Recognition (NER) models as part of query pre–processing to dramatically improve precision (and ranking) of search results. Furthermore, Workday is a multi–tenanted system with strong privacy and isolation guarantees. Workday does not have direct access to tenant data, including query data. This makes traditional approaches to NER training and validation impossible. We propose a novel scheme to train, validate, and calibrate our models for every one of our over eight hundred tenants. We show that our best models achieve signiﬁcant error reduction compared to baseline NER models.
How to beat the best Android UI testing tools? An infrastructure approach

Automated User Interface (UI) testing has been widely used for quality assurance during Android app development. Numerous Android UI testing tools have emerged from both the research community and the industry after years of development. Monkey , a randomized Android UI input generator developed by Google, is one of the earliest efforts in this direction. Authors of various tools developed after Monkey have all claimed that their tools can perform superiorly to Monkey based on more sophisticated app modeling and analysis; however, recent work [1, 2] shows that many of these tools barely outperform Monkey, despite their more advanced design. The arising question is how to improve these testing tools to achieve better testing effectiveness in practice?
Linking theory with practice and student projects with applied research: case study in Detroit

The Detroit Studio is a community outreach program of Lawrence Technological University’s College of Architecture and Design. It offers students and faculty the opportunity to link theory with practice, academia with the profession, and student projects with applied research. Architecture students work with stakeholders, participating in environmental and behavioral research for design projects. The project discussed here involved seventh grade students in Detroit’s underserved communities who worked with junior architecture students to develop master plans for the community. The paper argues that although children are certainly considered in underserved communities, they rarely actively influence revitalization. While literature suggests that urban design benefits architectural education, urban design is underutilized. Although there is research investigating children’s involvement in small projects, it rarely addresses the role of children in placemaking in the large built environment. Therefore, children play an important role in this project. In this project, a multiphase approach to incorporating children in placemaking was applied with architecture students leading teams of about four children. Phase 1 consists of inquiry by impression through kids’ video cameras, as they made observations through videos. Phase 2 examines formal pre-understandings, where children explore environmental variables and theoretical constructs. Phase 3 consists of therapeutic art exercises that help children transition to subsequent phases. Phase 4 includes exercises where children construct models of their ideal community. Phase 5 involves producing videos to educate the community about the emerging lessons. Phase 6 develops the children’s ideas into master plan concepts. Research is conducted on several themes central to the children’s ideas. Phase 7 incorporates the children’s input and research outcomes into specific community master plan strategies. Social construction, as a primary theoretical base for our project, has guided our efforts through all phases in the project. Conference theme: Collaborative and interdisciplinary research, education, and design
THE ALTERNATIVE LEGENDRE TAU METHOD FOR SOLVING NONLINEAR MULTI-ORDER FRACTIONAL DIFFERENTIAL EQUATIONS

In this paper, the alternative Legendre polynomials (ALPs) are used to approximate the solution of a class of nonlinear multi-order fractional differential equations (FDEs). First, the operational matrix of fractional integration of an arbitrary order and the product operational matrix are derived for ALPs. These matrices together with the spectral Tau method are then utilized to reduce the solution of the mentioned equations into the one of solving a system of nonlinear algebraic equations with unknown ALP coefficients of the exact solution. The fractional derivatives are considered in the Caputo sense and the fractional integration is described in the Riemann-Liouville sense. Numerical examples illustrate that the present method is very effective for linear and nonlinear multi-order FDEs and high accuracy solutions can be obtained only using a small number of ALPs.
ORIGINAL COMMUNICATIONS Calculating energy requirements for men with HIV=AIDS in the era of highly active antiretroviral therapy

Objectives: 1. To determine if resting energy expenditure (REE) adjusted for body composition is elevated in HIV-positive males when compared with healthy controls in the era of highly active antiretroviral therapy. 2. To examine the accuracy of prediction equations for estimating REE in people with HIV. 3. To determine if REE adjusting for body composition is significantly different between those HIV-positive subjects reporting lipodystrophy (LD) or weight loss (� 5%) and those who are weight stable when compared to controls. Design: Cross-sectional study. Setting: Tertiary referral hospital HIV unit and an outpatient clinic specializing in HIV care. Subjects: HIV-positive males (n ¼ 70) and healthy male controls (n ¼ 16). Methods: REE was measured using indirect calorimetry. Body composition was assessed using bioelectrical impedance analysis. Results: 1. REE when adjusted for fat-free mass and fat mass using the general linear model (analysis of covariance) was greater in HIV-positive subjects than controls (7258 � 810 kJ, n ¼ 70 vs 6615 � 695 kJ, n ¼ 16, P < 0.05). 2. The Harris and Benedict, Schofield, Cunningham and the two equations previously published by Melchior and colleagues in HIV-positive subjects all gave an estimate of REE significantly different from the measured REE in the HIV-positive subjects, therefore a new prediction equation was developed. The inability of the published equations to predict REE in the different HIV-positive subgroups reflected the heterogeneity in body composition. 3. REE adjusted for fat-free and fat mass was significantly greater in the both the HIV patients who were weight stable and those with lipodystrophy compared with the healthy controls. Conclusion: REE is significantly higher in HIV-positive males when compared with healthy controls. Body composition abnormalities common in HIV render the use of standard prediction equations for estimating REE invalid. When measuring REE in HIV-positive males adjustment steps should include fat-free and fat mass. European Journal of Clinical Nutrition (2003) 57, 209 ‐ 217. doi:10.1038=sj.ejcn. 1601536
ADAPTIVE AND CLASSICAL TECHNIQUES FOR PREDICTING AND FORECASTING TIME SERIES: CRITERIA COMPARISON

This study applies and compares classical approaches, specifically Akaike’s Information Criterion (AIC), Akaike’s Corrected Information Criterion (AICC) and Bayesian Information Criterion (BIC) and an extension of the well known multimodel partitioning algorithm (MMPA) to the time series prediction problem. The time series data is real and represents the monthly thunderstorm days, covering the period from 01/1980 to 12/2005. The data has been collected from the National Meteorological Authority of Hellas. Comparison shows that all the methods produce satisfactory results with the adaptive MMPA having the best performance amongst the compared algorithms.
The Impact of Barrier Layers on Arabian Sea Surface Temperature Variability

Barrier layers (BLs) in the Arabian Sea may lead to increased mixed layer (ML) warming or cooling relative to non‐BL conditions, depending on differences in ML properties and atmospheric forcing. We use 13 years of profiling float data from the Arabian Sea to identify when and where each impact is likely based on analysis of the ML temperature tendency. We use a novel method based on the vertical spice profile to identify BLs and their characteristics. BLs are most likely to increase warming during the northeast monsoon, owing to temperature inversions, preceding the development of the miniwarm pool and onset of the southwest monsoon. This effect is nonuniform and is counteracted in some regions by increased penetration of solar radiation below the shallower ML associated with BL. Relative rates of cooling increase in the presence of BL during the southwest monsoon; intermonsoon periods show little net effect from BL.
On the Relation between S&P 500 Options and VIX Derivatives

This paper introduces the variance parity that must hold between S&P 500 (SPX) options and VIX derivatives. Exploiting this parity condition, I find that VIX derivatives contribute more to the price discovery of volatility risk than SPX options, even before the 2008 financial crisis when the former were thinly traded. Additionally, I find that consistent with the theory of limited arbitrage, dislocations between SPX option and VIX derivative prices are associated with measures of funding and market liquidity, even after the peak of the crisis is excluded. Specifically, as liquidity conditions deteriorate, investors appear willing to pay premiums for VIX futures relative to SPX options.
Qualitative Methods in Rehabilitation Research and their Relevance to Rehabilitation Counselling Practice

This paper discusses the potential limitations, and barriers to acceptance, of qualitative research methodologies. Qualitative research has been dismissed for consisting of small unrepresentative samples that limit the generalisability of findings, for lacking reliability and validity, for providing analyses that mask the individual differences that it purports to highlight, and for being too subjective. It was argued that these criticisms have to be considered against a different set of criteria to those applied to quantitative research. Moreover, the rationale behind qualitative research can provide rehabilitation counsellors with a better understanding of living with disability. This paper seeks to encourage rehabilitation counsellors to (a) gain insight into the different perspectives of persons with disabilities; (b) develop their clinical or knowledge base; and (c) be self-reflexive and critically self-examine their interaction with clients.
A multi-model approach to fatigue crack growth monitoring and prediction

In this paper an efficient multi-model partitioning algorithm (MMPA) for parameter identification, the so-called Adaptive Lainiotis Filter (ALF), is applied to the problem of Fatigue Crack Growth (FCG) monitoring and identification in order to improve the prediction of the final crack or residual time to failure. The MMPA and Extended Kalman Filter (EKF) algorithms are both tested in order to compare their efficiency. Through extensive analysis and simulation it is demonstrated that the MMPA has superior performance both in parameter identification, as well as, in predicting the remaining lifetime to failure. Furthermore it is shown that the MMPA is fast when implemented in a parallel/distributed-processing mode and it is more robust and converges sooner than the augmented EKF.
Bank Competition and Risk Taking in a Zero Interest Environment

How do near-zero interest rates affect bank competition, risk taking and financial regulation? I study these questions in a tractable dynamic general equilibrium model, in which forward-looking banks compete imperfectly for deposit funding, and deposit insurance may induce excessive risk taking. The zero lower bound (ZLB) distorts bank competition and boosts risk shifting incentives, particularly if rates are expected to remain near-zero for long. At the ZLB, capital regulation becomes a less effective tool to curb moral hazard. When banks cannot pass on the cost of capital to depositors, tight capital requirements erode franchise value, countervailing the usual “skin in the game” effect. Very low interest rates may therefore motivate weaker capital regulation, despite overall higher risk. Complementing existing regulation with policy tools that subsidize the funding cost of banks may improve welfare at the ZLB.
An Efficient Model for Effective Bug Triage

— Most of the software companies needs to deal with large number of software bugs every day. Software bugs are unavoidable and fixing software bugs is an expensive task. The goal of effective bug triaging software is to assign potentially experienced developers to new-coming bug reports. To reduce time and cost of bug triaging, an automatic approach is proposed in this paper that predicts a developer with relevant experience to solve or fix the new coming bug report. In this paper, the five term selection methods on the accuracy of bug assignment are used. In addition, the load between developers based on their experience is re-balanced. The proposed system is built with intention to suggest or recommend the bug and not to automatically assign it. This allows a window to handle real time crisis that come up during project development lifecycle.