Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech-Model Based Accurate Blind Reverberation Time Estimation Using an LPC Filter

In this paper, we propose a speech-model based method using the linear predictive (LP) residual of the speech signal and the maximum-likelihood (ML) estimator proposed in “Blind estimation of reverberation time,” (R. Ratnam , J. Acoust. Soc. Amer., 2004) to blindly estimate the reverberation time (RT60). The input speech is passed through a low order linear predictive coding (LPC) filter to obtain the LP residual signal. It is proven that the unbiased autocorrelation function of this LP residual has the required properties to be used as an input to the ML estimator. It is shown that this method can successfully estimate the reverberation time with less data than existing blind methods. Experiments show that the proposed method can produce better estimates of RT60, even in highly reverberant rooms. This is because the entire input speech data is used in the estimation process. The proposed method is not sensitive to the type of input data (voiced, unvoiced), number of gaps, or window length. In addition, evaluation using white Gaussian noise and recorded babble noise shows that it can estimate RT60 in the presence of (moderate) background noise.
Comparative experiments to evaluate the use of syllables for the automatic recognition of arabic spoken names in noisy environment

This paper addresses the problem of noise robustness of automatic speech recognition (ASR) systems, using a hybrid technique: a speech pre-processing enhancement technique and the use of the syllables as the acoustic units for the ASR process. The speech pre-processing enhancement technique was accomplished by the use of the Ephraim-Malah filter. We tested our system using a database that consists of spoken Arabic names in noisy environments. This is achieved by the use of an HMM-based statistical recognition engine. Comparative experiments show that the syllable-based recognition outperforms the monophone- and triphone-based recognition in noisy environments. The HTK hidden Markov model toolkit was used throughout our experiments. Results show that the recognition rate obtained in noisy environments using syllables, outperformed the rates obtained using both triphones and monophones by 5.79% and 39.72%, respectively. On the other hand, with the integration of the Ephraim-Malah filter in the front-end of our syllable-based ASR system, we show through experiments that, the recognition rate using syllables outperformed the rate obtained using triphones and monophones by 6.58% and 39.72%, respectively.
Digital Image Integrity

The integrity of a digital image is paramount in fields such as forensics, medical imaging, and military and industrial photography. Courts make decisions affecting an individual’s liberty based, in part, on images presented as evidence. Physicians and researchers make diagnoses based on imaging—holding people’s lives in the balance. Military photographs may determine target locations based on their content and interpretation. Industrial photographs depict defects in materials that could lead to faulty and dangerous consumer products.
Radiative Transfer Effects during Photoheating of the Intergalactic Medium

The thermal history of the intergalactic medium (IGM) after reionization is to a large extent determined by photoheating. Here we demonstrate that calculations of the photoheating rate which neglect radiative transfer effects substantially underestimate the energy input during and after reionization. The neglect of radiative transfer effects results in temperatures of the IGM which are too low by a factor of 2 after He II reionization. We briefly discuss implications for the absorption properties of the IGM and the distribution of baryons in shallow potential wells.
Bayesian Estimation for Markov Modulated Asset Prices

A Stochastic Differential Equation (SDE) appearing in mathematical finance is considered in random environment by assuming that its two parameters are switched by an unobserved continuous-time Markov chain whose states represent the states of the market environment. A Dirichlet process is placed as a prior on the space of the sample paths of this chain, leading to a hierarchical Dirichlet model whose estimation is done both on simulated data and on a real data set from the Indian market.
dSir2 deficiency in the fatbody, but not muscles, affects systemic insulin signaling, fat mobilization and starvation survival in flies

Sir2 is an evolutionarily conserved NAD+ dependent protein. Although, SIRT1 has been implicated to be a key regulator of fat and glucose metabolism in mammals, the role of Sir2 in regulating organismal physiology, in invertebrates, is unclear. Drosophila has been used to study evolutionarily conserved nutrient sensing mechanisms, however, the molecular and metabolic pathways downstream to Sir2 (dSir2) are poorly understood. Here, we have knocked down endogenous dSir2 in a tissue specific manner using gene-switch gal4 drivers. Knockdown of dSir2 in the adult fatbody leads to deregulated fat metabolism involving altered expression of key metabolic genes. Our results highlight the role of dSir2 in mobilizing fat reserves and demonstrate that its functions in the adult fatbody are crucial for starvation survival. Further, dSir2 knockdown in the fatbody affects dilp5 (insulin-like-peptide) expression, and mediates systemic effects of insulin signaling. This report delineates the functions of dSir2 in the fatbody and muscles with systemic consequences on fat metabolism and insulin signaling. In conclusion, these findings highlight the central role that fatbody dSir2 plays in linking metabolism to organismal physiology and its importance for survival.
Centre for Evidence Based Policy and Practice : Working Paper 10 Conceptual Synthesis 1 : Learning from the Diffusion of Innovations

This paper sets out the key ideas, models and debates within the diffusion of innovations (DoI) literature and reviews the implications of these for developing a better understanding of research utilisation and evidence-based policy and practice (EBPP) implementation. The analytical review of the literature is organised around the types of knowledge at issue, the forms of utilisation that are envisaged, how the process of DoI has been modelled, the ‘ways of seeing’ that underpin analyses within the field, and finally the key findings to emerge from research in this area. The paper concludes by drawing out the implications of the ideas to emerge from the DoI literature for EBPP implementation.
Quantitative Trait Loci Associated with Phenological Development, Low-Temperature Tolerance, Grain Quality, and Agronomic Characters in Wheat (Triticum aestivum L.)

Plants must respond to environmental cues and schedule their development in order to react to periods of abiotic stress and commit fully to growth and reproduction under favorable conditions. This study was initiated to identify SNP markers for characters expressed from the seedling stage to plant maturity in spring and winter wheat (Triticum aestivum L.) genotypes adapted to western Canada. Three doubled haploid populations with the winter cultivar ‘Norstar’ as a common parent were developed and genotyped with a 90K Illumina iSelect SNP assay and a 2,998.9 cM consensus map with 17,541 markers constructed. High heritability’s reflected large differences among the parents and relatively low genotype by environment interactions for all characters considered. Significant QTL were detected for the 15 traits examined. However, different QTL for days to heading in controlled environments and the field provided a strong reminder that growth and development are being orchestrated by environmental cues and caution should be exercised when extrapolating conclusions from different experiments. A QTL on chromosome 6A for minimum final leaf number, which determines the rate of phenological development in the seedling stage, was closely linked to QTL for low-temperature tolerance, grain quality, and agronomic characters expressed up to the time of maturity. This suggests phenological development plays a critical role in programming subsequent outcomes for many traits. Transgressive segregation was observed for the lines in each population and QTL with additive effects were identified suggesting that genes for desirable traits could be stacked using Marker Assisted Selection. QTL were identified for characters that could be transferred between the largely isolated western Canadian spring and winter wheat gene pools demonstrating the opportunities offered by Marker Assisted Selection to act as bridges in the identification and transfer of useful genes among related genetic islands while minimizing the drag created by less desirable genes.
Temporal granularity for unanchored temporal data

Granularity is an integral feature of bothanchored(e.g., 25 October 1995 , July 1996) andunanchored(e.g.,3 minutes, 6 hours 20 minutes , 5 days, 1 week) temporal data. In supporting temporal data that is specified in different gran ularities, numerous approaches have been proposed to deal with the issues of converting temporal data from one granularity to another. The emphasis, however, has only been on granularity conversions with respect to anchored temporal data. This is because a granularity in these approaches i s modeled as an anchoredpartitioning of the time axis, thereby making it difficult to deal with granularity conversions in u anchored temporal data. In this paper we provide a novel approach to the treatment of granularity in temporal data. A granularity is modeled as a special kind of unanchored temporal primitive that can be used as a unit of time. That is, a granularity is modeled as a unit unanchored temporal primitive. Granularities are accommodated within the context of calendarsand granularity conversions are presented and discussed in terms of unanchored durations of time. This allows us to consistently model and operate on unanchored temporal data that is comprised of different and mixed granularities. Specifically, we show how unanchored temporal data is represented, give procedures for converting the data to a given granularity, provide canonical forms for the data, an d describe how operations between the data are performed.
Corporate Investment in Commercial Real Estate with Costly Reversibility

Commercial real estate transactions by corporations are matched with transactions of comparable assets by non-institutional investors to evaluate outcomes resulting from corporate investment policy. Corporations invest in long-term assets at a significant premium estimated relative to fundamental values. Divestiture is at a significant discount. Differences in operating performance fail to explain this outcome. Instead, corporate investors value commercial property differently than non-institutional investors. Valuation differences contribute to overpayment during periods of expansion and liquidation during contraction. Corporate sellers also exhibit a high degree of impatience with significantly reduced marketing periods.
Interactive Cutaway Illustrations

In this paper we discuss different approaches to generate cutaway illustrations. The purpose of such a drawingis to allow the viewer to have a look into an otherwise solid opaque object. Traditional methods to draw thesekinds of illustrations are evaluated to extract a small and effective set of rules for a computer‐based renderingof cutaway illustrations. We show that our approaches are not limited to a specific rendering style but can besuccessfully combined with a great variety of well‐known artistic or technical illustration techniques. All methodsof this paper make use of modern graphics hardware functionality to achieve interactive frame rates.
Video similarity measurement approach via dimensionality reduction with distance space and random projection: Application with sports video classification

A critical issue of measuring video similarity is most video data are huge files, which vary in terms of length and amount of data, resulting in time-consuming data processing. Therefore, reducing the dimensionality of the data becomes a necessity. This paper proposes the video similarity measurement approach for sports video classification by dimensionality reduction with distance space and random projection (RP). All frames of training videos are extracted by color histogram based method. After that, the clustering technique is performed to provide the centroids of each cluster, called reference vectors. These vectors are used as a set of basis to create new space, called distance space. For any sequence in distance space, the new feature is represented by the frequencies of similar frame comparing with each reference vector. Afterwards, all features of videos are projected onto a low-dimensional subspace using a random projection. Finally, the nearest neighbor (NN) classifier is applied to compare the similarity of the videos between the training videos and the test videos in new space. Accordingly, the proposed approach helps enhance feature dimension reduction, resulting in faster data processing. The experimental results show that this approach is both efficient and effective in sports video similarity measurement.
Noninvasive Physiologic Assessment of Coronary Stenoses Using Cardiac CT

Coronary CT angiography (CCTA) has become an important noninvasive imaging modality in the diagnosis of coronary artery disease (CAD). CCTA enables accurate evaluation of coronary artery stenosis. However, CCTA provides limited information on the physiological significance of stenotic lesions. A noninvasive “one-stop-shop” diagnostic test that can provide both anatomical significance and functional significance of stenotic lesions would be beneficial in the diagnosis and management of CAD. Recently, with the introduction of novel techniques, such as myocardial CT perfusion, CT-derived fractional flow reserve (FFRCT), and transluminal attenuation gradient (TAG), CCTA has emerged as a noninvasive method for the assessment of both anatomy of coronary lesions and its physiological consequences during a single study. This review provides an overview of the current status of new CT techniques for the physiologic assessments of CAD.
Wavelet based integrated color image retrieval

Image retrieval is an active research area in numerous applications. Color and Texture is an important Low Level features for content based image retrieval and this paper proposes a method for extracting color feature using Discrete Wavelet transform and Auto-Correlogram. The Weighted Wavelet Auto-Correlogram is applied on the High frequency sub bands such as LH, HL and HH of the Y, Cb, and Cr channel of the image respectively for Color feature extraction. Similarly the texture feature such as Mean and Standard Deviation is extracted from the two level decomposed sub bands. The efficiency of the method is experimented with the subset of Corel database and compared with some existing methods such as color histogram, color correlogram and wavelet correlogram. The proposed method yields high retrieval rate.
A Collaborative Biomedical Research System

The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent "chronicles" of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.
Supporting R&D Support Groups

OVERVIEW: R&D support groups make essential contributions to the process of innovation and can help speed time-to-market, yet the role they play is often overlooked or undervalued relative to that of their internal customer groups. When status differences interfere with collaboration and the free flow of knowledge between support groups and their customers, process efficiency and effectiveness can suffer. It is therefore important for organizations to understand the relationships between support groups and their customers, and to determine whether or not they are working as equals in carrying out their shared work process. Social network analysis can be used to describe the relationships between support groups and their customers in terms of the amount and direction of interaction that takes place between them, and can highlight those areas where interactions are out of balance and more collaborative partnerships need to be formed.
A Comparison of Cross-Versus Single-Company Effort Prediction Models for Web Projects

Background: In order to address the challenges in companies having no or limited effort datasets of their own, cross-company models have been a focus of interest for previous studies. Further, a particular domain of investigation has been Web projects. Aim: This study investigates to what extent effort predictions obtained using cross-company (CC) datasets are effective in relation to the predictions obtained using single-company (SC) datasets within the domain of web projects. Method: This study uses the Tukutuku database. We employed data on 125 projects from eight different companies and built cross and single-company models with stepwise linear regression (SWR) with and without relevancy filtering. We also benchmarked these models against mean and median based models. We report a case-by-case analysis per company as well as a meta-analysis of the findings. Results: Results showed that CC models provided poor predictions and performed significantly worse than SC models. However, relevancy filtered CC models yielded comparable results to that of SC models. These results corroborate with previous research. An interesting result was that the median-based models were consistently better than other models. Conclusions: We conclude that companies that carry out Web development may use a median-based CC model for prediction until it is possible for the company to build its own SC model, which can be used by itself or in combination with median-based estimations.
Fast Convergent Factorial Learning of the Low-Dimensional Independent Manifolds in Optical Imaging D

In many functional-imaging scenarios, it is a challenge to s eparate the response to stimulation from the other, presumabl y independent, sources that contribute to the image formation. When the brain is optically imaged, the typical variabilities of some of these sources force the data to lie close to a low-dimensiona l, nonlinear manifold. When an initial probability model is deriv ed by the Karhunen-Loève Transform (KLT) of the data, and some fa ctors of this manifold happen to be accessibly embedded in sui tably chosen KLT subspaces, vector quantization has been used to c haracterize this embedding as the locus of maximum likelihood o f the data, and to derive an improved probability model, in which t he factors—the dynamics on this locus and away from it—are esti mated independently. Here we show that such a description ca n serve as the starting point for a convergent procedure that a ltern tively refines the estimates of the embedding of, and the dyna mics on, the manifold. Further, we show that even a very crude init ial estimate, from a heavily mixed subspace, is sufficient for co nvergence in a small number of steps. This opens the possibility o f hierarchical semi-blind separation of the independent sou rces in optical imaging data, even when their contributions are non li ear.
Semi-supervised learning based Chinese dialect identification

The main contribution of this paper is to present the novel information fusing framework. The shifted delta cepstra (SDC) feature and prosodic feature are firstly used to train two base classifiers respectively. Then co-training algorithm presented in semi-supervised learning is employed to improve Chinese dialect identification accuracy. Four Chinese dialects is tested with this system. The experimental results showed that the proposed system outperformed the original GMM based system.
Students’ learning achievement using Knisley learning model with Brainstorming method

This study aims to determine whether there is an influence of using Knisley learning model with Brainstorming method toward students’ mathematics learning achievement. This experimental research conducted in one of junior high schools in South Sulawesi, Indonesia by taking a class randomly from the eight grade. The research was carried out during seven meetings consisted of one pre-test meeting, one post-test meeting and five preferential treatment meetings. Data were analyzed using descriptive and inferential statistical analysis. Based on the score of pre-test and post-test, the students scores show an improvement from low to high category. The average of students score in post-test was complete which was approximately three-fourth of the total percentage, while in reverse, a hundred percent of students’ score in pre-test showed students failure to reach a standard completeness score set by school. Furthermore, gain normalization analysis illustrated a well increase of students learning achievement because its values experienced high category. According to those progress and supported by the result analysis of teacher and students activities sheets and students responses sheets to the learning, it can be concluded that that there is an influence of using Knisley learning model with brainstorming method as its result shows a positive change toward the students’ mathematics learning achievement.
Energy-efficient routing

This work is focused on explore different heuristics that may be applied to provide a link-based cost for energy-aware multihop routing for wireless environments which integrate heterogeneous devices that are carried or owned by Internet end-users. We propose to analyze based on simulations of the different heuristics when applied to both distance-vector and link-state approaches, namely the AODV and OLSR routing protocols.