Miracle-dz/UniFAR-FTUs huggingface dataset:
Objective Assessment of a Speech Enhancement Scheme with an Automatic Speech Recognition-Based System

A single-ended method for the prediction of perceived listening effort based on an automatic speech recognition system was adopted from the literature and modified to evaluate a near-end listening enhancement (NELE) scheme. The listening effort prediction method employs a deep time delay neural network (TDNN) that was trained as part of an automatic speech recognizer. The TDNN computes phoneme posterior probabilities (or “posteriorgrams”), which degrade in the presence of noise or other distortions. The degree of posteriorgram degradation is quantified by a performance measure and serves as a predictor for mean subjective listening effort ratings of normal-hearing listeners. The modification of the original method consists of the usage of a TDNN (in contrast to a regular feed-forward DNN used before), which was trained on a much bigger speech corpus. Without any task-specific training or optimization, the modified method achieves a very high correlation with subjective listening effort ratings from the used test data set of unprocessed and NELE-processed speech in two types of background noise (r = 0.98), generalizes to unseen noise conditions, and produces consistent predictions across these conditions that can be directly compared.
ASYMMETRIC REAL EXCHANGE RATE AND INFLATION CAUSALITY BASED ON TODA-YAMAMOTO DYNAMIC GRANGER CAUSALITY TEST

ASYMMETRIC REAL EXCHANGE RATE AND INFLATION CAUSALITY BASED ON TODA-YAMAMOTO DYNAMIC GRANGER CAUSALITY TEST This study re-investigates the causal relationship between real exchange rate and inflation using asymmetric causality based on Toda-Yamamoto (1995) augmented Granger causality test. The study is conducted on Malaysia, Nigeria, the Philippines and South Africa. We simulate the critical values used in this study based on leverage bootstrapping and asymmetric causality test. The results are compared between the Granger asymptotic chi-square distribution and the leverage bootstrapped distribution with asymmetric test. Conflicting findings are obtained which prove the existence of size distortion and nuisance parameter estimates when the former method is applied. The Toda-Yamamoto results with asymmetric causality test reveal that policy intervention on positive cumulative inflation shocks can stabilize real exchange rate in Malaysia, the Philippines (including negative cumulative inflation shocks) and South Africa but not vice versa. The results also show the existence of one way causation from positive cumulative shocks in real exchange rate to inflation in Nigeria. The policy implication of this finding is that a strong price stabilization policy during positive cumulative price shocks can stabilize real exchange rate fluctuations in all the countries under study except Nigeria.
Spatial and temporal variability in fish community structure in Mediterranean climate temporary streams

Temporary streams naturally experience flow intermittence and hydrologic discontinuity that act to shape fish community structure. Yet, alteration of the flow regime of temporary rivers may lessen the resilience of fish communities to tolerate hydrologic change imposed by droughts. This long-term study (2001–2013) predicted spatial structuring (across catchments and amongst reaches) in fish community composition and abundance across a hydrologically-altered Mediterranean-type region dominated by temporary streams. Shifts from freshwater specialist and diadromous species to more generalist and tolerant species (i.e. freshwater generalist, aliens and estuarine species) were anticipated as the region experienced low flows (2001–2006) and critical water shortage (2007–2010) associated with a most severe drought. It was anticipated that changes in composition and abundance would be revealed during flood (2011) and post-flood (2012–2013) periods after the drought. Contrary to these predictions, fish community structure was broadly consistent across catchments, despite varying climatic and hydrologic (mainly, flow intermittence) regimes. As expected, significant spatial variability was revealed at the reach scale, with significant differences between upper reaches and terminal wetlands, and with a clear transition in fish community structure between these reach types. Significant temporal variability was also revealed with the reduced abundance of diadromous species (although Pseudaphritis urvillii did increase) and increases in the abundance of aliens and surprisingly freshwater specialists over the period of critical water shortage relative to antecedent low flows. This was followed by mixed reach-dependent responses of fish during the flood and post-flood periods. The differential responses of fish communities across reaches and temporal periods must be considered as part of the management of threatened species in hydrologically-altered regions dominated by temporary streams.
AN INTELLIGENT APPROACH OF QUERY PROCESS OPTIMISATION USING COOPERATIVE SEMANTIC CACHING TECHNIQUE

Query processing is the key aspect in any of client-server application. Optimality is measured based on various concerns such as architecture base, search engines, query processor, query formalism, query representation languages, IO and storage cost. Researchers strived and provided various solutions for query process optimization in the past. Efficient query processing requires an advanced caching mechanism to reduce the query response time. Semantic caching produce results for optimizing the evaluation of database queries by caching results of old queries and using them when answering new queries. We use a collaboration of semantic caches in a cooperative way process to resolve user queries so-called cooperative semantic cache as an extension. As a novelty in this paper, a new way of cooperative semantic cache organisation is proposed, i.e., a paradigm for reference caching as prefix _index of query information to reduce access latency as well as fault tolerance. Intelligence is used for fetching the relevant cache content and its consistency. Optimality is measured with an empirical test on real time application.
Climate Change and Sustainable Water Resource Management

Almost all aspects of climatic patterns are affected by rising level of Greenhouse gases (GHGs) and increasing anthropogenic activities. Change in climate is observed and studied by various researchers. In this article, the present and future effects of climate change on precipitation, temperature, flood events and droughts are discussed in the form of case studies. Significant rising trends in observed temperature are found in some parts Ganga basin. Future trends of temperature and precipitation also shows rising trend in entire Ganga basin. Rajasthan, a semi-arid hot zone, receives lesser rainfall and more prone to droughts. Historical and future trends of Standard precipitation index (SPI), which is a drought index, are also discussed in this study. It is seen that, overall there will be less severe droughts based on annual and monsoon months, but Northern and Western part of Rajasthan will be prone to more sever droughts. Rising level of CO2 emission is major contributor to the global warming. To keep the global warming lesser than 2◦C than pre-industrial time, carbon capture and storage (CCS) is only feasible solution. Maintaining the sustainability of the water resources is of prime importance because (i) due to the rising temperature, the available water resources will decline in the long term and (ii) water requirements will increase due to the growing population and economic advancements. This article also discusses the considerations and components of sustainable water resource management highlighting the approaches employed for managing agricultural water which is a major share-holder in the consumption of water resources.
MICROPROPAGATION AND GREENHOUSE CULTIVATION OF SCROPHULARIA TAKESIMENSIS NAKAI , A RARE ENDEMIC MEDICINAL PLANT

A protocol was developed for direct shoot regeneration of Scrophularia takesimensis using shoot tip explants. The explants were excised from mature field grown plants and cultured on Murashige and Skoog (MS) medium with different concentrations of 2-isopentyl adenine (2-iP), 6-benzyl adenine (BA) or thidiazuron (TDZ). Among the three cytokinins studied, BA was found to be the most effective cytokinin for multiple shoot induction. Addition of auxin to the shoot regeneration medium significantly enhanced the percentage of shoot induction and number of shoots per explant. The greatest percentage of shoot induction was achieved when shoot tip explants were cultured on MS medium supplemented with 3.0 mg L BA and 1.0 mg L indole-3-acetic acid (IAA) with an average of 13 shoots per explant. The regenerated shoots were transferred to modified MS medium supplemented with 3% (w/v) sucrose, 1.0 mg L indole-3-butyric acid (IBA), and solidified with 0.8% (w/v) agar for four weeks to induce the growth of shoots and roots. Plantlets were transferred to the greenhouse with 100% survival rate. High performance liquid chromatography analysis detected the presence of harpagoside in greenhouse-grown plants organs which were established from in vitro culture. The content of harpagoside was high in fruit followed by leaf, root and stem. This protocol could be utilized for conservation and clonal propagation and chemical analysis of this economically important plant.
Front-End Bound L 1 Bound L 2 Bound L 3 Bound DRAM Bound Others Retiring

The recently released SPEC CPU2017 benchmark suite has already started receiving a lot of attention from both industry and academic communities. However, due to the significantly high size and complexity of the benchmarks, simulating all the CPU2017 benchmarks for design trade-off evaluation is likely to become extremely difficult. Simulating a randomly selected subset, or a random input set, may result in misleading conclusions. This paper analyzes the SPEC CPU2017 benchmarks using performance counter based experimentation from seven commercial systems, and uses statistical techniques such as principal component analysis and clustering to identify similarities among benchmarks. Such analysis can reveal benchmark redundancies and identify subsets for researchers who cannot use all benchmarks in pre-silicon design trade-off evaluations. Many of the SPEC CPU2006 benchmarks have been replaced with larger and complex workloads in the SPEC CPU2017 suite. However, compared to CPU2006, it is unknown whether SPEC CPU2017 benchmarks have different performance demands or whether they stress machines differently. Additionally, to evaluate the balance of CPU2017 benchmarks, we analyze the performance characteristics of CPU2017 workloads and compare them with emerging database, graph analytics and electronic design automation (EDA) workloads. This paper provides the first detailed analysis of SPEC CPU2017 benchmark suite for the architecture community.
Exponential intuitionistic fuzzy entropy measure based image edge detection

This paper address a new algorithm for image edge detection using exponential entropy with Attanassov's intuitionistic fuzzy set theory. Attanassov's Intuitionistic fuzzy set takes into account the uncertainty in determining the degree of membership known as hesitation degree. A distance measure, called Exponential Intuitionistic Fuzzy Divergence have been introduced. Edge detection is carried out using the suggested distance measure. Experimental results reveal that the proposed method exhibits better performance and may efficiently be used for the detection of edges in images.
Summer 6-15-2016 ETHICAL IMPLICATIONS OF BIG DATA ANALYTICS

This study aims to identify, define, and prioritize ethical concerns related to big data analytics as they pertain to individuals, organizations and society. Big data analytics is a fast evolving phenomenon with poorly understood consequences. Identifying and exploring its ethical implications is therefore important for both users of big data, and for those who are the target of its use. Our research design is based on a Delphi study of academic and practitioner experts who illuminate the ethical concerns associated with big data analytics. Our emerging empirical insights thereby provide both, an important theoretical contribution to the information systems literature, as well as informing managerial and policy guidelines for implementing, and ethically benefiting from, big data analytics.
The Relationship between Serum Leptin and C - reactive protein in Iraqi Type 2 Diabetic Patients

Background: Higher serum levels of leptin and highly sensitive C-reactive protein (CRP) have been associated with type 2 diabetes mellitus (T2DM) as well as with obesity and increased risk of cardiovascular disease. Aim of study: To test whether serum leptin has an association with CRP in type 2 diabetic patients independent of the influences of some factors that might contribute to evolution of T2DM and its complications. Materials and methods: Eighty eight patients (44 males and 44 females) with T2DM were enrolled in this study. The following clinical characteristics were reported: Age, body mass index (BMI), blood pressure measurement, and family history of diabetes. Laboratory analyses included serum leptin, serum CRP, serum glucose assay, blood glycosylated hemoglobin (HbA1c) assay, and lipid analysts which included serum cholesterol, serum triglycerides, and serum high density lipoprotein-cholesterol. Results: The patients were categorized into a male group and a female group. Leptin showed a highly significant positive correlation with CRP (r = 0.449, P<0.01) as well as with BMI (r =0.677, P<0.001) in male patients. Leptin also showed a highly significant positive correlation with CRP (r = 0.468, P<0.01) as well as with BMI (r = 0.479, P<0.001) in female patients but in these patients CRP was also positively correlated with BMI (r = 0.667, P<0.001). The latter highly significant positive correlation was not detected in male patients. The correlation between leptin and CRP has remained significantly positive independent of age, duration of T2DM, fasting serum glucose and HbA1c in both male and female group. The correlation, when studied independent of BMI, has remained significantly positive in male group but became non significant in female group. Conclusion: Serum leptin level is associated with CRP level in type 2 diabetic patients. This association was independent of BMI in males and independent of many other variables that are associated with the evolution of T2DM in both males and females. This link may suggest a possible mechanism by which leptin may play a role in the development of diabetic complications such as cardiovascular disease.
Ex-post Risk Premia Tests using Individual Stocks : The IV-GMM solution to the EIV problem ∗

This paper develops an IV-GMM approach that uses past beta estimates and firm characteristics as instruments for estimating ex-post risk premia while addressing the error-in-variables problem in the two-pass cross-sectional regression method. The approach is developed in the context of large cross sections of individual stocks and short time series. We establish the N -consistency of the IV-GMM ex-post risk premia estimator and obtain its asymptotic distribution along with an estimator of its asymptotic variance-covariance matrix. These results are then used to develop new tests for asset pricing model implications. Empirically, we examine a number of popular asset pricing models and find support for the recent q-factor model proposed by Hou, Xue, and Zhang (2015).
IMAGE RESTORATION BASED ON ENHANCED SWITCHED MEDIAN FILTER WITH NSSK

This paper deals with an image restoration process, based on unsymmetrical high density impulse noise and Gaussian blur. By using these two different methods researcher first implemented an Enhanced switched median filter which is applied for denoising and second a Noise Suppressed Steering Kernel (NSSK) and median filter applied for deblurring. Even different methods are proposed for impulse noise, due to time complexity it has been replaced by switched median filter. To overcome the limitations in fuzzy filtering a switched median filter is proposed for obtaining a better performance on visual clarity and time in this proposed work. The proposed method is a hybrid method which employs an mixture of noise and blur . Different digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conforms better restoration through proposed method.
Verbs in Egyptian Arabic: a case for register variation

The limited availability of Egyptian Arabic (EA) corpus resources, especially speech corpora, has left open opportunity for research into such dialect phenomena as register. In this paper we introduce a new two-millionword EA corpus, CALM. We perform a register analysis on EA between two subcorpora of CALM (i.e. Movies and Blogs), showing several features that vary between the two. A discussion follows about how annotation was carried out automatically, how it was handcorrected, and what the prospects are for carrying out similar studies using CALM.
Oliver Landmann Understanding Economic Growth On the work of

All modern formal thinking on the determinants of long-term economic growth owes a great debt to Solow’ s (1956) seminal neoclassical model – a model built around a production function linking output to capital, labor and technology. Solow’s key insight was that the growth of per-capita income in a market economy is not driven by investment in the very long term, as was commonly believed at the time, but by the advance of technology. For this landmark contribution to the theory of economic growth, Solow was awarded a Nobel Prize in 1987. Influential as it was, and still is to the present day, Solow’s model left two very big questions unanswered. First, if technology drives growth, what drives technology? Without a model of the creation of new ideas, new products and more efficient production processes, Solow could not address this question. In effect, his theory was not so much a theory of economic growth as a model of a growing economy, taking growth as given. The second question left open by Solow was that of the sustainability of ongoing economic growth in the face of limited natural resources and a limited ecosystem. In the absence of an explicit role for natural resources in the production function and without decreasing returns to scale due to a limited anthroposphere, the issue of sustainability was beyond the scope of the Solowmodel as well. The research of the two Nobel laureates of 2018, William D. Nordhaus and Paul M. Romer, has gone a long way towards closing these two glaring gaps in our understanding of economic growth. In the remainder of this article, their contributions and some of their major implications are sketched.
Chapter 1 3 D Point Cloud Based Hybrid Maps Reconstruction for Indoor Environments

In this article we investigate the problem of constructing a useful 3D hybrid map for both human being and service robots in the indoor environments. The objects in our laboratory include different tables, shelves, and pillar, which are of great importance for indoor service robot. We detail the components of our map building system and explain the essential techniques. The environment is detected in 3D point clouds, after sophisticated methods operating on point cloud data removing noise points and down sampling the data, we segment the data into different clusters, estimate the posture for clusters that can be recognized from library and replace it with VRML model we built in advance, then reconstruct surface for which cannot be recognized. Finally the preliminary hybrid maps are represented with the form of point cloud, VRML model and triangular meshes in 3DMapEidtor.
Improvements on ORCA for Fast Computation of Graphlet Degree Vectors of any Graphlet Order

It is increasingly common to find real-life structures or behaviors represented as graphs in many areas of the computing sciences. Comparing these graphs is a hard task, especially when we are interested in assigning a non-binary similarity score between two large graphs based on some domain-specific context. In bioinformatics, social network analysis and other areas is frequently necessary to compute graph similarities based on the local topological information of each vertex of the given graphs. This is why graphlet degree vectors have become more and more popular in these areas. They provide a simple yet detailed representation of a vertex’s topology by counting the number of times such vertex touches a list of small predefined sub-structures called graphlets. In this thesis, we study the state-of-the-art algorithm to compute graphlet degree vectors, the Orbit Counting Algorithm (ORCA). ORCA generates a triangular system of linear equations that can be quickly solved to obtain the graphlet degree vector of a vertex. We make theoretical and practical improvements to this algorithm and measure the difference in speed after these improvements. The theoretical improvement consists of finding automorphisms of graphlets given a fixed vertex that is required to map to itself in such automorphisms. We observe that one piece of the algorithm runs much faster than before with this improvement, especially for larger graphlet orders. This helps the algorithm take less time in generating the linear system that we use to find the desired graphlet degree vector. The practical improvement consists of making a flexible implementation of the algorithm, which can take any graphlet size as input, any number of input graphs, and compute the graphlet degree vector for every vertex in each one of those graphs.
MEASURING KINDNESS AT SCHOOL : PSYCHOMETRIC PROPERTIES OF A SCHOOL KINDNESS SCALE FOR CHILDREN AND ADOLESCENTS

In this study, we sought to create and validate a brief measure to assess students’ perceptions of kindness in school. Participants included 1,753 students in Grades 4 to 8 attending public schools in a large school district in southern British Columbia. The School Kindness Scale (SKS) demonstrated a unidimensional factor structure and adequate internal consistency. The pattern of associations of the SKS to a corpus of theoretically relevant constructs obtained via student self-reports (classroom supportiveness, optimism, happiness, prosocial and social goals, satisfaction with life, and academic self-efficacy) provided evidence for convergent and discriminant validity. Furthermore, the SKS was significantly and positively associated with teacher reports on students’ empathy, social skills, and peer acceptance. Analyses by gender and grade indicated that girls perceived significantly higher levels of kindness in school than did boys, and that students’ perceptions of kindness in school decreased from fourth to eighth grade, with fourth-grade students reporting the highest levels of kindness in school and eighth-grade students reporting the lowest levels. The theoretical importance of investigating students’ perceptions of kindness in the school context and the practical implications of this research for informing educational efforts to promote social and emotional competencies in school communities are discussed. C © 2015 Wiley Periodicals, Inc.