Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Processing Using Speech Enhancement Techniques for Unit Selection and Hidden Markov Model Based Low Resource Language Marathi Text-to-Speech System

A speech signal captured by a distant microphone is generally contaminated by background noise, which severely degrades the audible quality and intelligibility of the observed signal. To resolve this issue, speech enhancement has been intensively studied. In this paper, we consider a text-informed speech enhancement, where the enhancement process is guided by the corresponding text information, i.e. a correct transcription of the target utterance. The proposed Unit Selection Synthesis (USS) and Hidden Markov Models (HMM)-based framework are motivated by the recent success in Text-to-Speech (TTS) research. The primary aim of the study is to improve the quality of speech after synthesizing voice employing USS and HMM methods for building low resource Marathi TTS using speech enhancement techniques. Taking advantage of the nature of USS and HMM that allows us to utilize disparate features in an inference stage, the proposed method infers the clean speech features by jointly using the observed signal and widely-used TTS features derived from the corresponding text. In this paper, we ﬁrst introduce the background and the details of the proposed method for low resource Marathi language. Then, we show how the text information can be naturally integrated into speech enhancement by utilizing USS and HMM and improve the synthesis speech enhancement performance. The spectral subtraction method is used to remove the noise from synthesized speech and improve the quality. The spectral parameters of both the methods shows the progress in the enhanced speech.
An Enhanced Elliptic Curve Cryptosystem for Securing Data

The purpose of this research is to enhance the cryptographic system called the Elliptic Curve. Elliptic Curve cryptosystem (ECC) is a technique of public-key encryption, which is rooted on the arithmetical construction of elliptic curves over finite fields. Elliptic Curve Cryptographic System necessitates smaller keys compared to non-ECC cryptography to offer equal security. The security of RSA is based on the computational task of considering extensive numbers leading to an increase in encryption computation time, slower connection of the SSL handshake and increase in CPU usage during handshakes. Therefore, there should be a new way of solving this problem, which is ECC encryption. Elliptic curves are effective for digital signatures, key agreement, generators, pseudo-random and other related tasks. The first phase of the project involves understanding the key exchange of Diffie-Hellman and applying the properties of the Elliptic Curves. It is terminated with key facts that the Elliptic Curve Cryptography has a shorter key length, saves bandwidth, which facilitates key generation during the encryption/decryption of data, also the assurance of faster encryption and decryption, and
The effects of drought stress on photosynthetic physiological characteristics of Rhododendron hybrides ‘Cosmopolitan’ simulated by PEG

By adding different concentrations(5% to 25%) of polyethylene glycol(PEG-6000) into Hogaland solution to simulate the drought stress,the change of photosynthetic physiological characteristics in leaves of Rhododendron hybrides ‘Cosmopolitan’ was investigated.The results show that there is a close correlation between drought-resistance and the alteration of photosynthetic characters and chlorophyll fluorescence parameters,of which the correlation index of the net photosynthesis rate(Pn) and stomatal conductance(Gs) is higher(r=0.988 1,n=5). With increasing drought stress,the content of photosynthetic pigments increases,while Pn,Gs,transpiration rate(Tr),the maximal photochemical efficiency of PSⅡ(Fv/Fm),actual photochemical efficiency of PSⅡ(Fv′/Fm′) and photochemical quenching coefficient(qP) decreases,which demonstrates that light-harvesting chlorophyll a/b proteins in the lamella of chloroplasts are destroyed,the structures of photosystem are damaged seriously and the advantage of inspissation of photosynthetic pigments is neutralized.The comprehensive results indicate that the reduction of Pn under drought stress is mainly due to non-stomatal limitation.
Performance analysis of Intel Core 2 Duo processor

With the emergence of thread level parallelism as a more efficient method of improving processor performance, Chip Multiprocessor (CMP) technology is being more widely used in developing processor architectures. Also, the widening gap between CPU and memory speed has evoked the interest of researchers to understand performance of memory hierarchical architectures. As part of this research, performance characteristic studies were carried out on the Intel Core 2 Duo, a dual core power efficient processor, using a variety of new generation benchmarks. This study provides a detailed analysis of the memory hierarchy performance and the performance scalability between single and dual core processors. The behavior of SPEC CPU2006 benchmarks running on Intel Core 2 Duo processor is also explained. Lastly, the overall execution time and throughput measurement using both multi-programmed and multi-threaded workloads for the Intel Core 2 Duo processor is reported and compared to that of the Intel Pentium D and AMD Athlon 64X2 processors. Results showed that the Intel Core 2 Duo had the best performance for a variety of workloads due to its advanced micro-architectural features such as the shared L2 cache, fast cache to cache communication and smart memory access.
Fast Quality Inspection of Micro Cold Formed Parts using Telecentric Digital Holographic Microscopy

Quality inspection is an integral part of the production process and often part of the quality management agreements between manufacturer and customer. Especially when it comes to safety-relevant parts, i.e. in the automobile or medical industry, often a 100% quality inspection is mandatory. Here, we present a solution comprised of a digital holographic measurement system, as well as fast algorithms for geometric evaluation and surface defect detection that paves the way for the inspection of metallic micro cups in less than a second. By use of a telecentric lens instead of standard microscope objective, we compensate scaling effects and wave field curvature, which distort reconstruction in digital holographic microscopy. Due to limited depth of focus of the microscope objective, depth information from different object layers are then stitched together to yield 3D data of its geometry. The resulting point cloud data is automatically decomposed into simple geometric shapes in order to analyse geometric deviations. Amplitude as well as phase distribution images are then analysed for surface defects. Our approach is demonstrated by inspecting cold formed micro cups. Defects larger than 2 μm lateral resolution and 5 μm depth can be detected.
Study on the optimization models of nitrogen,phosphorus and potassium application for Polygonatum.sibiricum Red.production in Yangling

The effect functions of fertilizing amount of nitrogen,phosphorus and potassium on rhizoma yield and polysaccharide content of Polygonatum.sibiricum Red.were established by the D-saturation optimal design with three factors.The results showed that the effect of nitrogen,phosphorus and potassium on yield improvement was phosphoruspotassiumnitrogen and that on polysaccharide content was potassiumnitrogenphosphorus,and the effect of phosphorus on polysaccharide content was negative,while the effect of nitrogen and potassium were positive.When the target yield was 3 500～4 500 kg/hm2, the optimum fertilizing amount was 89.74～136.18 kg/hm2 for N,121.39～157.16 kg/hm2 for P2O5,and 61.06～92.63 kg/hm2 K2O with 95% confidence interval.When the polysaccharide content was during 8%～9.5%,the optimum fertilizing amount was N 103.91～153.65 kg/hm2,P2O5 69.5～124.27 kg/hm2,and K2O 68.16～95.36 kg/hm2 with 95% confidence interval.The optimum fertilizing amount for high yield,high efficiency and high quality in Polygonatum.sibiricum Red.production was 103.91～136.18 kg/hm2 for N,121.39～124.27 kg/hm2 for P2O5,and 68.16～92.63 kg/hm2 for K2O.The best ratio of N:P2O5: K2O was 1∶0.89～0.91∶0.5～0.68.
The degradation of acetic acid and nitrobenzene in water by O_3/UV.

Acetic acid in water was degraded utilizing O3/UV and O3/H2O2. The removal rate of acetic acid by O3/UV was only 3% higher than that of ozonation alone after 60min treating under the same condition, but O3/H2O2 degraded completely acetic acid under these conditions. However, O3/UV showed better oxidative degradation ability in nitrobenzene treatment process. The by-product H2O2 produced in the nitrobenzene degradation process played important role in the removal of organic compound; therefore, the increase of O3/UV oxidative degradation ability might be caused in two aspects: the activation of UV light on organic compound, and the production of free hydroxyl of high activity from medium product H2O2 catalytic decomposition of ozone. The efficiency of free hydroxyl generation from catalytic decomposition of ozone by single UV light was very low.
Evaluating optimizations that reduce global memory accesses of stencil computations in GPGPUs

This work compares the performance of optimizations that transform replicated global memory accesses into local memory accesses on 3D stencil computations in the NVIDIA Tesla K80 GPGPU. The optimizations reduce global memory contention caused by the set of multiprocessors. Evaluated optimizations are grid tiling, inserting spatial and temporal loops into kernels, register reuse, and some of their combinations. A standardized experiment evaluates performance variation with grid size and stencil size for each optimization. Experimental data show that codes that use these optimizations are up to 3.3 times faster than the classical stencil formulation. It also shows that the most profitable optimization varies with grid and stencil sizes.
A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.
Visualizing Appropriation of Research Funding with t-SNE Algorithm

: [ Objective ] This paper designs a visualization method for the appropriation of research funding, aiming to more effectively present the locations of funded projects. [ Methods ] First, we retrieved 4,669 funded projects from NSF’s Information and Intelligent System. Then, we added topic tags to these projects using clustering algorithm and human interpretation. Third, we extracted the high-dimensional text features for the application documents with TF-IDF model and LSA model. Fourth, we used the t-SNE algorithm to project high-dimensional features into two or three-dimensional spaces for visualization. Finally, we examined the visualization results with pre-classified topic labels. [ Results ] The proposed method created maps of funded projects, in both two-dimensional or three-dimensional spaces. [ Limitations ] The algorithm parameters need to be adjusted manually. More research is needed to evaluate the proposed method with documents of projects funded by other agencies. [ Conclusions ] The proposed method could generate maps for the funded projects, which is a helpful tool for scientific management.
Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.
Study on nutrient components and the extracting condition of the skin gelatin of tilapia

The nutrient content was analyzed in th skin of tilapia and the conditions of gelatin extraction from the skin of tilapia were studied in this paper through the orthogonal experiment and the main physicochemical properties and microorganisms were also tested.The results showed that the contents of protein and collagen in the skin were 33.14% and 27.8%,respectively.Collagen accounted for 83.9% of all protein.The optimal conditions for gelatin extraction were established by soaking in 2.5%(W/V)NaOH solution for 3.5 h and in 0.02%(V/V)HCl for 3 h and incubated at temperature 55℃. Extraction yield of fish gelatin was 26.5%,gel strength was 468 g and the content of protein was 82.43%. The total number of bacteria was 1.3×105 CFU·g-1 and coliform bacteria indicated negative.
Photometric Registration using Specular Reflections and Application to Augmented Reality

Photometric registration consists in blending real and virtual scenes in a visually coherent way. To achieve this goal, both reflectance and illumination properties must be estimated. These estimates are then used, within a rendering pipeline, to virtually simulate the real lighting's interaction with the scene. In this paper, we are interested in indoor scenes where light bounces off of objects with different reflective properties (diffuse and/or specular). In these scenarios, existing solutions often assume distant lighting or limit the analysis to a single specular object. We address scenes with various objects captured by a moving RGB-D camera and estimate the 3D position of light sources. Furthermore, using spatio-temporal data, our algorithm recovers dense diffuse and specular reflectance maps. Finally, using our estimates, we demonstrate photo-realistic augmentations of real scenes (virtual shadows, specular occlusions) as well as virtual spec-ular reflections on real world surfaces.
Optimal Scheduling for Precedence-Constrained Applications on Heterogeneous Machines

Graphical Abstract Abstract. High-Performance Computing (HPC) is a growing necessity of our technological society, HPC demands high loads of parallel computing jobs, an optimal scheduling of the parallel applications tasks is a priority to meet the demands of its users on time. Branch-and-bound (BB) Algorithms and Mathematical Programming (MP) solve complex optimization problems in an optimal manner, some MP or BB even have parallel computing capabilities, making them suitable solutions to solve realworld problems. In this paper, we propose two exact algorithms, a BB and an MP Model for scheduling precedence-constrained applications, on heterogeneous computing systems, as far as we known the first ones on his kind presented in the state of the art. One major contribution of the work is the proposed formulations of the objective function in both methods. Experimental results obtained more than twenty optimal values for synthetic applications from the literature.
A High-Performance Piano Note Recognition Scheme

ABSTRACT An efficient piano note recognition scheme utilizing precise onset detection followed by segmented short-time Fourier transform (STFT) has been presented. The performance of the proposed scheme has been verified under different noise levels by adding additive white Gaussian noise (AWGN) of different power levels with the original music signal, i.e. by examining the proposed method under different signal-to-noise ratio (SNR).
A $Q$ -Learning-Based Proactive Caching Strategy for Non-Safety Related Services in Vehicular Networks

Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic <inline-formula> <tex-math notation="LaTeX">${\varepsilon }_{{n}}$ </tex-math></inline-formula>-greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.
A Convergence of Decision-making Criterions in a Human-robot Group: - The Robot Makes a Suitable Decision-making Criterion by Interacting with the Group Members -

This paper presents a model to enable robots to create a suitable criterion for decision-making by indirectly interacting with people in a group. Using this model, a robot learns a suitable criterion for the group as a group member through reinforcement learning. When people, who have different personalities form a group, they adjust their criteria to a common criterion for the group. The present study investigates whether the robot can make a suitable decision-making criterion in a group by learning from interactions. Participants and the robot answer easy quizzes that have vague questions without direct conversation. Our experiments reveal that a group consisting of the participants and the robot forms a common criterion in a limited scenario. However, further study is required to reveal a robot’s social influence of human.
The Efficacy of Administering Fruit-Derived Polyphenols to Improve Health Biomarkers, Exercise Performance and Related Physiological Responses

Polyphenols are secondary metabolites involved in a myriad of critical processes in plants. Over recent decades, special attention has been paid to the anti-oxidative role of fruit-derived polyphenols in the human diet, with evidence supporting the contribution of polyphenols in the prevention of numerous non-communicable disease outcomes. However, due to the low concentration in biological fluids in vivo, the antioxidant properties of polyphenols seem to be related to an enhanced endogenous antioxidant capacity induced via signaling through the nuclear respiratory factor 2 pathway. Polyphenols also seem to possess anti-inflammatory and antioxidant properties and have been shown to enhance vascular function via nitric oxide mediated mechanisms. Consequently, there is rationale to support fruit-derived polyphenol supplementation to enhance exercise performance, possibly via improved muscle perfusion. Fruit-derived polyphenol supplementation in exercise studies have included a variety of fruits, e.g., New Zealand blackcurrant, pomegranate, and cherry, in the form of extracts (multicomponent or purified), juices and infusions to varying degrees of benefit. For example, research has yet to link the health-related benefits of black elderberry (Sambucus nigra L.) ingestion to exercise performance in spite of the purported health benefits associated with black elderberry provision in vitro and in vivo models, which has been attributed to their high antioxidant capacity and polyphenol content. This review summarizes the existing evidence supporting a beneficial effect of fruit-derived polyphenols on various biological processes and outlines the potential for black elderberry ingestion to improve nitric oxide production, exercise performance, and the associated physiological responses before-, during- and post-exercise.
Determinants of Nocturnal Cardiovascular Variability and Heart Rate Arousal Response in Restless Legs Syndrome (RLS)/Periodic Limb Movements (PLMS)

Recent studies have suggested that restless legs syndrome is associated with an increased prevalence of cardiovascular diseases mediated by sympathetic activation occurring during periodic limb movements. The aim of this study was to establish which factors affect the degree of sympathetic activation during the basal condition and during periodic limb movements that may contribute to increased vascular risk. Fifty untreated restless legs syndrome patients aged 62.6 ± 11.1 y, free of cardiovascular diseases, were examined. Heart rate variability was calculated during wakefulness and all sleep stages, during periods with and without periodic limb movements. Heart rate changes before and after periodic limb movement onset were analyzed to assess the arousal response to periodic limb movements. Both analyses took into account the effects of age, gender, periodic limb movement duration, periodic limb movement index, periodic limb movement interval and periodicity, and magnitude of muscular activity (electromyogram power). Compared to periods without periodic limb movements, a significant increase in sympathetic activity occurred in periods with periodic limb movements, independent of age, sex and periodic limb movement characteristics. Data obtained from the cardiac arousal response to periodic limb movements showed that electromyogram power is the factor affecting sympathetic tonus. These results suggest that other factors, such as electromyogram power and individual susceptibility, should be considered in the assessment of the vascular risk related to restless legs syndrome.
The Performance Of Space Time Block Coding (Stbc) In Mimo Relay Network

Sciences and Technology (CEST-2018), September 25-27, 2018, vol. 1. This is an open access article under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which permits any non-commercial use, distribution, adaptation, and reproduction in any medium, as long as the original work is properly cited. ISBN: 978-81-936820-5-0 The performance of Space Time Block Coding (STBC) in MIMO relay network Hamza Eldenferia, Jamal Elbergali