Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cloud-Based Platform for Watching Same Content on Three-Screen TV Continuously in Smart Home

Watching Same Content on Three-Screen TV Continuously (WSC3STVC) has been considered as one of the representative services in smart homes recently. This service offers content mobility among multiple kinds of screens based on user position in home. Quality of Experience (QoE) and service implementation cost are two important challenges for supporting WSC3STVC service. To the best of our knowledge, there is no visible attempt to design a comprehensive platform for supporting this service in smart homes. Benefiting from cloud computing, peer-to-peer (P2P) network, clustering and H.264 SVC transcoding, this paper proposes a QoE-aware and cost-effective platform for supporting WSC3STVC service in smart home. The strong points of the proposed platform are transcoding in cloud instead of Home GateWay (HGW) for decreasing HGW cost, content switching inside HGW for reducing service delay, using a cloud-managed P2P network for improving bandwidth between cloud and homes and also clustering homes for reducing transfer delay between homes.
Constrained estimation with truncated Gaussians

The state estimation problem consists of estimating the hidden state of a dynamic system, governed by a stochastic linear or nonlinear process model, from a set of noisy observations, which depend on the hidden state by a stochastic linear or nonlinear observation model. This problem is often solved recursively using Bayesian filtering. In the recursive Bayesian filtering approach, a new state estimate is determined at every time-step, given the latest measurement and some knowledge of the state at the previous time-step. However, incorporating a priori knowledge in the form of equality or inequality constraints on the states proves to be difficult and is still a topic of ongoing research [1, 2].
Integration of Seismic and Geomechanics for 4 D Monitoring-Application to Heavy-oil Reservoir

The analysis considers a heavy-oil reservoir, produced using a well-known process called Steam Assisted Gravity Drainage, or SAGD. In this in-situ, thermal recovery method, two horizontal wells are drilled into the reservoir above each other about 5 meters apart. The upper well injects steam into the reservoir at high pressure and temperature, which is a very effective way to lower the oil viscosity so it can begin to flow via gravity down to the producer. In an ideal homogeneous reservoir, the steam chamber would develop around the well pair as a perfect inverted triangle shape. However, presence of heterogeneities within the reservoir (for example, shale bodies) can slow down or even block the steam chamber progression.
A toolkit for voice inverse filtering and parametrisation

Voice inverse ﬁltering is a process in which the effects of the vocal tract are cancelled from the speech signal to estimate the airﬂow through the glottis. Although inverse ﬁltering has many applications in both research and clinical examination of voice production, few voice inverse ﬁltering software packages exist. In the present paper, we propose a ﬂexible software package, HUT Voice Source Analysis and Parametrisa-tion Toolkit (Aparat) for use in the MATLAB environment. It implements glottal inverse ﬁltering and several time-based parameters of the voice source in a graphical user interface. The software package is available under an open source licence at http://www.acoustics. hut. ﬁ/software/aparat/.
Two video analysis applications using foreground/background segmentation

Probably the most frequently solved problem when videos are analyzed is segmenting a foreground object from its background in an image. After some regions in an image are detected as the foreground objects, some features are extracted that describe the segmented regions. These features together with the domain knowledge are often enough to extract the needed high-level semantics from the video material. In this paper we present two automatic systems for video analysis and indexing. In both systems the segmentation of the foreground objects is the basic processing step. The extracted features are then used to solve the problem. One system analyses traffic videos, the other tennis games.
Comparison of Individual and Pooled Stool Samples for the Assessment of Soil-Transmitted Helminth Infection Intensity and Drug Efficacy

Background In veterinary parasitology samples are often pooled for a rapid assessment of infection intensity and drug efficacy. Currently, studies evaluating this strategy in large-scale drug administration programs to control human soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura, and hookworm), are absent. Therefore, we developed and evaluated a pooling strategy to assess intensity of STH infections and drug efficacy. Methods/Principal Findings Stool samples from 840 children attending 14 primary schools in Jimma, Ethiopia were pooled (pool sizes of 10, 20, and 60) to evaluate the infection intensity of STHs. In addition, the efficacy of a single dose of mebendazole (500 mg) in terms of fecal egg count reduction (FECR; synonym of egg reduction rate) was evaluated in 600 children from two of these schools. Individual and pooled samples were examined with the McMaster egg counting method. For each of the three STHs, we found a significant positive correlation between mean fecal egg counts (FECs) of individual stool samples and FEC of pooled stool samples, ranging from 0.62 to 0.98. Only for A. lumbricoides was any significant difference in mean FEC of the individual and pooled samples found. For this STH species, pools of 60 samples resulted in significantly higher FECs. FECR for the different number of samples pooled was comparable in all pool sizes, except for hookworm. For this parasite, pools of 10 and 60 samples provided significantly higher FECR results. Conclusion/Significance This study highlights that pooling stool samples holds promise as a strategy for rapidly assessing infection intensity and efficacy of administered drugs in programs to control human STHs. However, further research is required to determine when and how pooling of stool samples can be cost-effectively applied along a control program, and to verify whether this approach is also applicable to other NTDs.
Midkine, a Potential Link between Obesity and Insulin Resistance

Obesity is associated with increased production of inflammatory mediators in adipose tissue, which contributes to chronic inflammation and insulin resistance. Midkine (MK) is a heparin-binding growth factor with potent proinflammatory activities. We aimed to test whether MK is associated with obesity and has a role in insulin resistance. It was found that MK was expressed in adipocytes and regulated by inflammatory modulators (TNF-α and rosiglitazone). In addition, a significant increase in MK levels was observed in adipose tissue of obese ob/ob mice as well as in serum of overweight/obese subjects when compared with their respective controls. In vitro studies further revealed that MK impaired insulin signaling in 3T3-L1 adipocytes, as indicated by reduced phosphorylation of Akt and IRS-1 and decreased translocation of glucose transporter 4 (GLUT4) to the plasma membrane in response to insulin stimulation. Moreover, MK activated the STAT3-suppressor of cytokine signaling 3 (SOCS3) pathway in adipocytes. Thus, MK is a novel adipocyte-secreted factor associated with obesity and inhibition of insulin signaling in adipocytes. It may provide a potential link between obesity and insulin resistance.
A Learning-to-Rank Approach to Software Defect Prediction

Software defect prediction can help to allocate testing resources efficiently through ranking software modules according to their defects. Existing software defect prediction models that are optimized to predict explicitly the number of defects in a software module might fail to give an accurate order because it is very difficult to predict the exact number of defects in a software module due to noisy data. This paper introduces a learning-to-rank approach to construct software defect prediction models by directly optimizing the ranking performance. In this paper, we build on our previous work, and further study whether the idea of directly optimizing the model performance measure can benefit software defect prediction model construction. The work includes two aspects: one is a novel application of the learning-to-rank approach to real-world data sets for software defect prediction, and the other is a comprehensive evaluation and comparison of the learning-to-rank method against other algorithms that have been used for predicting the order of software modules according to the predicted number of defects. Our empirical studies demonstrate the effectiveness of directly optimizing the model performance measure for the learning-to-rank approach to construct defect prediction models for the ranking task.
Multilevel coding strategy for two-hop single-user networks

In this paper, a two-hop network in which the information is transmitted from a source via a relay to a destination is considered. It is assumed that both channels are quasi-static fading and all nodes are equipped with a single antenna. The knowledge of the channel for each transmission hop is only available at the corresponding receiver. The relay is assumed to be simple, i.e., not capable of data buffering over multiple coding blocks or rescheduling tasks. Considering a continuum of multilevel codes at both of the source and the relay, in conjunction with decode and forward strategy, we present a scheme to optimally allocate the available source and relay powers to different levels of their multilevel codes. Assuming Rayleigh fading, the performance of this scheme is also evaluated and compared with the previously known strategies.
Assessment of hydrodynamic separators for storm-water treatment

Hydrodynamic separators are proprietary underground devices designed to remove floatable debris e.g., leaves, trash, oil and to remove suspended solids from storm-water runoff by sedimentation. They are designed for storm-water treatment in urban areas to meet tight space constraints. Limited data on the suspended solids removal performance of installed devices are available, and existing data are questionable because of the problems associated with assessment by monitoring. The objectives of our research are to: 1 investigate the feasibility and practicality of field testing to assess the performance of hydrodynamic separators as underground storm-water treatment devices; 2 evaluate the effects of sediment size and storm-water discharge on the performance of six devices from different manufac- turers; and 3 develop a universal approach for predicting the performance of a device for any given application. In the field tests, a controlled and reproducible synthetic storm event containing sediment of a well defined size distribution and concentration was fed to a precleaned device. The captured sediment was then removed, dried, sieved, and weighed. To assess the performance of the devices, suspended sediment removal efficiency was related to a Peclet number, which accounts for two major processes that control performance: 1 settling of particles; and 2 turbulent diffusion or mixing of particles. After analyzing the data, all devices showed similar behavior, therefore, a three-parameter performance function was proposed for all devices. Performance functions were developed from the result of the field tests and parallel testing of two other full-scale devices in the laboratory. The performance functions can be used to determine the efficiency of the tested devices and to improve the selection and sizing of hydrodynamic separators and the assessment of their overall performance after installation.
OPTIMIZED MULTICLOUD MULTITASK SCHEDULER FOR CLOUD STORAGE AND SERVICE BY GENETIC ALGORITHM AND RANK SELECTION METHOD

Many task computing (MTC) problems can be solved by the most popular scheme such as computing cluster, especially in terms of loosely coupled task but when we handle the physical cluster . we may face several issues like Hardware Investments, long periods of cluster usage, very low computational resource during demand periods in related with drawbacks, cloud computing technology is proposed to satisfy the peak work load. In this paper we explore this scenario to solve the loosely coupled MTC application by establishing computing cluster on the top of a multi cloud Infrastructure. the user can determine their requirement not only based on functional but also non functional. The selection process is completely automated this is achieved y the optimization. To find the most appropriate solution for the selection we are using the genetic algorithm and rank selection process.
Music Genre Classification using Similarity Functions

We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an alternative approach. We employ a (dis)similarity-based learning framework proposed by Wang et al. This (dis)similarity-based approach has a theoretical guarantee that one can obtain accurate classifiers using (dis)similarity measures under a natural assumption. We demonstrate the effectiveness of our approach in computational experiments using Japanese MIDI data.
Associations between Interleukin-1 Gene Polymorphisms and Coronary Heart Disease Risk: A Meta-Analysis

Objective A great number of studies regarding the associations between IL-1B-511, IL-1B+3954 and IL-1RN VNTR polymorphisms within the IL-1gene cluster and coronary heart disease (CHD) have been published. However, results have been inconsistent. In this study, a meta-analysis was performed to investigate the associations. Methods Published literature from PubMed and Embase databases were searched for eligible publications. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random- or fixed- effect model. Results Thirteen studies (3,219 cases/2,445 controls) for IL-1B-511 polymorphism, nine studies (1,828 cases/1,818 controls) for IL-1B+3954 polymorphism and twelve studies (2,987 cases/ 2,208 controls) for IL-1RN VNTR polymorphism were included in this meta analysis. The results indicated that both IL-1B-511 and IL-1B+3954 polymorphisms were not associated with CHD risk (IL-1B-511 T vs. C: OR = 0.98, 95%CI 0.87–1.09; IL-1B+3954 T vs. C: OR = 1.06, 95%CI 0.95–1.19). Similarly, there was no association between IL-1RN VNTR polymorphism and CHD risk (*2 vs. L: OR = 1.00, 95%CI 0.85–1.17). Conclusions This meta-analysis suggested that there were no associations between IL-1 gene cluster polymorphisms and CHD.
Consistent noise models for analysis and design of CMOS circuits

Simple, physics-based MOSFET noise models, valid over the linear, saturation, and subthreshold operation regions are presented. The consistency of the models representing series-parallel associations of transistors is verified. Simple formulas for hand analysis using the inversion level concept are developed. The proportionality between the flicker noise corner frequency and the transistor transition frequency is proved and experimentally verified under wide bias conditions. Application of the noise models to a low-noise design is shown.
Influence of the perceptual speech quality on the performance of the text-independent speaker recognition system

—In the following paper we examine the influence of the perceptual speech quality on the performance of the text-independent automated speaker recognition system (ASRS). The perceptual speech quality was objectively measured using Perceptual Evaluation of Speech Quality method (PESQ). The speech quality was degraded under various conditions as imposed in Voice over Wireless Local Area Network (VoWLAN), GSM and PSTN telephony. The ASRS error rates of this evaluation are presented by means of detection error tradeoff (DET) curves. The results show the correlations between PESQ MOS and ASRS equal error rate (EER) and promise the objective speech quality measurements can be used for the prediction of ASRS performance.
Diabetes incidence and glucose intolerance prevalence increase with higher outdoor temperature

Objective Rising global temperatures might contribute to the current worldwide diabetes epidemic, as higher ambient temperature can negatively impact glucose metabolism via a reduction in brown adipose tissue activity. Therefore, we examined the association between outdoor temperature and diabetes incidence in the USA as well as the prevalence of glucose intolerance worldwide. Research design and methods Using meta-regression, we determined the association between mean annual temperature and diabetes incidence during 1996–2009 for each US state separately. Subsequently, results were pooled in a meta-analysis. On a global scale, we performed a meta-regression analysis to assess the association between mean annual temperature and the prevalence of glucose intolerance. Results We demonstrated that, on average, per 1°C increase in temperature, age-adjusted diabetes incidence increased with 0.314 (95% CI 0.194 to 0.434) per 1000. Similarly, the worldwide prevalence of glucose intolerance increased by 0.170% (95% CI 0.107% to 0.234%) per 1°C rise in temperature. These associations persisted after adjustment for obesity. Conclusions Our findings indicate that the diabetes incidence rate in the USA and prevalence of glucose intolerance worldwide increase with higher outdoor temperature.
Applying differentiable mutual information to hyperspectral band selection

In this paper, we extend our earlier work by improving a mutual information (MI) based hyperspectral band selection method. Mutual information effectively measures the statistical dependence between two random variables. By modeling ground truth (e.g., a reference map) as one of the two random variables, MI can be used to find the spectral bands that contribute most to image classification. We apply a differentiable rather a histogram-based representation of mutual information to construct the estimated reference map, which results in an automatic solution by gradient searching. Experiments on the AVIRIS 92AV3C data set show that the proposed approach can find the best spectral window, and the bands in this window can be used to construct the reference map satisfactorily.
A multi parametric optimization based novel approach for an efficient design space exploration for ASIC design

High level synthesis (HLS) is the methodology of generating Register Transfer Logic(RTL) design taking into consideration the behavioural specification and constraints within an optimized cost function. Design space exploration (DSE), an important stage of HLS, is a task for identifying and evaluating design alternatives during system development for obtaining Pareto optimal solution. Concerns over the power dissipation coupled with the conventional metrics such as area, time delay, thermal, performance, reliability, cost and testability have raised the demand for an efficient technique of high level synthesis with better design space exploration. This paper presents a novel approach to achieve a Pareto optimal solution for this design space exploration in minimum possible design time using Greedy Algorithm and Priority Factor (PF) for power and timing analysis.
Wealth Inequality and Homeownership in Europe

The recently published Household Finance and Consumption Survey has revealed large differences in wealth inequality between the countries of the Euro area. We find a strong negative correlation between wealth inequality and homeownership rates across countries. We use two decomposition methods to shed more light on this correlation. First, a Gini decomposition by homeownership status shows that the negative relationship is mostly driven by large between-group inequality across owners and renters. Second, to control for other observables, we conduct a detailed counterfactual decomposition of cross-country inequality differences. We confirm the major role for homeownership rates in accounting for the wealth inequality differences. Our analysis suggests that the cross-country variation is mostly driven by differences in the savings behavior of households in the bottom half of the wealth distribution and that those differences in savings are to a large extent channeled through housing wealth.
Glucose intolerance is common in Japanese patients with acute coronary syndrome who were not previously diagnosed with diabetes.

OBJECTIVE Postprandial hyperglycemia has emerged as a new glycometabolic condition associated with an excessive risk for coronary artery disease. We therefore attempted to evaluate the frequency of postchallenge hyperglycemia in patients with acute coronary syndrome (ACS) who were not previously diagnosed to have diabetes and did not have a fasting glucose concentration of > or =7 mmol/l or an HbA(1c) level >6.0%. We further correlated the presence of postchallenge hyperglycemia with the extent of coronary atherosclerosis. RESEARCH DESIGN AND METHODS In all, 134 consecutive ACS patients who met the above inclusion criteria were studied. An oral glucose tolerance test was performed before discharge. RESULTS The mean age, fasting glucose, and HbA(1c) were 60 years, 5.15 mmol/l, and 5.4%, respectively. Among ACS patients, impaired glucose tolerance (IGT) and diabetes were found in 50 (37%) and 13 patients (10%), respectively. The homeostasis model assessment for insulin resistance did not differ substantially among the normal glucose tolerance (NGT), IGT, and diabetic groups. Insulinogenic index, however, was lower and the number of stenosed vessels higher in diabetic patients compared with NGT patients. CONCLUSIONS Postchallenge hyperglycemia, caused primarily by impaired initial insulin secretion, is commonly found in Japanese ACS patients who have not been previously diagnosed with diabetes, and this phenomenon is considered to be associated with advanced coronary atherosclerosis. Therefore, the present study strongly supports the notion that oral glucose tolerance test assessment of postchallenge hyperglycemia is essential to identify any previously undiagnosed diabetes cases among Japanese ACS patients.
Minimum BER Transmit Power Allocation and Beamforming for Two-Input–Multiple-Output Spatial Multiplexing Systems

A two-input-multiple-output (TIMO) system represents an important special case of multiple-input-multiple-output (MIMO) systems in practical scenarios. In this paper, transmit optimization for TIMO spatial multiplexing systems is investigated. Minimum bit-error rate (MBER) is employed as the optimization criterion. Approximate MBER transmit power allocation for a variety of receiver structures is proposed, including zero-forcing, successive interference cancellation (SIC), and ordered SIC. Transmit-beamforming schemes using 4-ary pulse-amplitude modulation and quaternary phase-shift keying premixing are also proposed, which eliminate error floors in ill-conditioned TIMO channels. It is shown both analytically and by numerical simulations that the proposed schemes offer superior performance compared to existing schemes