Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and design of multirate systems for cancellation of acoustical echoes

An analysis of the multirate concept for the echo cancellation problem is presented that results in the consideration of two classes of solutions. For the first class the solution is derived explicitly, whereas for the second class a general approach and a subclass are discussed. Regarding the application to the cancelling of acoustical echoes the design problem for the frequency-subband concept-which is shown to be a solution of the first class-is examined in more detail and results are presented verifying the efficiency of this method. <<ETX>>
Comparing the Severity of Obsessive-Compulsive Symptoms in Pre-pregnancy, Pregnancy, and Postpartum Period among Women of reproductive age

Background & aim: Obsessive-Compulsive disorder is considered as one of the most common disorders in pregnancy and postpartum period. There are few studies in this regard in Iran. Therefore, the purpose of the current study was to compare the severity of obsessive-compulsive symptoms in pre-pregnancy, pregnancy, and postpartum period among women of reproductive age. Methods: This descriptive-comparative study was carried out on 900 eligible women (500 non-pregnant women, 200 pregnant women, and 200 in postpartum period) referred to health centers in Mashhad in 2011, who were selected using multi-stage sampling method. The data were collected by Madezly and Yale Brown Obsessive-Compulsive scale and analyzed using descriptive statistics, Kruskal–Wallis one-way analysis of variance, and Mann-Whitney U test in SPSS software (version 11) with the confidence level of 95%. Results: The obtained results of Kruskal-Wallis test showed that there was significant statistical difference between the groups of pre-marriage (15.9±7.1), pregnancy (20.6±4.5), and postpartum period (20.9±5.1) in terms of mean score of obsessive-compulsive symptoms (P=0.007). Conclusion: Women in their pregnancy and postpartum period are at risk of obsessive-compulsive disorder that mostly are not recognized. As a Consequence, it is necessary for healthcare centers to take required measures in this regard.
EFFECTS OF BARK REMOVAL ON ACOUSTIC VELOCITY OF DOUGLAS-FIR LOGS

The acoustic velocities of 81 Douglas-fir logs were measured before and after debarking in a log yard. Bark removal led to an average increase in acoustic velocity of 3.6%. A regression model for determining the effect of bark removal on the change in acoustic velocity was developed. This model predicted an increase in acoustic velocity of 4.6% if I 00% of bark was removed. This is similar to increases in velocity found by other researchers for radiata pine logs.
An improved unequal error protection technique for the wireless transmission of MPEG-4 video

The performance of video transmission over wireless channels is limited by the channel noise. Thus many error-resilient tools have been incorporated into the MPEG-4 video compression method. In addition to these tools, the unequal error protection (UEP) technique has been pro- posed to protect the different parts in a MPEG-4 video packet (VP) with different channel coding rates based on the rate-compatible punctured convolutional (RCPC) codes. However, it is still not powerful enough for the noisy channels. To provide more robust MPEG-4 video transmission, this paper proposes an improved unequal error protection (I-UEP) technique to protect the COD bits. Experimental results show that it is very effective and efficient.
Design and Implementation of High speed and Energy Efficient MAC using Adaptive Logic

Every digital circuit objective is to achieve MEP system i.e., minimum energy, minimum power, minimum voltage. Theseconstraints can be achieved through adaptive logic. Adaptive logic is one of the fastest and innovative logic that has been implemented in digital circuit . Adaptive logic is implemented using the combination of both Nano magnetic technology and CMOS technology . Adaptive technique works very effectively in both threshold and sub – threshold regions. Internet of Things (IoT) is on the threshold of a massive breakthrough. Timing-error-detection (TED)-based systems have been shown to reduce power consumption or increase yield due to reduced margins. Reducing voltage in the circuit results in slow operation that incurs more delay. Canary circuit have been designed for error detection and error correction approach. Canary circuit results in large delay. Adaptive logic have been designed with dual latch phase in each stage. A combination of XNOR gate and flip-flop around each stage is added for the verification of correct operation. The entire architecture was modelled using Verilog code with the help of XILINX ISE tool.
THE ONLINE SOCIAL NETWORKS AS A TOOL FOR ESTIMATING VOTE

Fernando Moreno Reyes, PhD in Marketing. Phone +34 678 50 55 29, Email :femoreno@ucm.es / fermoreno1@msn.com University Complutense of Madrid, Spain. Maria Puelles Gallo, PhD in Marketing. Email: mpuelles@ucm.es University Complutense of Madrid, Spain. Alejandro Kress, PhD Candidate in Industrial Engineering. Email: luis.alejandro. kress@estudiant.upc. edu University Politecnica of Catalunya. Spain.
Multi-words in the Czech TV/radio News Transcription system

This article explores influence of multi-words (compound words) in the continuous speech recognition system of our Czech TV/radio News Transcription system. The main aim is to support recognition of short words, which are often misrecognized. Short words are joined with frequent longer words into a multi-word. Two measures for multi-words selection are tested. The first measure is based on pointwise mutual information, the second is based on occurrence frequency. Occurrence frequency based measure outperformed pointwise mutual information based measure. Adding multi-words increased performance of continuous speech recognition system and reduced misrecognition of short words.
Numerical Evaluation of the Singularity Arising in the Two-Dimensional Volume Electric Field Integral Equation

A numerical procedure for the treatment of the singularity arising in the Method of Moments (MoM) solution of the two-dimensional volume Electric Field Integral Equation (EFIE) is introduced in this paper. The procedure expresses the singular integral in terms of an analytic function and employs a singularity isolation process coupled with numerical quadrature along the domain perimeter to evaluate the selfterms. Numerical results are presented comparing the method to conventional techniques. In particular, fields scattered from a dielectric cylinder, discretised with pulse basis and delta testing functions, are computed and compared against those from a reference Mie series solution. The results obtained using the numerical procedure described are shown to be superior.
Building a Computational Culture: A Pedagogical Study of a Computer Programming Requirement

As computational design becomes increasingly important to architectural practice, curricula must be updated to teach new outlooks and skills to the next generation of design students. Over the last two years, UNC Charlotte has tested a curriculum that emphasizes computational thinking and methods. The core of this curriculum is a required course that introduces over 70 students a year to the fundamental ideas of computation through exposure to programming in a design context. This paper describes ourteaching methods and our findings from a study of the course, which includes attempts to measure student outcomes, attitudes about computing, and the application of computation after the course. The results of our study, which suggest an inclusive methodology and emphasize the cultural dimension of this pedagogical task, may help schools in the planning and implementation of their own courses that introduce computational design.
Towards Natural Arm Control: Classification of hand and elbow movements

A major problem in high spinal cord injured patients (lesion above C4) is that they lose control over their grasp and elbow functions. With the help of neuroprosthetic systems, it is possible to restore simple hand and arm movements. It seems that for those cases, a non-invasive BrainComputer Interface (BCI) based on EEG signals provides a good option to control such devices. After first attempts at using a BCI for hand control are already made [1, 2], the aim of the following study is to investigate whether the imagination of hand and elbow movements can be used for neuroprosthetic control. To start with, here we describe a first investigation of the separability of executed as well as imagined hand and elbow movements.
To Take Hold of Space: Isovists and Isovist Fields

The environment is defined as a collection of visible real surfaces in space. An isovist is the set of all points visible from a given vantage point in space and with respect to an environment. The shape and size of an isovist is liable to change with position. Numerical measures are proposed that quantify some salient size and shape features. These measures in turn create a set of scalar isovist fields. Sets of isovists and isovist fields form an alternative description of environments. The method seems relevant to behavioral and perceptual studies in architecture, especially in the areas of view control, privacy, ‘defensibility’, and in dynamic complexity and spaciousness judgements. Isovists and isovist fields also shed light on the meaning of prevalent architectural notions about space. In the latter role it is hoped that an information-field theory such as the one presented can help provide fruitful common ground for designers and researchers.
Setiono Sample selection for credit scoring SAMPLE SELECTION AND NEURAL NETWORK RULE EXTRACTION FOR CREDIT SCORING

We present an approach for sample selection using an ensemble of neural networks (NNs) for credit scoring. The ensemble determines samples that are outliers by checking the NN prediction accuracy on the original training data samples. Samples that are consistently misclassified by NNs in the ensemble are removed from the training data set. The remaining data samples are used to train another NN for rule extraction. Our experimental results show that by eliminating the outliers, NNs can be trained to achieve better predictive accuracy. The rule set extracted from one of these networks is more accurate than the rule set extracted from NNs trained with the original data.
SURVEYING THE RELATION BETWEEN THE LEVEL OF FINANCIAL MARKETS DEVELOPMENT , EXCHANGE RATE FLUCTUATIONS , AND ECONOMIC GROWTH IN SELECTED OPEC COUNTRIES *

Accelerating the economic growth is one of the main aims of many countries; and many national aims are set based on this matter. The real exchange rate is one of the issues that its fluctuations could affect the performance of macroeconomics and especially the economic growth. It could be said that one of the factors relatively ignored in different analyses regarding the relation between exchange rate fluctuations and economic growth is countries’ level of financial markets development. This study has been conducted with the aim of surveying the effect of exchange rate fluctuations on economic growth based on the level of financial markets development of selected OPEC countries including Algeria, Ecuador, Iran, Nigeria and Saudi Arabia during 1981-2010. Also the effect of inflation on economic growth has been surveyed. The current research results by the use of panel data show that the effect of financial development on economic growth and also the mutual effect of exchange rate fluctuations and financial development on economic growth are positive but statistically insignificant. On the other hand, the effect of exchange rate fluctuations on economic growth is negative and significant.
Numerical recipes understanding through optical applications

Usually mathematical abstractions are used as examples in teaching of general courses as well as in special courses such as numerical recipes. Using of examples from physics (or optics in particular) makes the course more interesting and understandable for students in optical engineering. In Applied and Computer Optics Department of ITMO University the course “Numerical Recipes in Optics” is studied via practical usage of optical image modeling and quality analysis. It helps to make studying of numerical recipes (such as numerical integration, approximation, fast Fourier transform etc.) more useful. Moreover it gives deeper understanding in mathematical models of physical processes. The most interesting exercises corresponding to different numerical recipes are presented in the paper.
New Hybrid CG Algorithm Based on PR and FR-CG Steps

In this paper, a new hybrid conjugate gradient algorithm is proposed for unconstrained optimization. This algorithm combines the desirable computation aspects of Polak-Ribier steps and useful theoretical features of Fletcher-Reeves CG-steps. Computational results for this algorithm are given and compared with those of the Fletcher and Polak standard CG methods showing a considerable improvement over the latter two methods.
A microstructure for interfacing with neurons: the neurochip

A new type of multielectrode array for studies of cultured neural networks is described. It is a silicon microstructure, a "neurochip", which embodies wells on the surface of a chip that are designed to contain single neurons. Each well has a platinized gold electrode which can be used for stimulating a neuron in the well or for recording from it. Each well is covered with a structure which provides tunnels through which axons and dendrites can grow out and a central hole through which the neurons can be loaded into the well. The processes thus trap the cells in the wells. The first application of this method is described: A4/spl times/4 array of wells spaced 100 /spl mu/m apart. Results are described which show long term survival and outgrowth of neurons from the wells, and the use of well electrodes to both record and stimulate electrical activity of individual neurons.
Demand Shocks as Technology Shocks∗

We provide a macroeconomic theory where demand for goods has a productive role. A search friction prevents perfect matching between producers and potential customers. Larger demand induces more search, which in turn increases GDP. We embed the product-market friction in a standard neoclassical model and estimate it using Bayesian techniques. Business cycles are driven by preference shocks, true technology shocks, and investment-specific shocks. Preference shocks account for a large share of the fluctuations in consumption, GDP, and the Solow residual. The dynamics of the model’s implied search effort matches the empirical fall in U.S. shopping time during the Great Recession.
Ice melt in fl uence on summertime net community production along the Western Antarctic Peninsula

The Western Antarctic Peninsula (WAP) is a highly productive marine environment that is undergoing rapid change, with consequences for productivity and total ecosystem carbon cycling. We present continuous underway O2/Ar estimates of net community production (NCPO2Ar) in austral summer 2012, 2013 and 2014 at sub-kilometer horizontal resolution within the Palmer Long-Term Ecological Research (PalLTER) grid region of the WAP. Substantial spatial variability is observed with NCPO2Ar ranging from 0 to 790 mmol O2 m 2 d 1 and considerable interannual variability with mean values in the grid region of 54.4748. 5, 44.6740. 5, and 85.6775. 9 mmol O2 m 2 d 1 in 2012, 2013 and 2014 respectively. Based on a strong correlation (r1⁄40.83) between residence time integrated NCPO2Ar and NCPDIC derived from seasonal DIC drawdown, we find the observed NCPO2Ar spatial and interannual variability to be consistent with the December–January NCPDIC magnitude. Seeking to explain the mechanistic drivers of NCP in the WAP, we observe a linear relationship between NCPO2Ar and meteoric water content derived from δO and salinity. This correlation may be due to Fe supply from glacial melt and/or strengthening of stratification and relief of light limitation. Elevated surface Fe availability, as indicated by Fv/Fm and measurements of surface water dissolved Fe and Mn (a rough proxy for recent potential Fe availability), and shallower, more stable mixed layers are present where meteoric water and/or sea ice melt is high near the coast. Light limitation is evident in the WAP when mixed layer depths are greater than 40 m. Additionally we document hotspots of NCP associated with submarine canyons along the WAP. While it is difficult to predict how the physical-biological system might evolve under changing climatic conditions, it is evident that NCP, and potentially carbon flux out of the mixed layer, along the WAP will be sensitive to shifts in meltwater input and timing. & 2016 Elsevier Ltd. All rights reserved.
A METHOD TO DETERMINE THE COEFFICIENT OF HYDRAULIC RESISTANCE IN DIFFERENT AREAS OF PUMP-COMPRESSOR PIPES

It is shown that in the oil production by the gas-lift method a basic problem is the determination of the coefficient of hydraulic resistance (CHR) on different areas in the pumpcompressor pipes (PCP). Using a least-squares method and basing the analysis on the layer statistical data (or well histories, from the moment of starting of exploitation of the well to present), the computational algorithm is proposed to find the CHR on every given area of lift. An illustrative example is presented.
Entrepreneurial Intentions among University Students : Motivations and entrepreneurial exposure as drivers of intentions

By conducting a structural equation modeling analysis, the present study tested a model of entrepreneurial intentions. We examined the effect of motivational factors and entrepreneurial exposure on students’ intentions to new venture creation through the mediating role of work attitudes and self-efficacy. Data were collected among 171 undergraduate students from three universities. On the one side, results indicate that intrinsic motivations have a positive indirect influence on entrepreneurial intentions through the mediation of work attitudes. On the other side, Self-efficacy mediates the relationship between entrepreneurial exposure and intentions. Discussion and implications for future research are presented.
Data Driven Leakage Detection and Classification of a Boiler Tube

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.
Patient Modeling for Next Generation Remote Patient Management Systems: Heart Failure Hospitalization Prediction

In order to maintain and improve the quality of care without exploding costs, healthcare systems are undergoing paradigm shift from patient care in the hospital to patient care at home. Remote patient management (RPM) systems offer a great potential in reducing hospitalization costs and worsening of symptoms for patients with chronic diseases, e.g. heart failure, and diabetes. Different types of data collected by RPM systems provide an opportunity for personalizing information services, and alerting medical personnel about the changing conditions of the patient. Early and highly accurate detection of situations that lead to worsening patient’s conditions (e.g. possible hospitalizations due to HF) is very important so that more patients will receive timely appropriate feedback (e.g. instruction or education) to overcome their conditions. However, the feedback provided by RPM systems nowadays is generic and given to all patients regardless of their personality or current condition. Additionally, although richness of data provides an opportunity for tailoring and personalizing information services, there is a limited understanding of the necessary architecture, methodology, and tailoring criteria to facilitate personalization. In this thesis we tackle these problems by presenting a possible next generation RPM system that enables personalization of educational content and its delivery to patients, and introducing a generic methodology for knowledge discovery (KDD) for patient modeling. We especially focus on a particular problem of patient modeling that is the HF hospitalization prediction. We consider the process of learning a predictive model from RPM data (collected during a clinical trial). The results of our experimental study illustrate that with the intelligent data analysis approach we can build models which are significantly more accurate than expert-based (pre)authored decisioning.
ASTEROID RENDEZVOUS UNCERTAINTY PROPAGATION

Most methods of propagating orbit uncertainty assume posteriori Gaussian distributions, require an intrusive implementation or suffer from the curse of dimensionality associated with high-dimensonal random inputs. Although Monte Carlo techniques avoid these drawbacks, the approach has a slow convergence rate. This paper considers the application of separated representations for orbit uncertainty propagation and discusses the theory behind their generation. The computation cost of a separated representation is largely linear with respect to dimension, thereby improving tractability when compared to methods that suffer from the curse of dimensionality. Generation of a separated representation requires the propagation of a small number of samples and yields an approximate solution, or surrogate, to a given stochastic differential equation describing the propagated orbit. This surrogate provides information on the moments and spatial density of possible solutions, as well as the sensitivity of the quantities of interest with respect to random inputs. This paper presents the case of spacecraft targeting an asteroid for a rendezvous, for which the initial conditions of each and the components of the interceptor maneuver are uncertain. Separated representations is used to estimate the probability of a successful rendezvous and analyze the resulting probability distribution functions.
The THU-SPMI CHiME-4 system : Lightweight design with advanced multi-channel processing , feature enhancement , and language modeling

In this paper, we describe our lightweight system designed for CHiME-4. For multi-channel processing, we experiment with a bundle of beamforming methods, including minimum variance distortionless response (MVDR), parameterized multi-channel wiener filter (PMWF), generalized sidelobe canceller (GSC), spectral mask estimation (ME), and compare these techniques with the same back-end. Combining MVDR’s distortionless and reliable estimation of the steering vector by ME is found to be most effective. We propose to applying histogram equalization (HEQ) to compensate for the residual noise in the MVDR beamformed speech. We apply the recently introduced transdimensional random field (TRF) language model and confirm its superiority in rescoring. In combination these techniques are surprisingly effective in the CHiME-4 task, achieving 6.55% word error rate (WER) for the real evaluation data while keeping low system complexity. Applying multi-channel training further reduces the WER to 5.81%.