Miracle-dz/UniFAR-FTUs huggingface dataset:
Rabbit: A Compiler for Scheme

We have developed a compiler for the lexically-scoped dialect of LISP known as SCHEME. The compiler knows relatively little about specific data manipulation primitives such as arithmetic operators, but concentrates on general issues of environment and control. Rather than having specialized knowledge about a large variety of control and environment constructs, the compiler handles only a small basis set which reflects the semantics of lambda-calculus. All of the traditional imperative constructs, such as sequencing, assignment, looping, GO TO, as well as many standard LISP constructs such as AND, OR and COND, are expressed as macros in terms of the applicative basis set. A small number of optimization techniques, coupled with the treatment of function calls as GO TO statements, serves to produce code as good as that produced by more traditional compilers.
Research on Vehicle Classification and Recognition Method Based on Vehicle Acoustic Signal CNN Analysis

—The present "shallow classification model" have shortcomings on modeling and representation ability, feature extraction, classification performance and so on. This study aims to improve the typical LeNet-5 convolution neural network and obtain three kinds of CNN structures to realize the classification of large and small vehicles. Firstly, we extracted the MFCC feature of vehicle acoustic signals; then took the feature signals as training samples; lastly adjusted the study rate, convolution kernel size and quantity in accordance with experiment and obtained the results. The experimental results indicate that the improved CNN model is better than the traditional machine learning method; and the classification performance of the improved CNN model is improved with the increase of data volume, and the accuracy of the test samples is 96.8%.
A Simulation Model Design Method for Cloud-Based Simulation Environment

Simulation technologies provide necessary validation tools for the conceptual design of complex products involving multiple disciplines. A variety of simulation models are developed in specific organizations or enterprises to verify the design plan, while they are hard to be shared and to be reused. Based on the analysis of several typical solutions to share and reuse different kinds of resources, a simulation model design method is proposed to provide a simple implementation of simulation model reuse for cloud-based simulation environment. This paper firstly creates the simulation models' metamodel and ontology for their universal description. Secondly, four rules are proposed to design/reprogram a simulation model into service-oriented form for its interoperability, and the ontology of service-oriented simulation model is established. Thirdly, the way to call one simulation model and the way to compose several simulation models into a simulation process are elaborated. Finally, a simple case of using this method to design an aircraft dynamic model is elaborated, and a prototype simulation system is constructed, and then a simple simulation process is composed to verify the practicability of the method. The result shows that the new design/reprogram method has big advantages on the compatibility, expansibility, and reusability despite the decreasing efficiency.
Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap.

Existing fully parametric multiple imputation (MI) techniques typically assume simple random sample designs, relying on use of design-based estimators at the analysis stage to account for design effects. More complex methods which include the sample design in the formulation of the imputation model typically require strong model assumptions and expensive modeling and computation, and may still fail to be fully “congenial”. This dissertation develops an innovative multiple imputation framework (termed “two-step MI”) that accounts for complex sample designs through synthetic data generation and simple parametric models for imputation of missing values. We term this approach “two-step MI”, since we first generate posterior predictive distributions of the population that includes the (item-level) missing data, then fill in the item-level missing data using standard parametric MI techniques. The first paper outlines the conceptual framework and develops a modified set of the standard MI combining rules for inference based on the new method. The focus is on the role of survey weights in conjunction with MI to adjust for item nonresponse. The new procedure uses a weighted finite population Bayesian bootstrap that generates posterior predictive distributions of the finite population that are free of complex design features. As a result, analysts need only to apply simple unweighted estimation methods to the imputed datasets, and, depending on the missingness mechanism, can ignore the sample design in the imputation procedure. Our simulation assuming a PPS sampling design shows that the proposed method achieves good frequentist properties in contrast to many alternative standard approaches that are not robust to model misspecification. The second paper extends the two-step MI to two-stage cluster sample design settings and develops two variations of the proposed procedure for simultaneously resolving clustering effects and “inversing” survey weights. Their performances are evaluated under a variety of simulation conditions in comparison with existing MI techniques. The third paper develops a general methodology to account for stratification effects in a highly stratified design. Quantile estimation and binary rare events data are investigated as well. Extensive analyses are conducted for real data applications on the Behavior Risk Factor Surveillance System (BRFSS), the Delta-V measure from NASS-CDS crash record data and Body Mass Index data from NHANES III.
Geochemical indicators of paleo-seismicity based on the data of study of Fumarolnoe lake bottom sediments (Kamchatka, Uzon)

The distribution of chemical elements in bottom sediments of Fumarolnoe lake was obtained by XRFA. The mineral composition of the sediment was examined by SEM, XPA. Variations in contents of alkaline elements in the column of bottom sediments of the Fumarolnoe Lake have been studied using spectral methods (Wavelet and Fourier analyses). It has been established that concentrations of these elements change periodically, periods being correlated with periodic changes in seismic conditions of the present region
The cross-bicepstrum: properties and applications for signal reconstruction and system identification

Complex cepstrum techniques are applied to cross-bispectra in order to simultaneously reconstruct three independent finite-time signals, given the cross-bispectrum of the three signals only. The method is applied to simultaneously identify three systems via the cross-bispectrum of their outputs, knowing only the statistics of the input. A least-squares method is presented to estimate the cross-bicepstral parameters of the three signals simultaneously. The method is nonparametric and noniterative, assuming only that the signals (or impulse responses) are exponential, stable, and have no zeros on the unit circle. It takes full advantage of the two-dimensional nature of the bispectrum to reconstruct the three signals (or identify the three systems) simultaneously. <<ETX>>
Effective Permittivity of Saline Ice Under Thermal Variation

A model for calculating the effective permittivity of saline ice under thermal variation is presented. The model includes multiphase inhomogeneities with multiple species characterized by orientation, size and shape distributions. The model is used to derive the effective permittivity as a function of temperature under the strong fluctuation theory which is extended to account for the complexity. The results calculated from the model are compared with experimental data at 4.8 GHz for saline ice grown at the US Army Cold Regions Research and Engineering Laboratory (CRREL). The comparison between measured and calculated complex permittivities is good for the imaginary part, and the difference is within 10 percent for the real part.
Feature extraction with wavelet transform for recognition of isolated handwritten Farsi/Arabic characters and numerals

A system is developed for recognition of handwritten Farsi/Arabic characters and numerals. The discrete wavelet transform is utilized to produce wavelet coefficients, which are used for classification. We used Haar wavelet for feature extraction in this system. The extracted features are used as training inputs to a feed forward neural network using the backpropagation learning rule. The learning and test patterns were gathered from various people with different educational backgrounds and different ages. We categorize 32 characters in Farsi language to 8 different classes in which characters of each class are very similar to each others. There are ten digits in Farsi/Arabic languages, but two of them are not used in postal codes in Iran, so we have 8 different extra classes for digits. This system yields the classification rates of 92.33% and 91.81% for these 8 classes of handwritten Farsi characters and numerals respectively. We used this system for recognizing the handwritten postal addresses which contain the names of cities and their postal codes. Our database contains 579 postal addresses in Iran. The system yields a recognition rate of 97.24% for these postal addresses.
Cross-Country Differences in Parental Reporting of Symptoms of ADHD

Previous studies within the United States suggest there are cultural and contextual influences on how attention-deficit/hyperactivity disorder (ADHD) symptoms are perceived. If such influences operate within a single country, they are likely to also occur between countries. In the current study, we tested whether country differences in mean ADHD scores also reflect cultural and contextual differences, as opposed to actual etiological differences. The sample for the present study included 974 participants from four countries tested at two time points, the end of preschool and the end of second grade. Consistent with previous research, we found lower mean ADHD scores in Norway and Sweden in comparison with Australia and the United States, and we tested four explanations for these country differences: (a) genuine etiological differences, (b) slower introduction to formal academic skills in Norway and Sweden than in the United States and Australia that indicated a context difference, (c) underreporting tendency in Norway and Sweden, or (d) overreporting tendency in the United States and Australia. Either under- or overreporting would be examples of cultural differences in the perception of ADHD symptoms. Of these explanations, results of ADHD measurement equivalence tests across countries rejected the first three explanations and supported the fourth explanation: an overreporting tendency in the United States and Australia. These findings indicate that parental reporting of ADHD symptoms is more accurate in Norway and Sweden than in Australia and the United States, and, thus, have important clinical and educational implications for how parental reporting informs an ADHD diagnosis in these countries.
An automatic light spectrum compensation method for CCD white balance measurement

The light source spectrum of a test system has been used to compensate the CCD response under mid-range exposure for variations in the illuminator. The CCD white balance can then be calculated and screened based on the ratios of the compensated R, G and B signals. First, a reference response is calculated based on the convolution of a reference light spectrum with a representative CCD responsivity. Then a current theoretical response is calculated by replacing the reference spectrum with the current light spectrum. The ratio of the reference response and current theoretical response provides the spectrum compensation factors to correct the measured CCD response in each of the three color channels. With these corrections, a CCD white balance measurement will reflect the true color filter characteristics. Error analysis shows that without correction, a light source spectral shift can cause a first order error in the sensitivity measurement. However the proposed correction method compensates this spectrum-induced error while introducing only a second order error from the difference of the responsivity curves between the CCD-under-test and the reference CCD.
Judo athletes´perceptions of two training models

Background & Study Aim: Training process planning and application is, first and foremost, the result of coach’s thinking. Subjective measures of training loads are useful tools which should be used with confidence by coaches. The aim of study the knowledge about judo athletes’ perceptions, after two different training models, of the following indicators: motivation, pleasure, fatigue, understanding, competitive specificity, physical fitness, tactical-technical preparation, randori variability, creativity, problem-solving and general evolution. Material & Methods: Twenty-four athletes from the U-18 (n = 14) and U-21 (n = 10) were randomly separated into two groups of 12 subjects. For two weeks, each group underwent both training models: Traditional Training (TT) and Functional Units Training (FUT). After the two weeks, an interview was conducted in order to collect athletes’ perceptions of both training models regarding each indicator. Results: Study participants considered that FUT was more efficient in the following categories: motivation; pleasure; understanding; competitive specificity; technical-tactical preparation, randori variability; creativity; problemsolving and general evolution, while TT was perceived as superior in the other two categories analysed: fatigue and physical fitness. Conclusions: After the two training processes, study participants considered FUT to be the most effective model for judo training.
Availability comparison of different protection mechanisms in SDH ring network

This work analyzes the ring network availability based on SDH technology. Two protection mechanisms are specially analyzed: path protection and self healing ring with two and four fibers. Calculation of availability is made using the data pertaining to the failures of optical cables and equipment which are obtained during years-long observation of the SDH network of HPT Mostar as well as those obtained from manufacturers.
A Novel Approach in Hand Gesture Recognition for Sign Language

— This paper presents an enhanced algorithmic approach for recognizing the symbols of the American Sign Language to reduce the communication gap between the hearing impaired people and the rest of the world. In sign language, each gesture has an assigned meaning (or meanings). This research mainly focuses on depicting the corresponding symbols of the American Sign Language (ASL) mapped to hand gesture. Our approach is based on image pre-processing, image database generation and key point comparison techniques, to detect the corresponding hand gesture through computer vision and machine learning approaches. This could pave way to achieve the goal of implementing an effective system in order to shrink the communication gap between the normal and the hearing impaired people. We’ve used MATLAB as the programming platform for this research. The Scale Invariant Feature Extraction (SIFT) algorithm, Nearest Neighbor search and new concepts have been used to as an effective and convenient way to process hand gestures and recognize them accurately. This new approach brings more benefits compared to existing approaches and related research concepts.
Transparallel processing by hyperstrings.

Human vision research aims at understanding the brain processes that enable us to see the world as a structured whole consisting of separate objects. To explain how humans organize a visual pattern, structural information theory starts from the idea that our visual system prefers the organization with the simplest descriptive code, that is, the code that captures a maximum of visual regularity. Empirically, structural information theory gained support from psychological data on a wide variety of perceptual phenomena, but theoretically, the computation of guaranteed simplest codes remained a troubling problem. Here, the graph-theoretical concept of “hyperstrings” is presented as a key to the solution of this problem. A hyperstring is a distributed data structure that allows a search for regularity in O(2N) strings as if only one string of length N were concerned. Thereby, hyperstrings enable transparallel processing, a previously uncharacterized form of processing that might also be a form of cognitive processing.
Notice of Retraction Object Tracking by Mean Shift Based on Color Distribution and Simulated Annealing

Easily falling into local extremum, plateaus, and fast moving targets couldn't tracked, which are main handicap to mean shift application, especially in those cases to track the multi-articulated human body fine features. Based on the analysis of the causes of the mean shift, in this paper, a new tracking approach, which is the mean shift based on color distribution and simulated annealing (SACD-MS), is proposed for human body tracking. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant. And simulated annealing has the characteristics such as robustness and high efficiency, which can improve the veracity. Local extremum and plateaus of the mean shift can be avoided through information exchange based on color distribution and simulated annealing, and the target candidate positions are added, thereby converging at the mode values of the target candidate region efficiently. At the same time, the tracking ability is ameliorated even if having the occlusion, rapid movement. Experimental results indicate that the proposed algorithm of the SACD-MS is robust and practical.
Neural networks for accurate estimation of software metrics

Nowadays, software requirements are widely increasing and modern software technologies are rapidly growing. Therefore, planning and managing the software projects are more important as compared to the past. Unsuitable project planning has been the main reason of software project fails in recent years. Estimating is performed at the early stages of project and it is one of the most important activities in software project planning. During the recent years many different methods have been proposed to estimate the software metrics. Software development effort and software size are most important metrics in this field. Since uncertain nature of software projects makes it difficult to estimate the metrics, soft computing techniques have been widely used in software metrics estimation. These techniques can improve the accuracy of estimations in software projects by means of neural networks, genetic algorithm, fuzzy logic and so on. Among all mentioned methods, due to high flexibility and adaptability, neural networks have been used more than the other methods. A comparative study can be useful to discriminate the performance of neural networks regarding the software metrics estimation. In this paper 15 previous research works in term of using neural networks for software estimation were investigated basically. Analyzing the selected research works showed that neural networks can estimate the software metrics more accurate than most common algorithmic methods.
Accounting for near-wellbore fracture behavior to unlock the potential of unconventional gas wells

Despite ongoing engineering efforts, unconventional horizontal gas wells completed with the multi-stage hydraulic fracturing technique are not yet producing to their full potential, as a meaningful proportion of clusters are producing marginally if at all. In this paper, we advocate that the near-wellbore behavior of the fractures could explain some of that behavior. We propose a method involving a test well completed with the pin-point fracturing technique to sample the variability in the near-wellbore behavior. With the help of a dedicated fully coupled hydraulic fracturing simulator, we show that the optimum number of clusters per stage can be selected taking into account this new information. The various steps of this method are illustrated with an example.
Comparison of the electromagnetic properties of building materials at 5.8 GHz and 62.4 GHz

Results of experiments made to the electromagnetic properties of typical building materials at 5.8 GHz and 62.4 GHz are presented and compared. Both transmission and reflection coefficients were measured as functions of the angle of incidence. A multiple successive internal reflection model was developed and used to estimate the dielectric constants by comparing measured and predicted values. It has been shown that, at a given frequency, the type of the material used determines the value of its dielectric constant. The variation of the value of this constant with frequency does not necessary follow the same trend for different materials.
DETERMINING MORPHOMETRIC PROPERTIES OF RADIATA PINE USING LONG WAVE INFRARED SENSING AND BIOLOGICALLY-INSPIRED VISION

Abstract. A miniaturised, high resolution visible and long wave infrared (LWIR) sensor was carried onboard an unmanned aerial vehicle to observe sections of radiata pine forests. The raw irradiance measurements were temporally pre-processed using a biologically-inspired vision (BIV) model to allow information within and across the images to be normalised. This permitted a larger, denser, and more tailored set of key points within the 2D image stacks to be corresponded, thereby improving 3D reconstructions of individual trees derived using structure from motion (SfM). The BIV model comprises multiple layers of processing derived from measured or assumed responses of the photoreceptor cells in the hoverfly. Its pre-processing expands the range of input signal obtained from the LWIR sensor and enhances foreground-background contrast. Morphological image processing techniques were also applied to enhance key image features before structure from motion is applied. The result allows structural properties of individual trees to be characterised in terms of their potential volume and quality; and contrasted with the point clouds obtained from the visible imagery that only depicts the tree canopies.
Single-channel EEG-based prosthetic hand grasp control for amputee subjects

This article explores the use of single-channel single-trial EEG signals for natural control of prosthetic hand grasp. It is natural in the sense of that the desired movement is what the subject intends to do. The motor tasks to be intended are the imagination of hand grasping and opening. For prosthetic hand grasp control, the discrimination of the resting state and the imagined voluntary movement is important that has been disregarded in the research area of BCI. This work provides a design for discriminating the resting state and the motor task imagery. To date most researchers have designed and test BCI system on normal subjects. In this work, the experiments were conducted on people with severe physical disabilities. One of the major problem in developing real-time BCI is the eye blink suppression. In this work, the eye blink artifact is suppressed by a neural adaptive noise canceller. This is a concern in real time application. We employ the multilayer perceptron with back-propagation learning rule for EEG classification. Preliminary results indicate that the classification accuracy of the EEG patterns at primary motor cortex and occipito-temporal recording sites is higher than that at other sites. An average correct classification rate of 83% is achieved using samples of the single-channel EEG signal.