Reviewerly/exHarmony huggingface dataset:
Storytelling with Holograms for the Development of Reading Competence

The use of emerging technologies such as interactive 3D virtual environments has improved the learning process in different areas in recent years, but their lack of interest and lack of interest in Educational Institutions for the use of these technologies and the use of traditional development strategies of reading competence, resulting in poor school performance in the area of communication and a decrease in PISA (Program for International Student Assessment). This paper proposes the creation and implementation of a low-cost, real-time holographic image generation model, using Blender, Kinect to detect the movements of a person and Ni-Mate that manages the data captured by the Kinect device. The research consists of five stages, the first one analyzes, designs and develops the assessment tools for reading competence, as the second stage the compilation of appropriate readings within the framework of the reading competence in PISA, so the third stage is an analysis and 3D model design, based on a satisfaction survey, as fourth the adequacy of space for better visualization and interaction of the teacher who tells the stories with the holograms generated and finally consider the research variables to make a comparison of the results obtained. In this project that proposes the use of hardware and software for the generation of low-cost interactive holograms, it will improve reading skills in students of Regular Basic Education (EBR) of primary level.
Educational Video Classification Using Fuzzy Logic Classifier Based on Arabic Closed Captions

The process of adding closed-captions to the E-Learning courses offers several benefits to the hard of hearing or deaf people. Closed captioning allows many different populations of viewers to access the contents of videos. Traditionally deaf/hard of hearing users are the population of this service, but captioning also helps non-native language speakers and also viewers with learning disabilities. Closed captions can be useful to the viewers with no hard of hearing such as: improving the comprehension dialogues for non-native speakers, providing the ability to observe videos in sound-aware environments such as libraries and offices, and recognizing the technical terminologies and brand names behind noise environments. In this paper, a video classification technique for Arabic closed captions to create semantic categories using Fuzzy logic is introduced. This classification is important to apply indexing and searching as meta-data which makes the involved videos text-searchable. This contribution will be focused to help the layer of society which has a defect hearing problem but not deaf, by integrating them with the normal students in the regular schools and keep them in a good level of education without the need to join them to special needs schools.
Feature Selection Models for Data Classification: Wrapper Model vs Filter Model

Data Classification is a managed learning process, which sort and organize the data into various categories to be used in more effective and efficient way. Feature selection (FS) is a noteworthy theme for the advancement of data classification process. It is regularly a basic information handling step preceding applying a learning algorithm, where the choosing of the most fitting subset of features that portrays a given classification task and the removing of irrelevant and repetitive information is occurred, hence this increases the performance of machine learning algorithms. Usually, two public methods of feature selection are used: a wrapper method in which the proposed learning algorithm itself is used to estimate how much the features are helpfully and a filter method, which assesses features heuristically, depend on the data general characteristics. In this work, the goal is to make a performance and effectiveness analysis of feature selection methodologies. This analysis handles a comparison between wrapper and filter method, by applying them on seven datasets prior to using these datasets in three classification algorithms. The results explains a set of important issues related to FS methods such as the selection taken time and the accuracy of the classification algorithms when apply FS methods to their testing datasets. The experiments were tested on seven standard datasets downloaded from multiple resources such as Machine Learning Repository and Weka tool. The experimental results have revealed that different feature selection methods can effectively enhance classification problems, but not all the time. No single method is the best for all datasets.
Affordable Annotation of the Mobile App Reviews

Abstract This paper focuses on the use-case study of the annotation of the mobile app reviews from Google Play and Apple Store. These annotations of sentiment polarity were created for later use in the automatic processing based on machine learning. This should solve some of the problems encountered in the previous analyses of the Czech language where data assumptions play a greater role than annotation itself (due to the financial constraints). Our proposal shows that some of the assumptions used for English do not apply to Czech and that it is possible to annotate such data without extensive financing.
Improved Scheme Securing Voting System for Elections

In this paper, we provide an improved Electronic Voting System (EVS) for political and social elections based on known Cryptographic schemes. In cryptography, the ElGamal encryption system is an asymmetric key encryption algorithm which is based on Diffie-Hellman key exchange. We will use the ElGamal algorithm to generate and encrypt random keys for the voters. This scheme is designed in a way that the communication channels are anonymous and in the meantime privacy, eligibility and fairness are applied to the entire system. Encrypted receipt-free transactions are provided to the voters after they submit a vote successfully. After the voting completes the voters can check on voting online using the encrypted receipt, only to confirm their participation in the election. The voters will not be aware of the chosen candidate to prevent cheating and vote selling, common issues in elections these days. The name of the chosen candidate will be confidential. The proposed voting system efficiently gives the opportunity for people to vote via their own PCs/laptops, thus decreases the queues accumulated up at voting centers. In addition, it offers a highly dependable authentication approach by national ID or biometrically which leads to overcoming electoral fraud.
Multi-condition Place Generator for Robust Place Recognition

As an image retrieval task, visual place recognition (VPR) encounters two technical challenges: appearance variations resulted from external environment changes and the lack of cross-domain paired training data. To overcome these challenges, multi-condition place generator (MPG) is introduced for data generation. The objective of MPG is two-fold, (1) synthesizing realistic place samples corresponding to multiple conditions; (2) preserving the place identity information during the generation procedure. While MPG smooths the appearance disparities under various conditions, it also suffers image distortion. For this reason, we propose the relative quality based triplet (RQT) loss by reshaping the standard triplet loss such that it down-weights the loss assigned to low-quality images. By taking advantage of the innovations mentioned above, a condition-invariant VPR model is trained without the labeled training data. Comprehensive experiments show that our method outperforms state-of-the-art algorithms by a large margin on several challenging benchmarks.
Infrared Versus Visible Image Matching for Multispectral Face Recognition

Multispectral face recognition has been an interesting area of research where images obtained from different bands are matched. There are many face image datasets available which contain infrared and visible images. In most face recognition applications, the IR image taken in different circumstances is matched against the visible image available in the application database. High computational cost is required for processing these images. In the literature, there is no guideline about the optimal number of features for dealing with multispectral face datasets. Thus, in this paper, we will perform image matching using infrared and visible images for face recognition and establish the threshold of the optimal number of features required for multispectral face recognition. The experiments conducted are on SCFace—surveillance cameras face database. The experimental setup for multispectral face recognition using LBP and PCA feature sets and the experimental results are discussed in the paper.
Development of an interactive shirt for self-directed motor learning

Purpose The purpose of this paper is to develop a framework for an interactive clothing that offers a self-directed learning environment in which learners can practice exercises in a time and cost-efficient manner. Design/methodology/approach To verify the validity of the framework, an interactive shirt has been developed that can help its wearer practicing certain motor skills in a self-directed manner. This shirt enables the wearer to set reference body postures and to compare current posture with them and can notify whether its wearer repeats them correctly or not through vibrotactile feedback. Findings The interactive shirt prototype developed in this study will offer an environment in which learners can practice exercises in a time and cost-efficient manner. Originality/value The smart garment framework developed in this study consists of sensor-actuator module, switch device and control software. As this framework is easily scalable, it is expected that it can be used for various smart garment projects where an interaction between the garment and its wearer is needed.
Default Rules in the Logic of First-Degree Entailments

Monotonicity is a property of the consequence relation of classical logic, which has been questioned for a range of reasons, some of which we overview. Default logic, which is a non-monotonic logic, was introduced to model defeasible conclusions that may be justified by default rules. In this paper, we scrutinize certain features of non-monotonicity of a consequence relation to legitimize it. The logic of first-degree entailments, which makes more distinctions than 2-valued logics does is especially suitable as the base logic for a system of default logic, because the applicability of default rules depends on the absence of formulas from a theory. We define two notions of extensions with default rules and illustrate their use by some standard as well as new examples.
3D Face Recognition Analysis Using Random Forest

Face recognition is an emerging field due to the technological advances in camera hardware and for its application in various fields such as the commercial and security sector. Although the existing works in 3D face recognition perform well, a similar experiment setting across classifiers is hard to find, which includes the Random Forest classifier. The aggregations of the classification from each decision tree are the outcome of Random Forest. This paper presents 3D facial recognition using the Random Forest method using the BU-3DFE database, which consists of basic facial expressions. The work using other classifiers such as Neural Network (NN) and Support Vector Machine (SVM) using a similar experiment setting also presented. As for the results, the Random Forest approach has yield 94.71% of recognition rate, which is an encouraging result compared to NN and SVM. In addition, the experiment also yields that fear expression is unique to each human due to a high confidence rate (82%) of subjects with fear expression. Therefore, a lower chance to be mistakenly recognized someone with a fear expression.
A Switching Morphological Algorithm for Depth Map Recovery

In this paper, we propose a switching morphological filter for RGB-D depth map recovery. The switching algorithm consists of the following steps: detection of noisy pixels and hollow areas (holes) using morphological filtering; correction of the detected noisy and hole pixels. With the help of computer simulation, we show that the proposed algorithm is able to fast recover depth maps. So, the accuracy of 3D surface reconstruction with the proposed filtering noticeably increases. The performance of the proposed algorithm is compared in terms of the accuracy of 3D surface reconstruction and processing time with that of common depth filtering algorithms.
Distributed Graph Analytics

Graph Analytics is important in different domains: social networks, computer networks, and computational biology to name a few. This paper describes the challenges involved in programming the underlying graph algorithms for graph analytics for distributed systems with CPU, GPU, and multi-GPU machines and how to deal with them. It emphasizes how language abstractions and good compilation can ease programming graph analytics on such platforms without sacrificing implementation efficiency.
Approximated Particle Flow for Continuous Filter, Predictor, and Smoother

The filtering and smoothing techniques for nonlinear continuous-discrete model which is applicable to physical state estimation problems are explained. The multiple distribution estimation and distribution selection are applied to design the non-Gaussian filter. After the execution of the filter, the non-Gaussian smoother is applied to improve the filtered state estimates further. The filtering and smoothing problem for the spacecraft re-entry simulation model is provided as the numerical example and the proposed approach is compared with the other non-Gaussian filters and smoothers.
Sparse Representation On Single Image

In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.
Self-organizing Migrating Algorithm with Non-binary Perturbation

The self-organizing migrating algorithm (SOMA) is a popular population base metaheuristic. One of its key mechanisms is a perturbation of the individual movement with a binary-valued perturbation (PRT) vector. The goal of perturbation is to improve the diversity of the population and exploration of the search space. In this paper, we study a variant of the SOMA algorithm with non-binary PRT vector. We investigate the effect of introducing a third possible value, a negative (repulsive) element, into the PRT vector. The aim is to slow the population convergence and prolong the exploration phase. The inspiration is taken from previous successful implementations of repulsive mechanics in another swarm-based method: the Particle Swarm Optimization.
Separate BOS-visualization of temperature and pressure distributions in a solid medium

Abstract Background Oriented Schlieren (BOS) is a technique widely used for visualizing disturbances in optically transparent objects. This method makes possible to visualize the variation of the refractive index, which depends on such physical parameters as temperature, pressure and concentration. Therefore, obtaining information about these parameters is also possible. In the course of the work, an experiment was conducted to visualize the optical inhomogeneity formed by changes in pressure and temperature in plane-parallel glass plate. Processing of experimental data has shown that under certain conditions it is possible to obtain information about the individual parameters of the environment which forms a complex inhomogeneity.
MODELING A COMMUNICATION SYSTEM USING LINEAR CODES AND EVALUATING THEIR EFFECTIVENESS

The paper presents some methods of linear coding: NRZ-L (Non-Return to Zero Level), NRZ-M (Non-Return to Zero Mark), RZ-AMI (Return to Zero Alternate Mark Inversion) and Manchester code. On the NI ELVIS II+ and module Emona DATEx it was succeeded to realize the prototype of digital communication system and estimate efficiency of signal demodulation.
Accurate and Robust Active Contour Model for Medical Image Segmentation and Correction

Magnetic resonance imaging has developed at an extremely rapid rate and magnetic resonance (MR) images can provide a lot of information for doctors to diagnose. However, MR images always suffer from intensity inhomogeneity which may cause difficulty in segmentation and analysis. This paper presents an accurate and robust active contour model for MR images. Inspired by the idea of the multiplicative intrinsic component optimization (MICO) model, we first define a data term by transforming the energy functional of the MICO model into the level set framework. Then, we define a new energy functional by incorporating a length term and an edge detector function into the data term. We present our model in both two-phase and four-phase level set formulations. The split Bregman method is applied to efficiently minimize the energy functionals. We apply our model to lots of brain MR images to test its performance. Experimental results show that our model can handle these images well even if they are polluted with serious bias field or shadows and is robust to noises. Comparison with the MICO model and the Coherent Local Intensity Clustering (CLIC) model also demonstrates the superiority of our model in terms of segmentation accuracy and correction effect.
Requirements Engineering Framework for Service and Cloud Computing (REF-SCC)

Requirements engineering (RE) is the most difficult and important stage of any business process or project development. This research endeavors to find out the characteristics and aspects of requirements engineering enforced by cloud computingCloud computing. Business Process Modeling NotationBusiness Process Modeling Notation (BPMN) (BPMN) has made an impact in the respect to capture the process and to make the changes accordingly for improvement in business operations. This chapter defines how BPMNBusiness Process Modeling Notation (BPMN) can be used as a method of requirements engineering in cloud business operations. Furthermore, this chapter presents the requirements engineering frameworkRequirements Engineering Framework (REF) for service andBusiness Process Modeling Notation (BPMN) cloud computingCloud computing (BPMN-REF-SCC) andService and Cloud Computing (SCC) will also discuss the reference architecture for service andCloud computing cloud computingService and Cloud Computing (SCC). Finally, the research delivers a case of financial cloud business, which has developed 15 hypotheses for the validation and evaluation through simulation.
DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training

Data parallelism has become the de facto standard for training Deep Neural Network on multiple processing units. In this work we propose DC-S3GD, a decentralized (without Parameter Server) stale-synchronous version of the Delay-Compensated Asynchronous Stochastic Gradient Descent (DC- ASGD) algorithm. In our approach, we allow for the overlap of computation and communication, by averaging in parameter space and compensating the inherent error with a first-order correction of the locally computed gradients. We prove the effectiveness of our approach by training Convolutional Neural Network with large batches and achieving state-of- the-art results.
Image Dehazing Using Degradation Model and Group-Based Sparse Representation

Handling images captured in hazy weather conditions is a big challenge, for they usually have poor visibility because of the light scattering as well as absorption effects. This paper addresses an integration of a degradation model constructor and a group-based sparse representation (GSR) strategy for single image dehazing. The degradation model is constructed based on a classical physical model, i.e., dichromatic atmospheric scattering model. Then, the new degradation model is integrated into the group-based sparse representation framework. Finally, the single image dehazing problem is regarded as an image restoration problem, which can be well optimized by GSR. The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations, and results indicate considerable improvement over existing algorithms.
Utilizing Tandem Features for Text-Independent Speaker Recognition on Short Utterances

This study focuses on the application of Deep Neural Networks trained to discriminate amongst senones to improve speaker recognition performance when dealing with text-independent Short Utterance Speaker Recognition (SUSR). The features derived from the said network are theorized to be more robust given that they can eliminate any unnecessary information in the final representation of the speaker. The efficacy of these features is evaluated using the test subset of the LibriSpeech speech corpus. It is found that the system's performance, especially when dealing with SUSR is greatly improved when these above-mentioned features are concatenated with traditional, more widely used Mel Frequency Cepstral Coefficients (MFCC) as measured in terms of Equal Error Rate (EER).
A New Binomial Crossover Considering Correlation Among Decision Variables for Adaptive Differential Evolution

When problems with strong dependency among decision variables are optimized, a characteristic distribution, which is a thin elliptical distribution, may appear. In order to generate good children, it is necessary to change the variables (genes) simultaneously along the long axis of the elliptical distribution. Since binomial crossover in differential evolution determines whether each gene is crossed or not with the same probability, it is difficult to change some genes simultaneously. In this study, we propose a crossover operation GBX which uses correlation coefficients of search points in order to detect such distribution. The highly correlated genes are grouped and the genes in each group are crossed (or not crossed) simultaneously. However, if only GBX is used, the diversity of the search points tends to be lost rapidly. The adaptive control of the probability for applying GBX is also proposed. The advantage of the proposed method is shown by solving several bench- mark problems.
Relocating Local Outliers Produced by Partitioning Methods

Partitioning methods is one of the most known and most used data clustering methods. The cluster center, known as centroid or medoid, represents each cluster that these methods produced. When the cluster center cannot describe all the data in the group, the undescribed data are mistakenly classified to their nearest cluster. These data are considered as local outliers. With this, the study seeks to address the problem of local outliers produced by partitioning methods specifically k-means and k-medoids. The Local Outlier Rectifier (LOR) is a proposed method used to relocate local outliers to its appropriate clusters. The results of the simulation show that several outliers are successfully transferred and the accuracy of the clustering performance increases.