Reviewerly/exHarmony huggingface dataset:
Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching

This article contributes to the discourse on how contemporary computer and information technology may help in improving foreign language learning not only by supporting better and more flexible workflow and digitizing study materials but also through creating completely new use cases made possible by technological improvements in signal processing algorithms. We discuss an approach and propose a holistic solution to teaching the phonological phenomena which are crucial for correct pronunciation, such as the phonemes; the energy and duration of syllables and pauses, which construct the phrasal rhythm; and the tone movement within an utterance, i.e., the phrasal intonation. The working prototype of StudyIntonation Computer-Assisted Pronunciation Training (CAPT) system is a tool for mobile devices, which offers a set of tasks based on a “listen and repeat” approach and gives the audio-visual feedback in real time. The present work summarizes the efforts taken to enrich the current version of this CAPT tool with two new functions: the phonetic transcription and rhythmic patterns of model and learner speech. Both are designed on a base of a third-party automatic speech recognition (ASR) library Kaldi, which was incorporated inside StudyIntonation signal processing software core. We also examine the scope of automatic speech recognition applicability within the CAPT system workflow and evaluate the Levenstein distance between the transcription made by human experts and that obtained automatically in our code. We developed an algorithm of rhythm reconstruction using acoustic and language ASR models. It is also shown that even having sufficiently correct production of phonemes, the learners do not produce a correct phrasal rhythm and intonation, and therefore, the joint training of sounds, rhythm and intonation within a single learning environment is beneficial. To mitigate the recording imperfections voice activity detection (VAD) is applied to all the speech records processed. The try-outs showed that StudyIntonation can create transcriptions and process rhythmic patterns, but some specific problems with connected speech transcription were detected. The learners feedback in the sense of pronunciation assessment was also updated and a conventional mechanism based on dynamic time warping (DTW) was combined with cross-recurrence quantification analysis (CRQA) approach, which resulted in a better discriminating ability. The CRQA metrics combined with those of DTW were shown to add to the accuracy of learner performance estimation. The major implications for computer-assisted English pronunciation teaching are discussed.
Feature Fusion Based on Bayesian Decision Theory for Radar Deception Jamming Recognition

As an important part of electronic warfare, radar countermeasure determines the trend of war to a large extent. Modern radar jamming technology, especially deception jamming technology, plays an increasingly important role. Therefore, how to identify radar deception jamming is very necessary. In this paper, a feature fusion algorithm based on Bayesian decision theory is used to recognize radar deception jamming signals. Firstly, the real echo signal, deception jamming signal (contains range gate pull-off jamming, velocity gate pull-off jamming and range-velocity gate pull-off jamming) and noise signal received by radar are acquired as signal sources. Then bispectrum transformation is used to extract features in several aspects. Finally, kernel density estimation is used to improve the fusion algorithm, and the feature fusion algorithm based on Bayesian decision theory is used to recognize the received signals from radar. Results of the experiment indicate that the algorithm not only can recognize the radar deception jamming, but also has high accuracy.
A Systematic Review of Augmented Reality in STEM Education

This study presents a systematic review of the existing literature on the benefits and challenges faced regarding the use of augmented reality (AR) in Science, Technology, Engineering, and Mathematics (STEM) education. Guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, a systematic review of the ScienceDirect database identified 19 related studies. Further investigation of these articles resulted in four themes of AR advantages, namely its contribution to learners, the learning outcomes, the interaction of AR, and other benefits. These four themes further produced a total of 16 sub-themes, while the challenges aspect of AR resulted in 5 sub-themes. The most reported benefit of AR is that it stimulates learning achievement. Some observed that the challenges imposed by AR are concerned with marker detection and usability. Several other problems and advantages of AR usage in STEM education were also discovered, which will be discussed in detail. Additionally, the existing gaps of AR study in the STEM education field were identified, and recommendations are therefore presented for future research.
Chaotic Arc Adaptive Grasshopper Optimization

The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://aliasgharheidari.com.
Anomalous Event Recognition in Videos Based on Joint Learning of Motion and Appearance with Multiple Ranking Measures

Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking Measures (DMRMs), which addresses context-dependency using a joint learning technique for motion and appearance features. In DMRMs, the spatial-time-dependent features are extracted from a video using a 3D residual network (ResNet), and deep motion features are extracted by integrating the motion flow maps’ information with the 3D ResNet. Afterward, the extracted features are fused for joint learning. This data fusion is then passed through a deep neural network for deep multiple instance learning (DMIL) to learn the context-dependency in a weakly-supervised manner using the proposed multiple ranking measures (MRMs). These MRMs consider multiple measures of false alarms, and the network is trained with both normal and anomalous events, thus lowering the false alarm rate. Meanwhile, in the inference phase, the network predicts each frame’s abnormality score along with the localization of moving objects using motion flow maps. A higher abnormality score indicates the presence of an anomalous event. Experimental results on two recent and challenging datasets demonstrate that our proposed framework improves the area under the curve (AUC) score by 6.5% compared to the state-of-the-art method on the UCF-Crime dataset and shows AUC of 68.5% on the ShanghaiTech dataset.
A Systemic Framework to Evaluate Student Satisfaction in Latin American Universities under the COVID-19 Pandemic

Latin American universities (LAUs) have been going through a serious lack of economic resources which has plunged them into a deep financial crisis. The COVID-19 pandemic has exacerbated this calamity. However, LAUs have implemented online teaching processes in order to mitigate the effects of scheduling and other classroom disruption. There is evidence that these modes of teaching have had a reasonable reception but the level of student satisfaction is yet unknown. This article takes a systemic view of the predicament facing LAUs. It represents the elements related to the disruption caused by COVID-19 in a rich picture, building a systemic framework to explore student satisfaction with remote teaching. Using a sample of 298 students from Brazil, Colombia, and Peru, the study analyzes their situation through: (a) Well-being, educational resources, and learning experience and; (b) General satisfaction with virtual classes. Applying exploratory factor analysis, this study identifies three dimensions: (a) satisfaction with support and adaptation in the virtual modality; (b) satisfaction with the interaction in the virtual classroom; and (c) satisfaction with the development of the study program. Medium/high scores for the dimensions indicate moderate/high levels of satisfaction. The findings suggest that there are still unsatisfied needs regarding access to digital resources and socio-emotional needs. This article could be of interest to Higher Education Institutions (HEI) planners dedicated to post-pandemic, virtual education.
Two-parameter counter-diabatic driving in quantum annealing

We introduce a two-parameter approximate counter-diabatic term into the Hamiltonian of the transverse-field Ising model for quantum annealing to accelerate convergence to the solution, generalizing an existing single-parameter approach. The protocol is equivalent to unconventional diabatic control of the longitudinal and transverse fields in the transverse-field Ising model and thus makes it more feasible for experimental realization than an introduction of new terms such as non-stoquastic catalysts toward the same goal of performance enhancement. We test the idea for the $p$-spin model with $p=3$, which has a first-order quantum phase transition, and show that our two-parameter approach leads to significantly larger ground-state fidelity and lower residual energy than those by traditional quantum annealing as well as by the single-parameter method. We also find a scaling advantage in terms of the time to solution as a function of the system size in a certain range of parameters as compared to the traditional methods.
Image Encryption Scheme with Compressed Sensing Based on a New Six-Dimensional Non-Degenerate Discrete Hyperchaotic System and Plaintext-Related Scrambling

Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.
Online Extremism and Terrorism Research Ethics: Researcher Safety, Informed Consent, and the Need for Tailored Guidelines

(2021). Online Extremism and Terrorism Research Ethics: Researcher Safety, Informed Consent, and the Need for Tailored Guidelines. Terrorism and Political Violence: Vol. 33, Ethics and Terrorism, pp. 367-380.
A survey on control for Takagi-Sugeno fuzzy systems subject to engineering-oriented complexities

Nonlinearities exist everywhere and the investigation on control problems for nonlinear systems is of both theoretical and practical significance. The Takagi-Sugeno (T-S) fuzzy techniques, as one of the powerful model-based tools for dealing with control problems for nonlinear systems, have received considerable and persistent research attention during the recent decades. Based on the T-S fuzzy model, many complicated nonlinear control problems have been addressed in the literature. In this paper, we aim to provide a survey with respect to the T-S fuzzy control problems with complexities that are frequently encountered in the engineering practice. Firstly, the backgrounds of the fuzzy control and engineering-oriented complexities are discussed. The considered complexities are divided into traditional engineering-oriented ones and the network-induced ones. The former mainly includes time-delays, parameter uncertainties, sensor/actuator faults and the latter contains network-induced phenomena, communication protocols, event-triggered schemes and cyber-attacks. Then, the recent progress on the T-S fuzzy control problems subject to these complexities has been reviewed in details. Finally, based on the literature review, the conclusions are drawn and some possible future topics are given.
LITERASI NUMERASI DALAM PEMBELAJARAN TEMATIK SISWA KELAS ATAS SEKOLAH DASAR

The purpose of this study was to describe numeracy literacy in the thematic learning of senior elementary school students. This writing is done through literature study. The object of this research is numeracy literacy and thematic learning. The research subjects were elementary school upper class students. The data collection tool in this research is to search journals contained in several electronic media such as digital libraries, the internet, through Google Scholar. The data analysis technique used in this study was annotated bibliography analysis. The results showed that numeracy literacy in thematic learning of upper grade students of elementary school Numeracy literacy in thematic learning of upper grade elementary school students can be done by providing stimulus to students. To stimulate student curiosity, the availability of facilities and infrastructure, the capacity of school residents, and the capacity of stakeholders, a numeracy literacy work program twice a week before learning hours takes place, a mathematics and non-mathematics teacher training for thematic learning based on numeracy literacy is held, and fostering love reading to students and learning experiences that are fun while stimulating the imagination.
Estimating Neural Network’s Performance with Bootstrap: A Tutorial

Neural networks present characteristics where the results are strongly dependent on the training data, the weight initialisation, and the hyperparameters chosen. The determination of the distribution of a statistical estimator, as the Mean Squared Error (MSE) or the accuracy, is fundamental to evaluate the performance of a neural network model (NNM). For many machine learning models, as linear regression, it is possible to analytically obtain information as variance or confidence intervals on the results. Neural networks present the difficulty of not being analytically tractable due to their complexity. Therefore, it is impossible to easily estimate distributions of statistical estimators. When estimating the global performance of an NNM by estimating the MSE in a regression problem, for example, it is important to know the variance of the MSE. Bootstrap is one of the most important resampling techniques to estimate averages and variances, between other properties, of statistical estimators. In this tutorial, the application of resampling techniques (including bootstrap) to the evaluation of neural networks’ performance is explained from both a theoretical and practical point of view. The pseudo-code of the algorithms is provided to facilitate their implementation. Computational aspects, as the training time, are discussed, since resampling techniques always require simulations to be run many thousands of times and, therefore, are computationally intensive. A specific version of the bootstrap algorithm is presented that allows the estimation of the distribution of a statistical estimator when dealing with an NNM in a computationally effective way. Finally, algorithms are compared on both synthetically generated and real data to demonstrate their performance.
The Impact of the COVID-19 Movement Restrictions on the Road Traffic in the Czech Republic during the State of Emergency

The COVID-19 pandemic crisis has impacted numerous areas of people’s work and free-time activities. This article aims to present the main impacts of the COVID-19 movement restrictions on the road traffic in the Czech Republic, measured during the first epidemic wave, i.e., from 12 March to 17 May 2020. The state of emergency was imposed by the Czech government as a de jure measure for coping with the perceived crisis, although the measure eventually resulted only in a quite liberal de facto form of stay-at-home instruction. Unique country-scale traffic data of the first six months of 2020 from 37,002 km of roads, constituting 66% of all roads in the Czech Republic, were collected and analyzed. For the prediction of the prepandemic traffic conditions and their comparison with the measured values in the period of the state of emergency, a long-term traffic speed prediction ensemble model consisting of case-based reasoning, linear regression, and fallback submodels was used. The authors found out that the COVID-19 movement restrictions had a significant impact on the country-wide traffic. Traffic density was reduced considerably in the first three weeks, and the weekly average traffic speed in all road types increased by up to 21%, expectedly due to less crowded roads. The exception was motorways, where a different trend in traffic was found. In sum, during the first three weeks of the state of emergency, people followed government regulations and restrictions and changed their travel behavior accordingly. However, following this period, the traffic gradually returned to the prepandemic state. This occurred three weeks before the state of emergency was terminated. From a behavioral perspective, this article briefly discusses the possible causes of such discrepancies between de jure and de facto pandemic measures, i.e., the governmental communication strategy related to loosening of movement restrictions, media reality, and certain culture-related traits.
A Vision-Based Target Detection, Tracking, and Positioning Algorithm for Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAV) play a pivotal role in the field of security owing to their flexibility, efficiency, and low cost. The realization of vehicle target detection, tracking, and positioning from the perspective of a UAV can effectively improve the efficiency of urban intelligent traffic monitoring. In this work, by fusing the target detection network, YOLO v4, with the detection-based multitarget tracking algorithm, DeepSORT, a method based on deep learning for automatic vehicle detection and tracking in urban environments, has been designed. With the aim of addressing the problem of UAV positioning a vehicle target, the state equation and measurement equation of the system have been constructed, and a particle filter based on interactive multimodel has been employed for realizing the state estimation of the maneuvering target in the nonlinear system. Results of the simulation show that the algorithm proposed in this work can detect and track vehicles automatically in urban environments. In addition, the particle filter algorithm based on an interactive multimodel significantly improves the performance of the UAV in terms of positioning the maneuvering targets, and this has good engineering application value.
Arabic sentiment analysis about online learning to mitigate covid-19

Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.