Reviewerly/exHarmony huggingface dataset:
Authentic-interactive activities to promote oral production on a virtual platform

Implementing learning or communicative strategies are not enough to develop oral production when learning English as a Foreign language; indeed, it is necessary to provide authentic activities that engage students in the act of speaking. The objective of this research is to know if the interactive activities operated in the Moodle platform promote oral production and identify the possible association between their scores from the instructional design and their speaking level from a standardized exam. For this, a longitudinal quantitative approach was carried out during spring 2020 in the Teaching English Bachelor at BUAP, having a sample of 20 subjects. The results demonstrated that the use of authentic-interactive activities improved the students’ speaking competence significantly. Furthermore, this study pretends to contribute with useful theoretical information for teachers and students who want to select appropriate tasks to improve this competence. It is essential to clarify that this is an innovative study since it promotes the use of activities and strategies mediated by technology asynchronously and synchronously, which are flexible and suitable for students’ needs in times of contingence.
Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

&lt;span&gt;Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.&lt;/span&gt;
Towards a Verification-Driven Iterative Development of Software for Safety-Critical Cyber-Physical Systems

Abstract Software systems are complicated, and the scientific and engineering methodologies for software development are relatively young. Cyber-physical systems are now in every corner of our lives, and we need robust methods for handling the ever-increasing complexity of their software systems. Model-Driven Development is a promising approach to tackle the complexity of systems through the concept of abstraction, enabling analysis at earlier phases of development. In this paper, we propose a model-driven approach with a focus on guaranteeing safety using formal verification. Cyber-physical systems are distributed, concurrent, asynchronous and event-based reactive systems with timing constraints. The actor-based textual modeling language, Rebeca, with model checking support is used for formal verification. Starting from structured requirements and system architecture design the behavioral models, including Rebeca models, are built. Properties of interest are also derived from the structured requirements, and then model checking is used to formally verify the properties. This process can be performed in iterations until satisfaction of desired properties are ensured, and possible ambiguities and inconsistencies in requirements are resolved. The formally verified models can then be used to develop the executable code. The Rebeca models include the details of the signals and messages that are passed at the network level including the timing, and this facilitates the generation of executable code. The natural mappings among the models for requirements, the formal models, and the executable code improve the effectiveness and efficiency of the approach.
Opportunities for Organizing Distance Learning Presented by the Moodle Platform: Experience in the Conditions of the COVID-19 Pandemic

The implementation of modern information technologies in education presents a promising direction of the pedagogical science since it makes it possible to prove educational services through a specialized information educational environment – a combination of information resources, technical devices, and telecommunication channels enabling the exchange of educational information at a distance. The goal of the present study is to analyze and identify the opportunities of the Moodle platform in education as a whole and in studying English at a university in particular. The methodological basis of the study is formed by the analysis of the experience of teachers of the Department of English Language, the methods of teaching English, and a survey of experts in the area. Using the Moodle platform in the process of teaching future English teachers allows both teachers and students to easily exchange files, carry out personal communication, and receive feedback. The presented electronic course allows teachers to create their own e-learning courses in foreign languages.
MOODLE BEFORE, DURING AND AFTER THE COVID-19 PANDEMIC: USING BY BACHELOR AND MASTER STUDENTS

The article focuses the problem of using e-learning, in particular Moodle system, during the Covid-19 pandemic. Moodle is widespread in high school and is an effective means of integrating traditional and distance learning. The aim of the study is to identify and compare the views of on the use of Moodle in a classical university before, during and after the Covid-19 pandemic. The author developed a questionnaire and included 1071 university students of different years of study in the online survey, including 800 Bachelor and 271 Master students; used IBM SPSS Statistics 23 for statistical analyses. Students were mostly easily registered (together the answers «very easy» and «rather easy») – 63-78% of students of different courses for studying in Moodle. Some difficulties are recorded by about 9-16% of students (together the answers are «very difficult» and «rather difficult») of different courses during registration. There was no statistically significant difference, according to the 2-criterion (p ≤0,05), in the indicators of student registration on various grounds: educational and professional level (Bachelor/Master), form of study (full-time/part-time training), gender (men/women), profession (pedagogical/other). The current research work found that students who were registered in Moodle system «very difficult» and «difficult», respectively, 81,4% and 58,9%, did not have any academic disciplines in Moodle system or had had very few ones before the Covid-19 pandemic. The lack of relevant e-learning experience probably led to further difficulties when registering with Moodle. The article describes the minor use of Moodle to educate students before the Covid-19 pandemic, as only 25% of Bachelor students and about 30% of Master students had studied most academic disciplines on this platform. These figures increased to almost 84% and 88% of Moodle use during the Covid-19 pandemic in distance learning, respectively. Bachelor (72%) and Master (78%) students expressed the wish to study most academic disciplines in Moodle after the end of the Covid-19 pandemic. A statistically significant difference (p≤0,05) was found between comparable groups of students (bachelor/master) before the Covid-19 pandemic regarding the use of Moodle, such a difference was not found during and after the Covid-19 pandemic
Lossy Image Compression with Normalizing Flows

Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been established and refined over many decades. However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization. Due to that, they inherently limit the range of quality levels that can be covered. In contrast, traditional approaches in image compression allow for a larger range of quality levels. Interestingly, they employ an invertible transformation before performing the quantization step which explicitly discards information. Inspired by this, we propose a deep image compression method that is able to go from low bit-rates to near lossless quality by leveraging normalizing flows to learn a bijective mapping from the image space to a latent representation. In addition to this, we demonstrate further advantages unique to our solution, such as the ability to maintain constant quality results through re-encoding, even when performed multiple times. To the best of our knowledge, this is the first work to explore the opportunities for leveraging normalizing flows for lossy image compression.
Efficient High-Resolution Image-to-Image Translation Using Multi-Scale Gradient U-Net

Recently, Conditional Generative Adversarial Network (Conditional GAN) has shown very promising performance in several image-to-image translation applications. However, the uses of these conditional GANs are quite limited to low-resolution images, such as $$256 \times 256$$ . The Pix2Pix-HD is a recent attempt to utilize the conditional GAN for high-resolution image synthesis. In this paper, we propose a Multi-Scale Gradient based U-Net (MSG U-Net) model for high-resolution image-to-image translation up to $$2048 \times 1024$$ resolution. The proposed model is trained by allowing the flow of gradients from multiple-discriminators to a single generator at multiple scales. The proposed MSG U-Net architecture leads to photo-realistic high-resolution image-to-image translation. Moreover, the proposed model is computationally efficient as compared to the Pix2Pix-HD with an improvement in the inference time nearly by 2.5 times. We provide the code of MSG U-Net model at https://github.com/laxmaniron/MSG-U-Net .
Light-YOLOv3: License Plate Detection in Multi-Vehicle Scenario

With the development of the Internet of Vehicles, License plate detection technology is widely used, e.g., smart city and edge senor monitor. However, traditional license plate detection methods are based on the license plate edge detection, only suitable for limited situation, such as, wealthy light and favorable camera's angle. Fortunately, deep learning networks represented by YOLOv3 can solve the problem, relying on strict condition. Although YOLOv3 make it better to detect large targets, its low performance in detecting small targets and lack of the real-time interactively. Motivated by this, we present a faster and lightweight YOLOv3 model for multi-vehicle or under-illuminated images scenario. Generally, our model can serves as a guideline for optimizing neural network in multi-vehicle scenario.
Design and Evaluation of Korean Electropalatography (K-EPG)

Recently, the development of medical rehabilitation technology has resulted in an increased interest in speech therapy equipment. In particular, research on articulation therapy for communication disorders is being actively conducted. The existing methods for the diagnosis and treatment of speech disorders, such as traditional tactile perception tests and methods based on the empirical judgment of speech therapists, have many limitations. Moreover, the position and contact force of the tongue are key factors in speech disorders with regards to articulation. This is a very important factor in the distinction of Korean characters such as lax, tense and aspirated consonants. In this study, we proposed a Korean-electropalatography (EPG) system to easily measure and monitor the position and contact force of the tongue during articulation treatment and diagnosis. In our proposed K-EPG system, a sensor was fabricated using an AgCl electrode and biocompatible silicon. Furthermore, the measured signal was analyzed by implementing a bio-signal processing module and monitoring program. In particular, the bio-signal was measured by inserting the device into the palate of an experimental healthy test group (four subjects). Through these experiments, we confirmed that our K-EPG system could be applied to clinical treatment in speech therapy.
Using Transformer Based Ensemble Learning to Classify Scientific Articles

Many time reviewers fail to appreciate novel ideas of a researcher and provide generic feedback. Thus, proper assignment of reviewers based on their area of expertise is necessary. Moreover, reading each and every paper from end-to-end for assigning it to a reviewer is a tedious task. In this paper, we describe a system which our team FideLIPI submitted in the shared task of SDPRA-2021 (https://sdpra-2021.github.io/website/ (accessed January 25, 2021)) []. It comprises four independent sub-systems capable of classifying abstracts of scientific literature to one of the given seven classes. The first one is a RoBERTa [] based model built over these abstracts. Adding topic models/Latent dirichlet allocation (LDA) [] based features to the first model results in the second sub-system. The third one is a sentence level RoBERTa [] model. The fourth one is a Logistic Regression model built using Term Frequency Inverse Document Frequency (TF-IDF) features. We ensemble predictions of these four sub-systems using majority voting to develop the final system which gives a F1 score of 0.93 on the test and validation set. This outperforms the existing State Of The Art (SOTA) model SciBERT's [] in terms of F1 score on the validation set. Our codebase is available at https://github.com/SDPRA-2021/shared-task/tree/main/FideLIPI.
An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators

Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While they can be addressed by fine-tuning, this is not practical for ML service providers (e.g., Google or Microsoft) who often receive customer models without training data. Specialized hardware for handling activation outliers can enable low-precision neural networks, but at the cost of nontrivial area overhead. We instead propose overwrite quantization (OverQ), a lightweight hardware technique that opportunistically increases bitwidth for activation outliers by overwriting nearby zeros. It has two major modes of operation: range overwrite and precision overwrite. Range overwrite reallocates bits to increase the range of outliers, while precision overwrite reuses zeros to increase the precision of non-outlier values. Combining range overwrite with a simple cascading logic, we handle the vast majority of outliers to significantly improve model accuracy at low bitwidth. Our experiments show that with modest cascading, we can consistently handle over 90% of outliers and achieve +5% ImageNet Top-1 accuracy on a quantized ResNet-50 at 4 bits. Our ASIC prototype shows OverQ can be implemented efficiently on top of existing weight-stationary systolic arrays with small area increases per processing element. We imagine this technique can complement modern DNN accelerator designs to provide small increases in accuracy with insignificant area overhead.
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation. We argue that for high-dimensional multi-class data, the optimal linear discriminative representation maximizes the coding rate difference between the whole dataset and the average of all the subsets. We show that the basic iterative gradient ascent scheme for optimizing the rate reduction objective naturally leads to a multi-layer deep network, named ReduNet, which shares common characteristics of modern deep networks. The deep layered architectures, linear and nonlinear operators, and even parameters of the network are all explicitly constructed layer-by-layer via forward propagation, although they are amenable to fine-tuning via back propagation. All components of so-obtained "white-box" network have precise optimization, statistical, and geometric interpretation. Moreover, all linear operators of the so-derived network naturally become multi-channel convolutions when we enforce classification to be rigorously shift-invariant. The derivation in the invariant setting suggests a trade-off between sparsity and invariance, and also indicates that such a deep convolution network is significantly more efficient to construct and learn in the spectral domain. Our preliminary simulations and experiments clearly verify the effectiveness of both the rate reduction objective and the associated ReduNet. All code and data are available at \url{https://github.com/Ma-Lab-Berkeley}.
Nature-Inspired Gravitational Search-Guided Artificial Neural Key Exchange for IoT Security Enhancement

The advantages of IoT are incontrovertible; however, significant risks and uncertainties over the protection of best practices and their related costs prevent many companies from adopting the technology. Also, users are aware of the consequences of breaches of IoT protection. Recent research indicates that 90% of consumers lack confidence in the safety of IoT devices. IoT defense has been described as one of the weakest areas. In this respect, significant efforts have been made, often through traditional cryptographic methods, to overcome security and privacy issues in IoT systems. The list of security issues (confidentiality, integrity, authentication, and availability) must be addressed to secure IoT devices from attack. IoT nodes' specific features do not adequately protect the whole security spectrum of IoT networks through existing solutions. This paper aims to provide security to the IoT devices using nature-inspired Gravitational Search-guided artificial neural key. In this paper, artificial neural synchronization is used to create a neural key exchange protocol between two IoT devices over a public channel for cryptographic purposes. This proposed technique has many benefits, such as 1) it provides an optimized design of the neural network that facilitates the development and establishment of a neural key between the two approved IoT devices. 2) It offers the sharing of the private neural key over the public channel via artificial neural synchronization 3)Three hidden layers of the neural network contribute to the deep internal design. So, it's going to be hard for the intruder to infer the internal layout. 4) The increase in the weight spectrum of the neural network raises the complexity of an effective attack exponentially, but the effort to create a neural key decreases over polynomial time. Various parametric experiments have been conducted out on the proposed methodology. The simulations of the procedure demonstrate efficacy in terms of the findings cited in the paper.
The reality of e-learning in the distance learning program in light of the emerging Corona pandemic "Covid-19" from the viewpoint of students in Jordan between theory and practice: واقع التعلم الإلكتروني في برنامج التعلم عن بعد في ظل جائحة كورونا المستجد "كوفيد 19" من وجهة نظر الطلبة في الأردن بين النظرية والتطبيق

The aim of this research is to identify the reality of e-learning in the distance learning program in light of the emerging Corona pandemic "Covid 19" from the viewpoint of students in Jordan between theory and practice. To achieve the goal of the study, the researcher prepared an electronic questionnaire that included (20) items distributed to the research sample electronically. It was confirmed its sincerity and consistency, and it was applied to a random sample from the study community consisting of (1200) students, and the descriptive analytical method was used. An analytical descriptive approach was used, The study concluded the following results: The importance of using e-learning in distance learning programs, the extent of using e-learning in distance learning programs. There are difficulties preventing the use of e-learning in distance learning programs. There were no statistically significant differences at (05.0 ≤ α) between the responses of the sample members of students about the reality of e-learning in distance learning programs due to gender variables. In light of these results, the study recommended encouraging and educating students to take advantage of the e-learning technology in order to facilitate and improve educational learning practice under the current circumstances. And strengthening the positive trend towards employing e-learning technology and benefiting from Arab and international experiences and expertise in the field of employing e-learning technology in distance learning programs.
The relationship between the features of figurative and logical thinking and learning difficulties

The article examines the relationship between the level of formation of verbal and logical operations and learning difficulties in primary school children. Thinking is considered as a key component of the cognitive development of the student’s personality. The study sample consisted of 71 primary school children aged 7 to 12 years, including 55 boys and 16 girls. The research methods were the survey method, diagnostic methods, and statistical methods. According to the results of the study, the levels of imaginative and logical thinking and learning difficulties in younger schoolchildren are identified, and their interrelationships are highlighted. The conclusions can be used in pedagogical practice for effective psychological and pedagogical support and training of children with special educational needs in general education schools.