Reviewerly/exHarmony huggingface dataset:
Efficient S-repetition method for constructing an IND-CCA2 secure McEliece modification in the standard model

encryption of S/2 various messages with one common secret permutation, in contrast to other modifications that use S-repetition encryption of one message.Thus, this modification provides IND-CCA2-security with an efficient information transfer rate.
HELPING STRUGGLING READERS TO READ: THE IMPACT OF THE CARE FOR THE NON-READERS (CRN) PROGRAM ON FILIPINO PUPILS’ READING PROFICIENCY

This study was conducted to determine the impact of the Care for the Non-Readers Program on the reading performance of struggling readers in all elementary schools of Dumingag II District, Dumingag, Zamboanga del Sur, Mindanao, Philippines during the School Year 2016-2017. Fifty teachers and 465 parents were selected as respondents of the study. It made use of the descriptive comparative method of research with the questionnaire-checklist as data-gathering instrument. Both the descriptive and inferential statistics were used in the analysis and interpretation of data. The findings of the study revealed that both teachers and parents considered the Care for the Non-Readers Program as “Very Effective” in improving the reading performance of pupils. “Fuller Technique,” “Independent Reading,” “Directed Reading Thinking Activity,” and “Say Something” were the leading strategies applied by teachers in the implementation of the program. The majority of the pupils still belonged to the "Frustration" level. However, there was a significant improvement in pupils’ reading performance for three school years. Teachers encountered "Serious" problems in the implementation of the program, while the suggested solutions were considered as "Very Effective." Furthermore, there was no significant difference between the assessments of the two groups of respondents on the effectiveness of the CNR program.
JokeMeter at SemEval-2020 Task 7: Convolutional Humor

This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7. The system is based on convolutional neural network architecture.We investigate the system on the official dataset, and we provide more insight to model itself to see how the learned inner features look.Kushner to visit Mexico following latest Trump tirades therapist 2.
Performance Assessment of Ultrasound Kidney Images using De-speckling algorithms

The key concern that occurs in non invasive Kidney stone diagnosis using Ultrasound (US) imaging is speckle noise, as it reduces the diagnostic quality of images, required for further medication.In this work, different preprocessing filters like Median, Adaptive median, Weiner and Wavelet domain filtering are applied to both normal and kidney stone US images, with results showcase, Neigh Sure Shrink is preeminent for kidney stone US images .Objective feature assessment of the different preprocessing filters are evaluated for noise variance from 0.01 and 0.08 along with level of decomposition in Neigh Sure Shrink against the parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Root Mean Square error (RMSE), Mean Squared Error (MAE) with the investigational outcome shows the preeminence of projected filters over the existing methods.Further, the segmentation method allows bifurcation of the US image to detect the kidney stone on basis of Region of Interest, followed by determining the presence of Centroid and area for kidney stone US images.The Graphical User Interface allows easiness in locating the area of kidney stone for kidney US images.
Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning

Secure multi-party computation has seen significant performance advances and increasing use in recent years. Techniques based on secret sharing offer attractive performance and are a popular choice for privacy-preserving machine learning applications. Traditional techniques operate over a field, while designing equivalent techniques for a ring Z_2^k can boost performance. In this work, we develop a suite of multi-party protocols for a ring in the honest majority setting starting from elementary operations to more complex with the goal of supporting general-purpose computation. We demonstrate that our techniques are substantially faster than their field-based equivalents when instantiated with a different number of parties and perform on par with or better than state-of-the-art techniques with designs customized for a fixed number of parties. We evaluate our techniques on machine learning applications and show that they offer attractive performance.
Syntactic Graph Convolutional Network for Spoken Language Understanding

Slot filling and intent detection are two major tasks for spoken language understanding.In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge.In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly.Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work.At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.
NUIG at SemEval-2020 Task 12: Pseudo Labelling for Offensive Content Classification

This work addresses the classification problem defined by sub-task A (English only) of the OffensEval 2020 challenge.We used a semi-supervised approach to classify given tweets into an offensive (OFF) or not-offensive (NOT) class.As the OffensEval 2020 dataset is loosely labelled with confidence scores given by unsupervised models, we used last year's offensive language identification dataset (OLID) to label the OffensEval 2020 dataset.Our approach uses a pseudo-labelling method to annotate the current dataset.We trained four text classifiers on the OLID dataset and the classifier with the highest macro-averaged F1-score has been used to pseudo label the OffensEval 2020 dataset.The same model which performed best amongst four text classifiers on OLID dataset has been trained on the combined dataset of OLID and pseudo labelled OffensEval 2020.We evaluated the classifiers with precision, recall and macro-averaged F1-score as the primary evaluation metric on the OLID and OffensEval 2020 datasets.
Perancangan Aplikasi Chatbot Sebagai Media E-Learning Bagi Siswa

The conventional question and answer method has limited time, space and level of understanding from each student so that it becomes an obstacle to the ineffective teaching and learning process. Global pandemic events that have a negative impact on the education sector. So that the teaching and learning process is temporarily stopped at school and diverted online. This study aims to produce a chatbot application as a media for distance learning that helps the teaching and learning process of teachers and students . In practice, This research uses telegram as a chatbot media. The method used for data collection is by interview, literature study, and observation. Testing will be carried out with a black box, calculation of accuracy, and a user acceptance test in the form of distributing questionnaires. The results of this study produce a Chatbot Application as an E-Learning Media for Students that can help teachers in providing distance learning to students efficiently and easily.&#x0D;
Topic-Document Inference With the Gumbel-Softmax Distribution

Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given collection, topic modeling infers which combination of topics is addressed by each individual document (the so-called topic-document inference), which can be useful for their classification and organization. However, the distributional assumptions for this inference are typically restricted to the Dirichlet family which can limit the performance of the model. For this reason, in this paper we propose modeling the topic-document inference with the Gumbel-Softmax distribution, a distribution recently introduced to expand differentiability in deep networks. To set up a performing system, the proposed approach integrates Gumbel-Softmax topic-document inference in a state-of-the-art topic model based on a deep variational autoencoder. Experimental results over two probing datasets show that the proposed approach has been able to outperform the original deep variational autoencoder and other popular topic models in terms of test-set perplexity and two topic coherence measures.
Impact of COVID-19 and Related Forced Digitalization Processes on the Competitiveness of Higher Education Institutions and Organizations

Research background: One of the areas that has undergone major changes in the processes of its activities and has had a strong impact on the change in social institutions is the field of education. The sharp transition to distance education and a number of technical, informational and human problems led to a significant complication of educational and other processes (scientific, innovative, entrepreneurial, etc.). Purpose of the article: The article raises the problem of the impact of the coronavirus pandemic on global social and public institutions. The purpose of the article is to identify the factors of ensuring the competitiveness of universities, as the least protected by state support, in the context of forced digitalization against the background of the covid-19 pandemic. Methods: The paper uses mechanisms for assessing the competitiveness of universities in the new reality of the pandemic and its consequences, implemented on the basis of a modified McKinsey matrix and matrix algorithms for evaluating priority vectors. Findings &amp; Value added: The analysis showed that the universities that have the greatest independence in the system face the greatest difficulties in carrying out their activities in the context of a pandemic. As scientific growth can be considered, the results of the analysis of the transition to distance learning processes that have combined higher education systems in different countries, and if earlier most universities competed at the regional or country level, now they are forced to enter into global competition with foreign universities.
The attitude of teachers to the opportunities and risks of spiritual and moral education in the context of digitalization

The relevance of the research is due to the fact that the digitalization of the world educational system as a modern trend in the development of education requires serious reflection and solutions to a number of problems. The use of digital educational content, in addition to its obvious advantages, also contains a number of very likely risks. Including risks related to the spiritual and moral sphere of the individual. The purpose of our research is to study the attitude of teachers to the opportunities and risks of spiritual and moral education in the context of digitalization, as well as to find ways to minimize possible risks. The study involved teachers of the Chelyabinsk region who had advanced training in teaching the discipline "Fundamentals of religious cultures and secular ethics" (on the basis of the Institute of continuing and additional education of the South Ural state humanitarian and pedagogical University). The survey method was used in the study. As a result of the survey, it was revealed that the main effect of using digital technologies, according to teachers, is better memorization of visually perceived information (72% of students), a third of respondents note the possibility of taking into account the individual characteristics of students, and a quarter of respondents – an increase in creative potential. Achievement of subject-based learning outcomes is, in the opinion of teachers, the main effect of using digital content, the educational effect of digitalization is not noted by respondents. More than 80% of teachers see the risk of using digital technologies in reducing the time for live communication, more than 60% – in reducing the communicative activity of children, in reducing interest in printed sources of information. The conclusions are related to the need for further research of this problem in the following areas: analysis of educational opportunities of existing digital educational content of spiritual and moral orientation and determination of criteria for evaluating such content; selection and creation of effective methods of using digital technologies that ensure the formation of students ' experience of emotional and value attitude to spiritual, historical and cultural heritage, as well as the ability to apply the knowledge gained in various life situations; training of teachers who are competent in the field of spiritual and moral education.
The Impact of Mobile Learning on EFL Students’ Learning Behaviors and Perceptions: From Content Delivery to Blended Interaction

Mobile learning (M - learning) emerges as the essential mark of technological revolution 4.0 and the great development of modern technology. It has proved that their smart functions were accompanied by the rapid expansion of mobile devices to support learning of learners everywhere and every time. Although mobile technology is expected to apply suitably to teaching and learning, the capacity of English as a Foreign Language (EFL) students to use mobile devices to support their English learning needs to be thoroughly evaluated. This paper is to present how EFL learners at a Vietnam University can exploit mobile technology by questionnaire through a survey of 300 EFL students. The investigation took place during the second semester of the 2018 - 2019 school year. The quantitative approach has been used to gather and analyze data, and results have shown that EFL students are confident of learning English through mobile devices through their use of mobile technology. This shows that learning English by mobile devices is feasible and has got positive perceptions from the students. Furthermore, the research results are expected to contribute as a theoretical background as reference for future researches related to mobile learning applications.
PREDICTING THE LOCATION OF THE UTERINE CERVICAL OS FROM 2D IMAGES WITH CNN

In an automated cervical cancer test, the prediction of the location of the cervical os from 2D images is required. Cervical os is the reference point to determine the lesion's location by either using cervical four-quadrant location or by 12 o’clock locations. Precise detection of the cervical os point ensures correct addressing of the lesions. This study used a 6-layer convolutional neural network to predict the center of the cervical os’ coordinates (x,y) on 2D grayscale images. We used a holistic approach without masking any visual element to predict the location of the cervical os. The 2D images were obtained using a telecentric lens and a CCD camera with light wavelengths of 500 550 nanometers. Because of the limited number of image samples (145 images), we used augmentation techniques to increase the training set size by rotating each original image in 1-degree increments from -30 degrees to +30 degrees relative to the center of the image. The 6-layer convolutional neural network was tested on 21 unseen cervix images using augmentation data. The outcomes showed that the image center-based augmentation technique improves the prediction performance. We obtained 2.4 RMSE in predicting the location of the cervical os.
A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments

Author profiling aims to correlate writing style with author demographics.This paper presents an approach used to build a Decision Support System (DSS) for detecting age and gender from Twitter feeds.The system is implemented based on Deep Learning (DL) algorithms and Machine Learning (ML) algorithms to distinguish between classes of age and gender.The results show that every algorithm has different results of age and gender based on the model architecture and power points of each algorithm.Our decision support system is more accurate in predicting the age and the gender of author profiling from his\her written tweets.It adopts the deep learning model using CNN and LSTM methods.Our results outperform those obtained in the competitive conference s CLEF 2019.
A Systematic Literature Review on English and Bangla Topic Modeling

Due to the enormous growth of information and technology, the digitized texts and data are being immensely generated.Therefore, identifying the main topics in a vast collection of documents by humans is merely impossible.Topic modeling is such a statistical framework that infers the latent and underlying topics from text documents, corpus, or electronic archives through a probabilistic approach.It is a promising field in Natural Language Processing (NLP).Though many researchers have researched this field, only a few significant research has been done for Bangla.In this literature review paper, we have followed a systematic approach for reviewing topic modeling studies published from 2003 to 2020.We have analyzed topic modeling methods from different aspects and identified the research gap between topic modeling in English and Bangla language.After analyzing these papers, we have identified several types of topic modeling techniques, such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Support Vector Machine (SVM), Bi-term Topic Modeling (BTM).Furthermore, this review paper also highlights the real-world applications of topic modeling.Several evaluation methods were used to evaluate these models' performances, which we have discussed in this study.We conclude by mentioning the huge future research scopes for topic modeling in Bangla.
Supervised Machine Learning Classifiers for Diversity Combined Signals in 6G Massive MIMO Receivers

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM).SVM, as a supervised machine learning tool, is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and 6G wireless communication networks.In this paper, SVM is applied to signal detection in 6G communication systems in the presence of channel noise in the form of fully developed Rayleigh multipath fading and receiver noise generalized as additive color Gaussian noise (ACGN).The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented.The performance of SVM is then compared to conventional M-ary signaling optimal model-based detector driven by M-ary phase shift keying (MPSK) modulation.We show that the SVM performance is superior to that of conventional detectors which require as much as 7 bits-coding (M ≥ 128) to produce comparable results to those of SVM.Finally, the SVM-based detector is implemented in an uplink SIMO system using both Equal Gain Combiner (EGC) technique and Root Mean Square Gain Combiner (RMSGC) technique in which the later technique will be proven to be superior to the earlier.
A survey of benchmarks for reinforcement learning algorithms

Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. \par To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. \par The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.
Concept of education: Confluence of definition criteria, temporary formative orientation and common activity as core content of its meaning

In this work, the purpose is to establish the need to go beyond the nominal definition of the concept of education and justify the existence of distinctive traits of the real definition of the term ‘education’ in character and sense inherent in its meaning, which must be taken into account at all times and places, whenever we carry out pedagogical intervention.&#x0D; It is about forming criteria on meaning of ‘education’ and importance of Pedagogy in the construction of education fields. Knowledge of education makes it possible to build fields of education over cultural areas, transforming information into knowledge and knowledge into education. And this requires executing pedagogical function with competence, establishing an educational relationship in which common activity is the working tool.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024×1024 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU; and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder. With thirteen datasets covering a wide variety of image domains, we show our model's robustness and its superior performance compared to the state-of-the-art StyleGAN2.
Student Optimism Regarding Online Learning During the Covid-19 Pandemic

&#x0D; &#x0D; &#x0D; Optimism is a positive attitude towards a situation, looking at something from the good side, and the hope of getting the best results from the situation. This study aims to find an overview of students’ optimism regarding online learning during the Covid-19 pandemic. The research subjects were high school students who were taught via online systems. The study is based on data analysis using descriptive statistics. The results showed that the opimism of female students was higher than that of male students. In terms of optimism, the most dominant trait was the personalization aspect and the weakest was the permanence aspect.&#x0D; Keywords: Optimism, online learning, Covid-19 pandemic&#x0D; &#x0D; &#x0D;