Reviewerly/exHarmony huggingface dataset:
Prospects for Teaching English at Higher Education Institutions in a Digitalized Educational Process

This article discusses the latest trends in the digitalization and computerization of education today, which contribute to the overall process of introducing modern technologies into education, as a process of a stable global trend, which entails an improvement in the content, forms and approaches to learning and in general throughout the education system. Particular attention is paid to the use of electronic testing schemes in the classroom while teaching foreign languages. The article also analyzes the process of teaching English in higher education institutions in the context of digitalization of modern society. Forms of this activity, such as online learning and classroom teaching, are considered. The comparative analysis of both forms of teaching is carried out, their advantages and disadvantages are revealed in the light of the rapid development of innovative technologies, computer systems and artificial intelligence. The reasons for the crisis in the existing classroom form of teaching English and the growing popularity of language learning in the online space are analyzed. The prospects for developing classroom and online language teaching are outlined. Recommendations are given on how to effectively combine both forms of teaching in the process of teaching professional English to university students in an ever-changing professional and business environment. Digitization of education in the area of teaching a foreign language implies providing students with opportunities to improve their knowledge of a foreign language through the use of a huge number of paid and free resources. The introduction of online courses and the support of courses with an online component are gaining high popularity around the world. The online component provides an opportunity to practice the skills formed during the implementation of the main part of the discipline, thereby taking out the old teaching method. A wide range of proposed innovations will help to solve the tasks set by the society in terms of training highly qualified personnel in the future.
Development of a transferring GAN based CAD scheme for breast mass classification: an initial study

The purpose of this study is to develop a novel computer-aided diagnosis (CAD) scheme to facilitate breast mass classification, which is based on the latest transferring generative adversarial networks (GAN) technology. Although GAN is one of the most popular techniques for image augmentation, it requires a relatively large original dataset to achieve satisfactory results, which may not be available for most of the medical imaging tasks. To address this challenge, we developed a novel transferring GAN, which was built based on the deep convolutional generative adversarial networks (DCGAN). This novel model was first pre-trained on a dataset of non-mass mammogram patches. Then the generator and the discriminator were fine-tuned on the mass dataset. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. We retrospectively assembled a total of 25,000 non-mass patches and 1024 mass images to assess this model, using classification accuracy and receiver operating characteristic (ROC) curve. The results demonstrated that our proposed approach improved the accuracy and area under the ROC curve (AUC) by 6.0% and 3.5% respectively, when compared with the classifiers trained without conventional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on medical imaging tasks with limited datasets.
Sigmoid models for Covid-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation M. Konstantinov, K. Konstantinov, S. Konstantinov; Sigmoid models for Covid-19 pandemic. AIP Conf. Proc. 8 March 2021; 2333 (1): 090002. https://doi.org/10.1063/5.0041728 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Arabic Multi-label Text Classification of News Articles

Text classification is the process of automatically tagging a textual document with the most relevant set of labels. This work aims to automatically map an input document based on its vocabulary features to multiple tags. To achieve this goal, a large dataset has been constructed from various Arabic news portals. The dataset has over 290k multi-tagged articles. The datasets shall be made freely available to the research community on Arabic computational linguistics. To examine this dataset, we tested both shallow learning and deep learn- ing multi-labeling approaches. A custom accuracy metric, designed for the multi-labeling task, has been developed for performance evaluation and the hamming loss metric. Firstly, we used classifiers compatible with multi-labeling tasks, such as Logistic Regression and XGBoost, by wrapping each in an OneVsRest classifier. XGBoost gave higher accuracy, scoring 91.3
HDR Image Compression by Multi-Scale down Sampling of Intensity Levels

HDR images are inherently very large in size compared to normal images. Hence, storage and communication overheads of HDR images are expensive to be used in mobile devices. Hence, invariably image compression is adopted for HDR images. In this paper, HDR image compression is achieved by down sampling the intensity levels while maintaining the dynamic range same as that of the original. This aspect retains the edge information of the images almost intact. Spatial down-sampling process is used to reduce the number of intensity samples. Consequently, this operation lowers the bit depth required to store the corresponding index file which in turn results in image compression.
Modeling and Extracting Information about Cybersecurity Events from Text

People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with many problems, including cybercrimes, stolen data, and identity theft. With the large and increasing number of transactions done every day, the frequency of cybercrime events is also growing. Since the number of security-related events is too high for manual review and monitoring, we need to train machines to be able to detect and gather data about potential cyber threats. To support machines that can identify and understand threats, we need standard models to store the cybersecurity information and information extraction systems that can collect information to populate the models with data from text. This dissertation makes two significant contributions. First, we defined rich cybersecurity event schema and annotated a news corpus following the schema. Our schema consists of event type definitions, semantic roles, and event arguments. Second, we present CASIE, a cybersecurity event extraction system. CASIE can detect cybersecurity events, identify event participants and their roles, including specifying realis values. It also groups the events, which are coreference. CASIE produces output in an easy to use format, as a JSON object. : We believe that this work will be useful for cybersecurity management in the future. It will quickly grasp cybersecurity event information out of the unstructured text and fill in the event frame. So we can keep up with many cybersecurity events that happen every day.
A Hybrid Decision Tree-Neural Network (DT-NN) Model for Large-Scale Classification Problems

As the Age of Information has evolved over the last several decades, the demand for technology which stores, analyzes, and utilizes data has increased substantially. Countless industries such as the medical, the retail, and the aircraft rely on this technology to guide their decision making. In the present paper, we propose a hybrid machine learning algorithm consisting of Decision Trees and Neural Networks which can effectively and efficiently classify data of varying volume and variety. The structure of the hybrid algorithm consists of a decision tree where each node of the tree is a neural network trained to classify a specific category of the output using binary classification. The data with which we used to train and test the classification ability of our algorithm is the Federal Aviation Administration's (FAA's) Boeing 737 maintenance dataset which consists of 137,236 unique records each composed of 72 variables. We perform this by classifying the discrepancy, or cause, of the incident into whether or not the incident occurred during scheduled maintenance operations and then further classifying specific details relating to the incident. Our results indicate that our hybrid algorithm is able to effectively classify incidents with high accuracy and precision. Additionally the algorithm is able to identify the most significant inputs regarding a classification allowing for higher performance and greater optimization. This demonstrates the algorithm's applicability in real-world scenarios while also showcasing the benefits of combining decision trees and neural networks as opposed to using them individually.
Adaptive Drift Detection Mechanism for Non-Stationary Data Stream

Mining is a challenging and important task in a non-stationary data stream. It is used in financial sectors, web log analysis, sensor networks, network traffic management, etc. In this environment, data distribution may change overtime and is called concept drift. So, it is necessary to identify the changes and address them to keep the model relevant to the incoming data. Many researchers have used Drift Detection Method (DDM). However, DDM is very sensitive to detect gradual drift where the detection delay is high. In this paper, we propose Adaptive Drift Detection Method (ADDM) which improves the performance of the drift detection mechanism. The ADDM uses a new parameter to detect the gradual drift in order to reduce the detection delay. The proposed method, ADDM, experiments with six synthetic datasets and four real-world datasets. Experimental results confirm that ADDM reduces the drift detection delay and false-positive rate (FPR) while preserving high classification accuracy.
Automatic Generation of Teachers’ Course Preferences Using Document Clustering

The current study examined the automated course preferences of teachers using document clustering. Data regarding teachers’ course preferences and course outlines were collected and preprocessed for further analysis. Two separate clustering solutions were generated for teachers and courses datasets. The clustering solution for teachers contained clusters of similar faculty members grouped together on the basis of their course preferences and courses taught by them in previous years. The clustering solution generated for courses contained the list of course outlines of assigned courses. Good quality clusters for both teachers and courses were generated using K-means clustering method in CLUTO software package. The generated clustering solutions were mapped for automated generation of course preferences for each teacher in the dataset. Precision, Recall and F-measure values were also reported and they indicated promising results.
Prediction of Pile Bearing Capacity Using Artificial Neural Networks.(Dept.C)

It is well known that the human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation neural networks were utilized to predict the working bearing capacity of piles. The data of performed pile load   tests are used to verify the applicability of the presented neural network procedure. The results showed that the maximum error of prediction did not exceed 25%. Thus, the use of Neural Networks to predict pile capacity seems to be feasible for practical purpose.
Improving Schrödinger Equation Implementations with Gray Code for Adiabatic Quantum Computers

The continuous-space Schr\"odinger equation is mapped to the transverse Ising model while achieving an exponential advantage over classical computers: A step closer to efficient simulation of multi-particle systems.
The Application Of Language Learning Strategies In Japanese Language Learning

The Educators must place their students as people who naturally have experience, knowledge, desires, and thoughts that can be used to learn, both individually and in groups. All language learning strategies have the main goal of increasing one's ability to speak. The choice of teaching and learning strategies language learning strategies must be based on the consideration of placing students as subjects of learning who not only passively accept what is conveyed by educators. Teaching Japanese especially kanji using various learning strategies makes it easy for Japanese learners especially in learning Japanese letters/starch. Language learning strategies/learning strategies used are direct strategies and indirect strategies. Direct Strategy is a strategy that is directly related to foreign language learning. That is directly related to language in various tasks and situations, consisting of (1) memory strategies, (2) cognitive strategies, and (3) compensation strategies. Indirect Strategies in language learning are strategies that indirectly support language learning through ways such as the focus of learning, planning, assessment, acquiring learning opportunities, avoiding anxiety in learning, working with others. This strategy consists of three elements, namely (1) metacognitive strategies that are tasked with coordinating the learning process, (2) affective strategies that are in charge of regulating emotions, and (3) social strategies that are tasked with fostering cooperation with others in the learning process. This study aims to obtain scientific data the process of increasing mastery of Japanese letters/kanji by applying Language learning strategies direct strategies and indirect strategies. The method used in this study is an action research method. Data from the research results were analyzed qualitatively and quantitatively. Referring to the model developed by Kemmis and Mc. Taggart is a spiral model. The research method used is the method of Action research with four steps, namely (planning, implementation, observation, and reflection). Use of the application of Language Learning Strategies in Kanji learning among others: (1) the development of writing and Kanji reading skills through meaningful learning experiences, (2) Kanji learning is more systematic and creative both individually and in collaboration with friendsKeywords: Language, learning, strategies, Kanji
Priorities of critical thinking: new paradigms of modern higher schools

Presented is philosophical analysis in issues related to the practice of introducing critical thinking into educational programs of the university. The content of the concepts “educated person”, “subject of education” is comprehended in relation to ideas about knowledge and information. Education is seen as a special tool in the creation of personality, the emphasis is on identifying critical thinking skills in the cognitive process. The mission of education is presented as a total transformation of a person, an ascent to his true self. The primary task facing a person is the education of oneself and, given the speed of transformations in modern society, the development of critical thinking is postulated as a priority goal of education. The pluralism of approaches to the implementation of critical thinking programs by domestic higher education is demonstrated. The focus is on the university course “Logic and Critical Thinking”, which is presented as a laboratory of rationality for the formation of rationality skills. By detailing the course, the idea of logic as a necessary foundation for the formation of a critical culture of thought is defended. It is concluded that the results of the study of the practice of implementing this course show an increase in the quality of thinking in general and critical thinking in particular.
EVALUATION OF THE K-NEAREST NEIGHBOR MODEL WITH K-FOLD CROSS VALIDATION ON IMAGE CLASSIFICATION

In this paper, the data used is the banana image which is extracted into the dataset into 4 attributes, namely red, green, blue, and the mean for the classification process. Image data is classified using the k-Nearest Neighbor method which will be optimized the model with the k-Fold Cross Validation algorithm. Evaluation of the k-NN model with the k-FCV algorithm can improve accuracy and can build better machine learning models in the image classification process. The default K-NN obtained an accuracy rate of 57%, while the results of the model evaluation with the k-FCV algorithm, on fold 3 obtained an accuracy rate of 68%. The percentage yield with the new model increased by 11% which indicates that the machine learning model that was built was quite optimal
Human Age Estimation Using Support Vector Machine

A person’s face provides a lot of information such as age, gender, and identity. It plays an important role in estimating or predicting the age and gender of a particular person just by looking at their face. The ability to recognize age and gender is very important because it has got many real-life applications. The requirement of estimation of the age of a person is growing rapidly with the advancement of technologies which is paving the way simultaneously for chances of fraud. As a result, modern intelligent systems are expected to have the ability to accurately recognize and interpret human faces in real time. In this paper, we are trying to estimate the age and gender of a person using the combination of two algorithms primarily. A fast and efficient gender and age estimation system based on machine learning is developed. For the development of this system, various artificial intelligence techniques have been employed. The proposed work consists of three phases, namely: 1. Feature Extraction, 2. Evaluation of the features, 3. Age and gender estimation. In this research, we used the data from the FG-NET dataset ( https://www.researchgate.net/figure/HaarNet-architecture-for-the-trunk-and-three-branches-25-Max-pooling-layers ) for training the model. In this work, we use SVM classifier, HaarNet, Linear Discriminant Analysis (LDA) with an advanced deep learning technique, deep neural networks. We also make use of tensor flow for the backend purposes and also for the essential machine learning libraries.
Slaying the Crypto Dragons: Towards a CryptoSure Trust Model for Crypto-economics

In order to move towards a CryptoSure regime, systems based on blockchain and distributed ledger technology have to give up the fiction of ‘consensus trustless mechanisms’, and, if they are to provide socially and legally acceptable security, privacy and trust, within the Rule of Law, have to accept the necessity of a Trusted Third Party. For example, the General Data Protection Regulation (GDPR) includes in its provisions Article 17, the Right to be Forgotten, which could potentially be a formidable barrier to the ubiquitous introduction of cryptographic blockchain software and technology outside of a CryptoSure regime. The investment mania that there has been for blockchain technology, with much money having gone into bitcoin and other cryptocurrencies, blockchain, smart contracts and distributed ledger technology, has been significantly fuelled by the ‘black cash’ of drug-dealers, money-launders, traffickers and the like. Crypto Dragons, the many and varied Financial Disputes over Crypto Assets, have arrived, with legal actions mounted by those defrauded increasing. This is not surprising, given that in Q2 2019 alone, misappropriation of cryptocurrency funds netted criminals some $4.26 billion. The foundations of global digital currencies go back well before the Satoshi bitcoin paper of 2008, and those early digital e-commerce visions did not require a cryptographic blockchain ‘mining’, or ‘distributed consensus’, existential model and were not intentioned of being so readily riven with the criminal black market profiteering of money-launders, scammers and fraudsters that bedevil much current cryptocurrency activity. Looking ahead, Facebook’s Libra digital currency could establish a new global e-commerce paradigm much closer to the pre-bitcoin electronic cash visions, and one more compliant with the existing norms and customs of the Rule of Law, where a responsible Trusted Third Party, in this case, Facebook, is fundamental. Cryptocurrencies apart, some blockchain applications more generally are likely here to stay, and the majority will be robust implementations by established major corporations, with most of us, as consumers, hardly needing to know any of the details. For the properly cautious ICT expert and professional, when considering the use of blockchain for any proposed use case, the ‘fundamental things apply’. There is always the need for Trusted Third Parties, and for probative Electronic Evidence. A key point in any court trial will be examination of the Digital Evidence and, although a Crypto Asset may essentially be ‘decentralised digital vapour’, a Court of Law can make a binding order to get forensic traction on it, because of the legally well-established Obligation of Disclosure. This article concludes with a Checklist giving practical, generally applicable wording for an effective Digital Asset Disclosure exercise.
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors

Abstract reasoning ability is fundamental to human intelligence. It enables humans to uncover relations among abstract concepts and further deduce implicit rules from the relations. As a well-known abstract visual reasoning task, Raven's Progressive Matrices (RPM) are widely used in human IQ tests. Although extensive research has been conducted on RPM solvers with machine intelligence, few studies have considered further advancing the standard answer-selection (classification) problem to a more challenging answer-painting (generating) problem, which can verify whether the model has indeed understood the implicit rules. In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables. The latent Gaussian process also provides an effective way of extrapolation for answer painting based on the learned concept-changing rules. We evaluate the proposed model on RPM-like datasets with multiple continuously-changing visual concepts. Experimental results demonstrate that our model requires only few training samples to paint high-quality answers, generate novel RPM panels, and achieve interpretability through concept-specific latent variables.
The Principal Parts of Finnish Nominals

We design an FST-driven computational method to calculate the minimal number of nominal forms—the principal parts—one must know to be able to fully inflect a lexeme in standard Finnish. To do this, we model the nominal inflection pattern as an FST according to the KOTUS inflectional classes. Our results show that knowing five forms always suffices to uniquely determine a nominal’s inflectional class, and to subsequently correctly inflect all the remaining forms. This contrasts with most sources in the literature that tend to assume seven forms are needed.