Reviewerly/exHarmony huggingface dataset:
Managing Graphics Training in Digitalized Environment

The article is devoted to the management of subject learning based on the principle of individualization in the context of the digital transformation of the educational process. Individualized learning technologies help increase student interest in achieving better results. The expediency of using such learning technologies that are focused on individualization has been proven, since individually oriented learning helps to stimulate students to achieve better results. Basic graphic preparation of students is used as an example of subject preparation. There is an example of a prepared educational resource-an electronic workshop, which presents a database of geometric problems with creative content. The necessity of using a practice- oriented project task for the purpose of introducing to professional creativity and mastering methodology of independent work with maximum disclosure of their individual potential is shown. The scheme of working at a project in a digital environment is considered. The target settings of subject-based learning are highlighted. The network schedule of planning individual work of students taking into account different levels of immersion in project activities is discussed. The authors demonstrate a set of tasks for the design of 3d models of objects of varying complexity, contributing to the formation of competencies in the practical implementation of algorithms for geometric constructions. It is concluded that the use of electronic educational resources in the process of basic geometric and graphic training makes it possible to bring the training support closer to the real professional design environment as well as introduces students to the latest innovative technologies.
LRS-Net: invisible QR Code embedding, detection, and restoration

QR code is a powerful tool to bridge the offline and online worlds. It has been widely used because it can store a large amount of information in a small space. However, the black-and-white style of QR codes is not attractive to the human eyes when embedded in videos, which greatly affects the viewing experience. Invisible QR code has proposed based on temporal psycho-visual modulation (TPVM) to embed invisible hyperlinks in shopping websites, copyright watermarks in movies, etc. However, existing embedding and detection methods are not robust enough. In this paper, we adopt a novel embedding method to greatly improve the visual quality of the embedded video. Furthermore, we build a new dataset of invisible QR codes named 'IQRCodes' to train deep neural networks. At last, we propose localization, refinement, and segmentation neural netowrks (LRS-Net) to efficiently detect and restore invisible QR codes that are captured by mobile phones.
Image Classification to identify Transverse Cirrus Band Clouds using Convolutional Neural Networks

The NASA IMPACT team is investigating a certain type of cloud called Transverse Cirrus Bands (aka. TCB), that can cause dangerous levels of aviation turbulence for nearby planes. We wanted to collaborate with the NASA IMPACT team by exploring how machine learning models that can detect Transverse Cirrus Band clouds in satellite images. To accomplish this task we decided to use various CNN models and data preprocessing methods to determine which would produce the most accurate detection. For our CNN models, we chose Simple Sequential, AlexNet, ResNet50, LetNet5, Google MobileNetV2, and VGG-16. For our data preprocessing methods we shrank our training images, converted images into grayscale, and turned images into edges by using edge detection. Our results showed that training CNNs on the originally colored satellite images produced the best results for most models. We believe there is still potential for images converted into edges to improve model accuracy, but we would need further work to calculate an appropriate edge detection threshold for satellite images of different sizes.
Zero-Shot Voice Cloning Using Variational Embedding with Attention Mechanism

Many voice cloning studies based on multi-speaker text-to-speech (TTS) have been conducted. Among the techniques of voice cloning, we focus on zero-shot voice cloning. The most important aspect of zero-shot voice cloning is which speaker embedding is used. In this study, two types of speaker embeddings are used. One is extracted from the mel spectrogram using a speaker encoder and the other is stored in an embedding dictionary, such as a vector quantized-variational autoencoder (VQ-VAE). To extract embedding from the embedding dictionary, an attention mechanism is applied, which we call attention- V AE (AT - V AE). By employing the embedding extracted by the speaker encoder as a query in the attention mechanism, the attention weights are calculated in the embedding dictionary. This mechanism allows the extraction of speaker embedding, which represents unseen speakers. In addition, training is applied to make our model robust to unseen speakers. Through the training stage, our system has developed further. The performance of the proposed method was validated in terms of various metrics, and it was demonstrated that the proposed method enables voice cloning without adaptation training.
ENGLISH TRAINING WITH DRILLS AND REPETITION FOR SIMPURSIA VILLAGE CORAL CADETS

The service team provides solutions based on the results of an analysis of the situation and problems faced by partners to increase opportunities for youth members to be immediately accepted to work after completing their education at vocational high schools. In terms of the solution's form, namely training and mentoring for youth members to practice spoken English using the drilling and repetition method, the results obtained at the end of this service were: (1) positive and enthusiastic response from members of the youth organization; (2) increased awareness of the importance of spoken English proficiency; (3) willingness to take risks in order to participate; (4) increased spoken English vocabulary, grammar, and pronunciation; (5) greater access to recorded voices of foreign speakers; and (6) more opportunities to practice speaking English in dynamic and enjoyable (but not monotonous) settings. (7) the availability of foreign speakers' voices in the form of mp3 recordings; (8) in their spare time, members of the youth organization are encouraged to listen to foreign speakers' voices, imitate them, and practice independently; and (9) after the training and mentoring is completed, the future availability of online sites for accessing and downloading learning media files.
Mechanical fragmentation vs. electrodynamic pulse disaggregation for weak acid leach sample preparation: Implications for the exploration of unconformity-hosted uranium deposits

The introduction of electrodynamic pulse disaggregation (EPD) as a sample preparation tool, traditionally dominated by mechanical (jaw-crusher) fragmentation (MF), begs the need for a comparison of element concentrations and isotopic ratios results from leaching of samples prepared by each of these methods. With this in mind, element concentrations and Pb isotope ratios were measured for samples from the Cigar Lake unconformity-hosted uranium deposit, Athabasca Basin, Canada using a weak acid leach (2% HNO3) digestion. Six sample preparation methods (SPMs) were established from MF and EPD: SPM1) MF samples sieved to 0.5–1.18 mm; SPM2) MF samples sieved to > 1.18 mm, shaken with DI water to extract a clay-rich water, clay-rich water dried; SPM3) dried aliquot of water after EPD; SPM4) < 2 μm clay-sized mineral fraction separated from settled EDP water; SPM5) EPD samples sieved to < 0.5 mm; and SPM6) EPD samples sieved to 0.5–1.18 mm. Pathfinder elements for unconformity-hosted uranium deposits (U, Pb, Th, Ni, Co, As, Cu, and Zn) show increasing concentrations with SPMs that produced a higher portion of the clay-sized mineral fraction, up to 15.92 μg/g U, 40.91 μg/g Th, and 4.85 μg/g Pb. Sample preparation methods 1, 2, and 4 have the most radiogenic Pb isotope ratios (207Pb/206Pb = 0.10–0.40), with least radiogenic ratios in other SPMs ranging from 207Pb/206Pb = 0.23 to 0.82. The 207Pb/206Pb values, combined with 206Pb/204Pb and 207Pb/204Pb, indicate that an increase in common Pb has diluted the radiogenic components for the least radiogenic fractions. MF and EPD both offer benefits for preparing samples for weak acid leach but results from this study show that clay extracted by EPD (SPM4) is the optimal method, producing higher pathfinder element contents and radiogenic Pb values. This study demonstrates that SPMs can result in variable mixes of the original minerals, which affects element concentrations and Pb isotope ratios that are extracted. The modification of geochemical results via SPMs, with the leaching of common Pb unrelated to ore system processes and changes to pathfinder elements concentrations, has implications for using weak acid leach and other partial leaches in exploration in sandstone hosted systems for all mineral commodities. The generation of unreliable geochemical results decreases the effectiveness of exploration programs, potentially missing positive anomalies and overall increases the cost associated with ore deposit discovery, offsetting the added costs of SPMs for geochemical surveys.
Identity Verification Using Age Progression &amp; Machine Learning

Facial age progression is the process of synthesizing a face image at an older age based on images showing a person at a younger age. The ability to generate accurate age progressed face images is important for a number of forensic investigation tasks. In this paper we analyze the performance of a number of publicly available age progression applications, with respect to different parameters encountered in age progression including imaging conditions of input images, presence of occluding structures, age of input/target faces, and age progression range. Through the examination and measurement of old enough movement precision within the sight of various conditions, we remove various ends that appear as a bunch of rules identified with factors that criminological craftsmen and age movement scientists should concentrate to deliver improved age movement techniques. Trial results utilizing an information base consisting of sets of face pictures that were recovered from the FGNET dataset. The check framework for faces isolated by as many AI calculations, accomplishes an equivalent exactness pace of 80%.
Cyberattacks on Business Website: Case Study

The main idea of this work based on real cyber attacks to our business website. In particular, our business website works world-wide for the collection via Submission System of scientific papers for publication. This study analyses a real-life situation with malicious actors to our Submission System by submitting empty form without attaching file contains paper body due to the bot used fake image/document to bypass the required file field. The problem in this case was defined as attacks by bot a little more “intelligent” than usual and qualified as Hacktivism with Ideological motivation by Disruption Goal to our Submission system and this problem has been solved. The best solution for this case was found as the identification attacker’s IP address followed by ban malicious IP directly via including it into blacklist of anti-Spam plugin. The result of this work consists logical scheme how to stop cyberattacks to business website. Also, this work details how to identify malicious IP and find it location. The result of this study can be implemented by other business website users.
Sound Zoning in an Ad-hoc Wireless Acoustic Sensor and Actuator Network

In this paper, the pressure matching (PM) method to sound zoning is considered in an ad-hoc wireless acoustic sensor and actuator network (WASAN) consisting of multiple audio-devices with loudspeakers and microphones. The goal of sound zoning is to simultaneously create different zones with different dominant sounds. To obtain this, a network-wide objective involving the acoustic coupling between all the loudspeakers and microphones is presented where the optimal solution is obtained by solving a quadratically constraint quadratic Program (QCQP). To allow for distributed processing, a Gauss-Seidel type algorithm is proposed. It requires only that all the nodes have access to the different microphone signals, but other than this there is no need for communication between different nodes or with a fusion center (FC). The algorithm is referred to as the distributed adaptive PM algorithm (DA-PM). The algorithm is proven to converge to the optimal solution, as also illustrated by Monte Carlo simulations and evaluated in a simulated acoustic environment.
A Machine Learning-Based Congenital Categorization Approach in Early Recognition of Breast Carcinoma

Nowadays, breast carcinoma is one of the maximum leading reasons for cancer dying inside with the less evolved international locations in the world. This is partially due to a change in the lifestyle that has resulted in a rise in the number of cases. The cancer of the breast develops from the internal part of milk ducts and may be invasive or non-invasive. Mammography, especially when incorporating with computer-aided detection, can now generate a figure that is accurate adequate for diagnosis, and virtual mammography allows for three-dimensional transmission. The proposed system’s motive is the creation of a novel computer-assisted diagnostic method for detecting destructive and dangerous (cancer generate) mammograms. This research has focused to create a figure processing algorithm that can reliably identify and classify breast lesions automatically. The radiologist was able to improve his assurance accuracy with the aid of a CAD method. To classify the breast malignant sarcoma as cordially, a supported vector-based machine (SVM) morpheme is used and included the best features of the discovery that play a key role in attaining the ideal turnout, which is then appointed and linked to artificial neural network (ANN) to train and identify.
Machine Learning based Twitter Sentiment Analysis on COVID-19

Corona virus is a deadly disease that has been spreading around the world for many years. COVID-19 is a new variant of coronavirus which causes its least attack as cold, fever and headache to its severe attack as breathing problems and death. Twitter is a social network tool where a large number of people communicate and express their feelings through tweets and posts in everyday life. Our task is to analyze the perception of people from all over the world during this pandemic. So, analyzing the sentiment of COVID-19 tweets will be one way to find out how people's emotions and the situations are in the world. This system uses various vectorizers, NLP and NLTK tools, which can be used while building various machine learning models for n-grams, and, consequently, to classify tweets into bi-class and tri-class. This system shows a maximum accuracy of 96.7%.
Controlling Factors on the Development and Connectivity of Fracture Network: An Example from the Baekildo Fault in the Goheung Area

The Baekildo fault, a dextral strike-slip fault developed in Baekil Island, Goheung-gun, controls the distribution of tuffaceous sandstone and lapilli tuff and shows a complex fracture system around it.In this study, we examined the spatial variation in the geometry and connectivity of the fracture system by using circular sampling and topological analysis based on a detailed fracture trace map.As a result, both intensity and connectivity of the fracture system are higher in tuffaceous sandstone than in lapilli tuff.Furthermore, the degree of the orientation dispersion, intensity, and average length of fracture sets vary depending on the along-strike variation in structural position in the tuffaceous sandstone.Notably, curved fractures abutting the fault at a high angle occur at a fault bend.Based on the detailed observation and analyses of the fracture system, we conclude as follows: (1) the high intensity of the fracture system in the tuffaceous sandstone is caused by the higher content of brittle minerals such as quartz and feldspar.(2) the connectivity of the fracture system gets higher with the increase in the diversity and average length of the fracture sets.Finally, (3) the fault bend with geometric irregularity is interpreted to concentrate and disturb the local stress leading to the curved fractures abutting the fault at a high angle.This contribution will provide important insight into various geologic and structural factors that control the development of fracture systems around faults.
ПРОФЕСІЙНА ПЕДАГОГІЧНА ДІЯЛЬНІСТЬ МАЙБУТНІХ УЧИТЕЛІВ ЯК ЧИННИК ЇХ ПРОФЕСІЙНОЇ САМОРЕАЛІЗАЦІЇ

The article considers the influence of professional pedagogical activity of future teachers during the period of study in higher pedagogical education on complex, stage and dynamic processes of their professional selfrealization, the analysis of practical experience of studying the problem of professional self-realization of future teachers in H. S. KhNPU.
Few-Shot Classification with Multi-task Self-supervised Learning

Few-shot learning aims to mitigate the need for large-scale annotated data in the real world. The focus of few-shot learning is how to quickly adapt to unseen tasks, which heavily depends on outstanding feature extraction ability. Motivated by the success of self-supervised learning, we propose a novel multi-task self-supervised learning framework for few-shot learning. To alleviate the deficiency of annotated samples in few-shot classification tasks, we introduce and analyze three different aspects, i.e., data augmentation, feature discrimination, and generalization, to improve the ability of feature learning. The proposed method achieves clear classification boundaries for different categories and shows promising generalization ability. Experimental results demonstrate that our method outperforms the state-of-the-arts on four few-shot classification benchmarks.
An Identifying Campus Garbage Application Based on Optimized Convolutional Neural Networks

This paper proposes to present an effective and practical mobile garbage identification application named GCNet (Garbage Classification Network), which is capable of classifying different types of campus garbage and estimating the results using convolutional neural networks. The GCNet consists of three major parts. First, a data enhancement based on image stitching is designed to enrich data sets and enhance network robustness. Then we propose a spatial attention for reducing model structure and detection accuracy enhancement. Deformable convolution, the third component, is used to solve the problem of loss of sampling details or burr of normal convolution. Furthermore, we deployed the GCNet model to the mobile terminal and designed a mobile terminal intelligent campus garbage identification application. The experimental results show that the proposed GCNet performs well on campus garbage detection with 89.8&#x0025; mean average precision (mAP), which outperforms the state-of-the-art methods. The running time on our application could achieve 0.083s per image, meeting the real-time detection. The proposed method is effective and applicable for accurate and real-time campus garbage detection.
Towards Explainable Visual Emotion Understanding

Emotion understanding from an image is an important computer vision research topic, but the reasoning behind the justification has been largely unexplored. We propose to utilize language, and more specifically, explanation of the judgement to increase the explainability for this task. We collect a dataset with image, emotion tag and explanation in natural language, and conduct analysis on the dataset to gain insights on the ambiguity of the human emotion perceptual. We examine baseline methods to predict emotion from image, explanation and general image description, and unifying both modalities. Our experiments shed lights on effects from different modalities and we also identify opportunities for future visual emotion categorization research based on the analysis. We release our dataset to advance future research.
Application of Software for the Purpose of Enhancing Online Education

Last year everything was changed, education was no exception. The exercises in the discipline "Mobile Cellular Networks" needed to be done online due to the COVID-19 pandemic. This provoked the lecturer to use software to give students the opportunity to do exercises alone. MATLAB was chosen for a software program. The paper presents the features of the software products used in the transition to online training in the discipline "Mobile Cellular Networks", one of the topics covered in the course, and the opinion of the students about the teaching methodologies applied in the educational process.
Emerging Applications in Healthcare and Their Implications to Academia and Practice

Data is important for health applications. Clinicians and academics use structured and unstructured data to arrive at both diagnosis and prognosis. Artificial Intelligence (AI) plays a significant role in identifying various clinical diagnostic elements using data. These clinical diagnostic elements are not readily visible to humans. In this tutorial, we bring together academics, clinicians, health administrators from both public and private health and research scholars to discuss their experiences in developing and implementing AI-based cutting-edge healthcare and medical applications.
Predicting User Preferences with Neural Network Learning to Rank Method

User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their target customers. The user preference is also needed in the movie domain area, as the film companies who produce the movie also need to understand how the customers value their movie products, whether the market like it or not. This can help them to predict the best components for the future movie. In order to produce the recommendation to the customer, we can show a list of item in a correct order, sorted from the most relevant/the most liked one to the most irrelevant item. In this paper, we utilize the Learning to Rank (LTR) approach from information retrieval domain area to learn the user preference in recommender system domain. We applied LTR with pairwise approach from the two most popular algorithms in this area, i.e. RankNet and LambdaRank, on 25,345 user ratings from 5,000 movie dataset and evaluate both algorithms. We used a Neural Network architecture as the learning mechanism. We also measuring the speed performance of the two algorithms.