Reviewerly/exHarmony huggingface dataset:
ДИНАМІКА РОЗВИТКУ СИНДРОМУ ВИГОРАННЯ ФАХІВЦІВ СОЦІОНОМІЧНОГО ПРОФІЛЮ ЧЕРЕЗ ПРИЗМУ ПОЗИТИВНОЇ ПСИХОТЕРАПІЇ Н. ПЕЗЕШКІАНА

Burnout syndrome is a problem of modern society, which is manifested through the stress of modern conditions, shortage of external and internal resources and imbalance of vital energy of this sociological specialist, namely, they suffer the most from the phenomenon. Positive psychotherapy is a resource-oriented, humanistic and transcultural method, officially recognized by the European Psychotherapy Association (EAP) in 1996. Also, positive psychotherapy is a conflict-centered method that contains a psychodynamic component. Conflict is one of the central factors of psychodynamics. The meaning of «conflict» from Latin is interpreted as a «conflict» of interests, various conflicting positions, obstacles to meet any needs. Conflict is a signal of imbalance. This article reveals the psychodynamic component of this method, which analyzes the differential-substantive analysis of the development of burnout through the prism of positive psychotherapy. The dynamics of burnout syndrome is characterized by the levels of meaningful analysis of personality: the actual level (symptomatic), key level (content) and basic level. Accordingly, conflicts are identified at each of these levels. The actual conflict is manifested in micro-events (frequent stressful situations) and macro-events (pandemic, war, etc.). Located and processed in the spheres of the balance model (body, achievement, contacts, fantasies), there is an imbalance in the distribution of life energy, where the individual pays excessive attention to the sphere - achievement (fatigue, etc.), and later the sphere - fantasies (loss of motivation and meaning). The basic conflict is manifested in the previous experience of parents, the environment, as well as forms of interaction in the model to follow, recorded in the form of concepts. Burnout syndrome will manifest itself in the sphere of «I», where the individual has formed concepts about himself and the scenario of his life, which leads him to fatigue, and later to exhaustion. Most often, specialists who have a high level of symptoms of burnout are guided by the following fixed ideas: «I’m ok when I do everything perfectly», «How ok when I do everything» and so on. Internal conflict is a conflict of early children’s institutions with variable activities, reactivation of the basic conflict through the current conflict (VC = BC + AC), which is defined as an unsolution, a stalemate. Thus, based on the study, we can interpret that burnout is an internal conflict of personality, which was caused by actual conflict (micro-events and macro-events) and the formed pattern of behavior that was familiar to professionals, but ceased to be relevant and help solve stressful situations (вasic conflict).
Faster Algorithm for Turn-based Stochastic Games with Bounded Treewidth

Turn-based stochastic games (aka simple stochastic games) are two-player zero-sum games played on directed graphs with probabilistic transitions. The goal of player-max is to maximize the probability to reach a target state against the adversarial player-min. These games lie in NP ∩ coNP and are among the rare combinatorial problems that belong to this complexity class for which the existence of polynomial-time algorithm is a major open question. While randomized sub-exponential time algorithm exists, all known deterministic algorithms require exponential time in the worst-case. An important open question has been whether faster algorithms can be obtained parametrized by the treewidth of the game graph. Even deterministic sub-exponential time algorithm for constant treewidth turn-based stochastic games has remain elusive. In this work our main result is a deterministic algorithm to solve turn-based stochastic games that, given a game with n states, treewidth at most t, and the bit-complexity of the probabilistic transition function log D, has running time O ((tn2 log D)t log n). In particular, our algorithm is quasi-polynomial time for games with constant or poly-logarithmic treewidth.
An Innovative Method to Predict the Accuracy of Phishing Websites by Comparing Logistic Regression Algorithm with Support Vector Machine Algorithm

To compare novel LR with the SVM technique to estimate the precision of phishing websites. Materials and Methods: The SVM method's algorithm for supervised learning (N = 20) is compared to the Logistic Regression algorithm's supervised learning algorithm (N = 20). To achieve great precision, the G power value is set to 0.8. Machine Learning is used in the framework. Compared to the SVM approach, LR has more precision (92.00%). (90.26%). With a confidence value of 95%, the impartial T-Test was run (p =.375), indicating the importance score that is statistically insignificant (p>0.05). Conclusion: The LR approach appeared to detect phishing websites with greater accuracy than the SVM technique.
White Blood Cell Image Generation using Deep Convolutional Generative Adversarial Network

White blood cells (WBCs) are a crucial component of the human immune system in medicine. The traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Insufficient data or unbalanced samples can also cause a low classification accuracy of a deep learning model used for medical diagnosis. The deep convolutional generative adversarial network (DCGAN) is the base of this study and is employed to produce images. The experiment show that the model gives 99.44% accuracy for generation of WBC blood cell image.
Radial Basis Functions Neural Network on Microcontrollers

This paper presents an implementation of a neural network based on radial functions and evaluates its performance using general purpose microcontrollers. As an example, an application of EGC signal classification based on the wavelet transform is presented. The evaluation of the implementation considers the number of clusters in which the inputs are divided, and the type of wavelet transformation used. The main results indicate that the implementation of a neural network based on radial functions is possible in real time (testing phase) with very good classification accuracy.
ACCR: Auto-labeling for Ancient Chinese Handwritten Characters Recognition on CNN

Chinese Character Recognition(CCR) is a critical application of Optical Character Recognition(OCR), a vital area of pattern recognition. Research on CCR in the past decades mainly focused on the modern Chinese characters, but not on the ancient ones. Compared to modern Chinese characters, ancient characters are more diverse and multiple ancient characters can correspond to one modern character. When doing recognition, the unique features of ancient Chinese characters cause a significant amount of time on manual labeling. This paper proposes an automatic labeling algorithm based on a semi-supervised dictionary training neural network that drastically decreases human effort. We first created an offline training set as a dictionary including 8,226 Chinese characters from ancient documents in modern fonts. And put the set into the network. Then we recursively retrained the network on an unlabeled data set of about 1.3 million characters images segmented from ancient documents resulting in a very high accuracy rate of 98.96 %. This work is one part of our wide recognition of ancient documents with handwritten Chinese characters project.
Current Situation of The Digital Transformation of The Mongolian Education Sector

Digital learning is a comprehensive, innovative approach to the provision and management of education. In order to incorporate this approach into our education system, there is a need for the capacity building of human resources, incorporate distance education resources, adapt curricula and teaching methodologies to distance education, enhance students’ and instructors’ digital capacities, plan and develop a comprehensive open University system and diligently research the potential risks that are posed to our students’ and instructors’ wellbeing, privacy and security as a result of the digital transformation. &#x0D; Policies and programs such as Mongolia’s “Sustainable Development Goals”, “Vision 2050 – Mongolia’s long-term development policy”, “Digital Society medium-term program 2022-2027”, “2020 – 2024 Mongolian Government operational program”. “Medium-term Education Sector Development Plan” have established the legal environment for the incorporation of digital technologies into the education sector. &#x0D; As a result of the Covid-19 pandemic that has ravaged the world since 2019 a dire need to transform the development strategies, plans and education arose in countries across the world. Attempts to prevent and minimize the spread of Covid-19 further necessitated the development and conduct of online and hybrid classes and we have been using digital technologies to maximize the effectiveness of education in the New Normal. &#x0D; The purpose of this paper is to determine the issues facing the digital transformation of education in terms of management, organization, curriculum, content, methodology, digital competencies of specialists, infrastructure, online resources, as well as education financing and propose solutions to some of the key issues.
Statistical tests for text homogeneity: using forward and backward processes of numbers of different words

The processes of growth in the number of diverse words in a text, when reading in the forward and backward directions, are studied in this article. Based upon the statistics achieved from the difference between these two processes, we construct a statistical test. This statistical test is used for text homogeneity checks. The elementary model states that words in a text are selected from some dictionary independent of each other according to the Zipf–Mandelbrot law. P-values of the statistical test are calculated based on the elementary probabilistic model using the asymptotic normality of corresponding statistics. At last but not least, this statistical test is applied for the analysis of homogeneity of sequences of sonnets.
Attempting at parametrization of moderate-length poetic texts: Moses, a poem by Ivan Franko

The aim of this study is to find parameters that can be used for classification of not very long texts, for example, by author, genre, etc. We go through various known parameters and analyze to what extent they are useful for the intended purposes. We also suggest some improvements that need to be checked further. We calculate the values of parameters at various points of text comprising N tokens (running words) counted from the beginning of text. As parameters with prospects of author and/or language attribution we identify, in particular, the h-point scaling coefficient, Yule’s K, relative repeat rate, and the fraction of dis legomena. These parameters demonstrate quite stable behavior in N. Another set includes scaling exponents of parameters with respect to N. Certain modifications are suggested for Lambda and entropy introducing logarithmic corrections being powers of ln N. The results are applicable for texts of thousands to tens of thousand words.
MAHGE: Point-of-Interest Recommendation Using Meta-path Aggregated Heterogeneous Graph Embeddings

The rapid growth of Location-Based Social Networks (LBSNs) has led to the generation of large amounts of users' check-in data, which has driven the development of many location-based recommendation services. Point-of-Interest (POI) recommendation is one such service that helps users find places they are interested in based on the current time and location. Unlike traditional recommendation tasks, users' check-in data contains rich heterogeneous data such as time, geographical information and social relationship information; thus it is challenging to capture the complex contextual relationships between these heterogeneous information for POI recommendation. To solve this problem, we propose a Metapath Aggregated Heterogeneous Graph Embeddings method(MAHGE). Specially, it firstly proposes a novel method to construct the heterogeneous LBSN graph which innovatively models time as the relationship on the edges of the graph in order to capture the complex dependency between user and time. Then, it proposes to profile the target node based on meta-paths because meta-path reflects the characteristics of target node from a multi-dimensional perspective. Moreover, it introduces a graph embedding method based on meta-path aggregation to learn the vector representation of the target node with attention mechanism. Finally, extensive experiments on two real-word datasets are conducted, and the results show the effectiveness of this method.
IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C). The aim of MobileB2C is bench-marking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/BiDAlab/MobileB2C_BehavePassDE, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. “Random” (different users with different devices) and “skilled” (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a non-trivial challenge. MobileB2C will be established as an on-going competition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://sites.google.com/view/mobileb2c/.
A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data

Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation resource requirements, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted to the pooled dataset. We demonstrate the performance of Fed-GLMM in numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities.
Robotic Control in Adversarial and Sparse Reward Environments: A Robust Goal-Conditioned Reinforcement Learning Approach

With deep neural networks based function approximators, reinforcement learning holds the promise of learning complex end-to-end robotic controllers that can map high-dimensional sensory information directly to control policies. However, a common challenge, especially for robotics, is sample-efficient learning from sparse rewards, in which an agent is required to find a long sequence of “correct” actions to achieve a desired outcome. Unfortunately, inevitable perturbations on observations may make this task trickier to solve. Here, this paper advances a novel robust goal-conditioned reinforcement learning approach for end-to-end robotic control in adversarial and sparse reward environments. Specifically, a mixed adversarial attack scheme is presented to generate diverse adversarial perturbations on observations by combining white-box and black-box attacks. Meanwhile, a hindsight experience replay technique considering observation perturbations is developed to turn a failed experience into a successful one and generate the policy trajectories perturbed by the mixed adversarial attacks. Additionally, a robust goal-conditioned actor-critic method is proposed to learn goal-conditioned policies and keep the variations of the perturbed policy trajectories within bounds. Finally, the proposed method is evaluated on three tasks with adversarial attacks and sparse reward settings. The results indicate that our scheme can ensure robotic control performance and policy robustness on the adversarial and sparse reward tasks.
Protection of images by combination of vernam stream cipher, AES and LSB steganography in a video clip

Visual communication has become more popular in recent years, and because data must be transferred safely over a restricted bandwidth, techniques of data security and preservation, such as masking and encryption, have to be included after the optimization process for the image in question. The two most common methods of data protection are encryption and steganography. Steganography is a way for covering data that is hidden in another medium without leaving any proof of the data being changed, whereas cryptography converts regular data into incomprehensible data, which is known as scrambled data. Using the least significant bit (LSB) technique, the information was scrambled with graphics. The Vernam encryption algorithm and the advanced encryption standard (AES) will have a side in the proposed method in the encryption step, and the three improvement proposals using quality standards and encryption will be compared with the Vernam encryption algorithm and the AES encryption algorithm, and the effect of the improvement ratio and the size of the encrypted data with different threshold values will be investigated.
Lossless Encoding of Mental Cutting Test Scenarios for Efficient Development of Spatial Skills

In the last decade, various mobile applications have been developed to improve and measure spatial abilities using different spatial tests and tasks through augmented reality (AR), Virtual Reality (VR), or embedded 3D viewers. The Mental Cutting Test (MCT) is one of the most well-known and popular tests for this purpose, but it needs a vast number of tasks (scenarios) for effective practice and measurement. We have recently developed a script-aided method that automatically generates and permutes Mental Cutting Test scenarios and exports them to an appropriate file format (to GLB (glTF 2.0) assets) representing the scenarios. However, the significant number of permutations results in more than 1,000,000 assets, requiring more than 6 GB of storage space. This paper introduces an encoding scheme consisting of four stages to handle this issue through significantly reducing the storage space, making the app suitable for everyday individual use, even on a mobile phone. The proposed method encodes a subset of assets from which it can decode the whole dataset with 3% time complexity compared to classical Blender’s computations, exceeding the compression ratio of 10,000 and storage space saving 99.99%. This paper explains the features of the original assets, introduces the encoding and decoding functions with the format of documents, and then measures the solution’s efficiency based on our dataset of MCT scenarios.
Effects of LLDPE on Mechanical Properties, Degradation Performance, and Water Absorption of Thermoplastic Sago Starch Blends

The purpose of this study was to improve the mechanical and physical properties, degradation performance, and water absorption of a thermoplastic mixture of sago starch with the addition of Linear Low-Density Polyethylene (LLDPE).The method used is the grafting method where polyethylene is grafted onto Maleic Anhydride (LLDPE-g-MA).In this research, Thermoplastics Sago Starch (TPSS) was made with a mixture of Sago Starch (65%) and Glycerol (35%), added with water as a solution.Compatibilizer (PE-g-MA) was made by reacting LLDPE (88%) and Maleate Anhydride (9%) with an additional initiator of Benzoyl Peroxide (3%).The concentration of LLDPE varied from 10,15, 20, 25, and 30%.Substances were mixed into an internal mixer Thermo Haake Polydrive with a speed of 100 mm/minute, for 15 minutes.The concentration of 30% LLDPE produced the highest tensile strength and elongation at a break of 4.30 N/mm2 and 2.15%, respectively.Simultaneously, as LLDPE concentration increased, Young's Modulus decreased.This was powered by the morphology of the sampling surface, with the occurred reaction of adhesion interface or dispersion of LLDPE toward the whole surface with the assistance of compatibilizer as connecting substance between hydrophilic polymer and a hydrophobic polymer, which could improve the properties of mechanical.The formation of ester groups from the reaction of inter groups of hydroxyls derived from the starch with the groups of anhydride derived from the mixture of compatibilizer was visibly seen at the peak of 1693 cm -1 .The degradation performance of the TPSS: LLDPE mixture with a ratio of 70:30 showed good performance where the degradation continued to increase up to 42% on the 30th day of degradation in freshwater, 18% in seawater, and 17% in soil burial.While the water absorption test showed that the higher concentration of LLDPE, the lower speed of water absorption.
HOTS (HIGH ORDER THINKING SKILL) DALAM TES BAHASA JERMAN

In learning, students are trained to reason, analyze and evaluate to find solutions to problems faced. The way to conclude students' achievements in such behavior is a test. This study aims to describe the point of the German question (GER113 Main Problem Package) by asking the question whether the questions fall into the category of HOTS (Higher Order Thinking Skills). This research is a descriptive type of analytical and the object of analysis in this study is a package of German questions used in national examinations. The results showed that the German language test developed by the teacher was still dominated by the question item at a low cognitive level. This conclusion is based on the findings that there are 58.33% of LOTS category questions consisting of 4.16% of the level of wheezing (C1), 16.67% of understanding levels (C2), and 37.5% with applying levels (C3). Then for the HOTS category, there is 41.67% consisting of 37.5% analyze level (C4), 4.17% evaluate level (C5), and 0% creating level (C6).