Reviewerly/exHarmony huggingface dataset:
Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

Abstract Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.
The Social Competences of High School Students from a Contextual Approach

Introduction. The formation of social competences of schoolchildren is an important area of modern science and pedagogical practice. Despite the large number of publications on this issue, there is virtually no research on the analysis of social context, which largely determines the practical implementation of social competences. The aim of this article is to analyse the high schoolʼs activities on social competence development using a contextual approach. The novelty of the approach lies in the identification of social competencies that characterize the effectiveness of the educational impact of the high school, taking into account the subjective context. Materials and Methods. The empirical base of the study includes data collected from sociological surveys conducted in Togliatti schools in 2010, 2014 and 2019 in the monitoring mode (in 2010, n = 1 183, in 2014, n = 945, in 2016, n = 1 745; in 2019, n = 976). The collection of data in the monitoring mode made it possible to identify the dynamics of results over a ten-year period and to determine how well the high school is doing in terms of developing social competences. Results. Among the social competencies that the high school forms, high school students note the ability to work with information, understand people, determine the propensity for science, independence, as well as a tolerant attitude to representatives of other cultures. There are contradictions in the formation of social competencies, and this is especially evident in the assessment of civic qualities. Over the past decade, the number of students who believe that school has prepared them for life has significantly decreased. Discussion and Conclusion. The results contribute to the development of scientific concepts of social competences. The materials of the article will be useful for the study of problems and contradictions of value self-determination of the younger generation. The practical significance of applying the proposed contextual approach is to increase the effectiveness of the school in the formation of studentsʼ social qualities necessary for life in modern society.
Parsing with graph convolutional networks and clustering

When designing machine translation systems, an important task is to represent data using graphs, where words act as vertices, and relations between words in a sentence act as edges. One of these tasks at the first stage of the analysis is the classification of words as parts of speech, and at the next stage of the analysis is to determine the belonging of words to the sentence members’ classes. A robust approach to carry out such a classification is to determine words embeddings by using Graph Convolutional Networks at the beginning of the analysis and then to apply k-means clustering which is an algorithm that splits objects (words) into groups. To determine weights an ordinary network is applied to obtained hidden layers in order to use these weights in subsequent analysis.
Design and Research of Mobile Learning Recommendation System Based on Deep Learning Perspective

Abstract With the advent of the era of big data, the amount of information shows an exponential explosive growth trend. At the same time, the personalized needs of users are increasing. Facing the massive data, how to help users quickly and accurately obtain the information they need is the main challenge facing them at present. With the rapid development of computer technology and communication technology, mobile teaching and deep learning have become new research hotspots in the field of education. Mobile education has become a very fashionable education mode, which not only brings modern people a more convenient education system, but also makes modern learners need not worry about learning time and content. Traditional recommendation methods generally have problems such as sparse data, cold start and difficulty in feature extraction. On the basis of expounding the concept, connotation and main characteristics of deep learning, this paper applies deep learning theory to mobile recommendation system, hoping to gradually improve and promote the existing mobile education and provide an effective learning way and method for learners.
Multilingual Sentiment Analysis using RNN-LSTM and Neural Machine Translation

The growing age of digital presence has made us realize the importance of data and the need to analyze this vast data. This paper addresses the need to classify the vast amount of multilingual text data to perform Sentiment Analysis (SA). Labeled data for multilingual text classification is hard to retrieve, here Neural Machine translation (NMT) is used to propose a labelling system with the help of labelled English dataset which is found in abundance. The proposed model will use Global Vectors (GloVe) as word embeddings which are fed to Recurrent Neural Network-Long short-term memory (RNN-LSTM) to generate context and to classify text in sentiment classes. The major highlight is the labelling system which is used to solve the problem of collecting labelled multilingual text data. The proposed model can also classify sentiment analysis depending on the content of the text, document, or series of text.
ProMT

Current computer systems are vulnerable to a wide range of attacks caused by the proliferation of accelerators, and the fact that current system comprise multiple SoCs provided from different vendors. Thus, major processor vendors are moving towards limiting the trust boundary to the processor chip only as in Intel's SGX, AMD's SME, and ARM's TrustZone. This secure boundary limitation requires protecting the memory content against data remanence attacks, which were performed against DRAM in the form of cold-boot attack and are more successful against NVM due to NVM's data persistency feature. However, implementing secure memory features, such as memory encryption and integrity verification has a non-trivial performance overhead, and can significantly reduce the emerging NVM's expected lifetime. Previous work looked at reducing the overheads of the secure memory implementation by packing more counters into a cache line, increasing the cacheability of security metadata, slightly reducing the size of the integrity tree, or using the ECC chip to store the MAC values. However, the root update process is barely studied, which requires a sequential update of the MAC values in all the integrity tree levels.
High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models

The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score.
A case for location based contact tracing

We present an evaluation of the effectiveness of manual contact tracing compared to bulletin board contact tracing. Classical contact tracing relies on reaching individuals who have been in proximity to an infectious person. A bulletin board approach focuses on identifying locations visited by an infectious person, and then contacting those who were at those locations. We present results comparing their effects on the overall reproductive number as well as the incidence and prevalence of disease. We evaluate them by building a new agent based simulation (ABS) model using the Susceptible Exposed Infectious and Recovered (SEIR) framework for disease spread. We conduct simulation experiments to quantify the effectiveness of these two models of contact tracing by calibrating the model to be compatible with SARS-CoV-2. We find that bulletin board contact tracing gives comparable results in terms of the reproductive number, duration, prevalence and incidence but is less resource intensive, easier to implement and offers a wider range of privacy options. Our experiments show that location-based bulletin board contact tracing can improve manual contact tracing.
Deep fake : An Understanding of Fake Images and Videos

The Deepfake algorithm allows its user to create fake images, audios, videos that gives very real impression but is fake in real sense. This degree of technology is achieved due to advancements in Deep Learning, Machine Learning, Artificial Intelligence and Neural Networking that is a combination of algorithms like generative adversarial network (GAN), autoencoders etc. Any technology has its positive and negative repercussions. Deep fake can come in use for helping people who have lost their speech to give them new improved voice, commercially deepfake can be used in improving animation or movie quality putting in creative imagination to work as well is therapeutic to people who have lost their dear once. Negative aspects of deep fake include creating fake images, videos, audios that look very real can cause threats to an individual’s privacy, organizations, democracy, and even national security. This review paper presents history on how deep fake emerged, will comprehend on how it works including various algorithms, major research works done on understanding deep fakes in the literature and most importantly discuss recent advancements in detection of deep fake methods and its robust preventive measures.
Graph Tikhonov Regularization and Interpolation Via Random Spanning Forests

Novel Monte Carlo estimators are proposed to solve both the Tikhonov regularization (TR) and the interpolation problems on graphs. These estimators are based on random spanning forests (RSF), the theoretical properties of which enable to analyze the estimators' theoretical mean and variance. We also show how to perform hyperparameter tuning for these RSF-based estimators. TR is a component in many well-known algorithms, and we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least squares. In the experiments, we illustrate the proposed methods on several problems and provide observations on their run time.
Factors Affecting Fault Tolerance during Load Balancing in Cloud Computing

Cloud computing is built upon the advancement of virtualization and distributed computing to support cost-efficient usage of computing resources and to provide on demand services. After methodical analysis on various factors affecting fault tolerance during load balancing is performed and it is concluded that the factors influencing fault tolerance in load balancing are cloud security, adaptability etc. in comparatively more software firms. In this paper, we have created a model for various IT industries for checking the fault tolerance during Load balancing. An exploration is done with the help of some renowned IT farms and industries in South India. This work consists of 20 hypotheses which may affect the fault tolerance during load balancing in South India. It is verified by using potential statistical analysis tool i.e. Statistical Package for Social Science (SPSS).
The Psycho-Didactic Approach in Religious and Moral Education. Towards Personal Growth and Positive Mental Health of Students

The psycho-didactic approach in education is becoming more and more popular. Its supporters emphasize the great values that are associated with the implementation of this approach to various fields of science and school subjects. The greatest value of the psycho-didactic approach is supporting the personal growth of the learner by respecting the developmental factors involved in each subject’s learning processes. Psycho-didactics could also be used in the design of core curricula and their implementation in the field of religious, ethical, and moral education. By supporting personal development through moral and religious education, it is possible to simultaneously influence the agencyof each learner. Personal agency in self-development, as well as in religious and moral development, is one of the greatest challenges for modern education and one of the most significant issues of positive mental health advancement in the field of education. Within psycho-didactics, it becomes possible to foster every student in an individual way, therefore all methods and techniques used in the psycho-didactic approach are focused on the student’s needs.
A review of intelligent optimization algorithm applied to unmanned aerial vehicle swarm search task

Collaborative search is one of the key application fields of UAV swarm, Efficient and accurate algorithm is very important to complete the task of UAV swarm search, and the dynamic and real-time uncertainty of unmanned aerial vehicle swarm search task makes the problem very difficult. Therefore, in the past few years, a large number of scholars have shown strong interest in the problem of UAV swarm search task. With the rapid development of computer technology and Intelligent optimization algorithm, many Intelligent optimization algorithm have been proposed to solve this problem. However, the research on cooperative control and search algorithm is still not comprehensive, and there is a lack of induction and summary of recent research results. The purpose of this paper is to introduce the mathematical model of the search task and give a comprehensive review of the intelligence algorithms used in the swarm search task in recent years and their improvement. In addition, the results and efficiency of each algorithm to solve UAV search tasks are compared, and the advantages and disadvantages of different swarm intelligence algorithms applied to UAV swarm search tasks are summarized and summarized, so as to provide useful reference for UAV swarm to complete search tasks in the future.
Learning Novelty Detection Outside a Class of Random Curves with Application to COVID-19 Growth

Abstract Let a class of proper curves is specified by positive examples only. We aim to propose a learning novelty detection algorithm that decides whether a new curve is outside this class or not. In opposite to the majority of the literature, two sources of a curve variability are present, namely, the one inherent to curves from the proper class and observations errors’. Therefore, firstly a decision function is trained on historical data, and then, descriptors of each curve to be classified are learned from noisy observations.When the intrinsic variability is Gaussian, a decision threshold can be established from T 2 Hotelling distribution and tuned to more general cases. Expansion coefficients in a selected orthogonal series are taken as descriptors and an algorithm for their learning is proposed that follows nonparametric curve fitting approaches. Its fast version is derived for descriptors that are based on the cosine series. Additionally, the asymptotic normality of learned descriptors and the bound for the probability of their large deviations are proved. The influence of this bound on the decision threshold is also discussed.The proposed approach covers curves described as functional data projected onto a finite-dimensional subspace of a Hilbert space as well a shape sensitive description of curves, known as square-root velocity (SRV). It was tested both on synthetic data and on real-life observations of the COVID-19 growth curves.
Scalable Distributed Data Anonymization

We present an approach for enabling a distributed anonymization process over large collections of sensor data. Our approach anonymizes large datasets (which might not fit in main memory) using an arbitrary number of workers within the Spark framework. We describe how to parallelize the anonymization process through a proper partitioning of the dataset. Our experimental evaluation shows that the proposed approach is scalable and do not affect the quality of the anonymized dataset.
Bound entangled singlet-like states for quantum metrology

Bipartite entangled quantum states with a positive partial transpose (PPT), i.e., PPT entangled states, are usually considered very weakly entangled. Since no pure entanglement can be distilled from them, they are also called bound entangled. In this paper, we present two classes of (2d×2d)-dimensional PPT entangled states for any d≥2 which outperform all separable states in metrology significantly. We present strong evidence that our states provide the maximal metrological gain achievable by PPT states for a given system size. When the dimension d goes to infinity, the metrological gain of these states becomes maximal and equals the metrological gain of a pair of maximally entangled qubits. Thus, we argue that our states could be called “PPT singlets.”Received 16 November 2020Revised 18 March 2021Accepted 29 March 2021Corrected 8 June 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023101Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionEntanglement manipulationQuantum metrologyGeneral PhysicsQuantum Information
Educational Dominants in Vasyl Sukhomlynsky’ Pedagogical Concept

The primary dominant of the educational process is the upbringing of the child’s need for another person, no compulsion to live among others or collective interest, and personal needs that is close to the soul. All the activity of the teacher was filled with the boundless faith in the child, the generosity and nobility of his/her soul, the inexhaustible wealth of intellectual and moral strength, the enormous educational opportunities that society creates. He wrote that the years of childhood were first of all the upbringing of the heart, which became its pedagogical meaning. Love for a child’s heart is born in someone who is able to dispel anxiety and confusion, comfort, instill faith in their own strength; who is able to protect vulnerable, very sensitive children feelings i.e. a sense of dignity. He was convinced that empathy is a sensitive period in the formation of the child moral «I». Such moral concepts as: good and evil, justice and injustice, truth and lie, sincerity and selfishness, are manifested in the child’s mind first and remain in children’s emotional memories for life. The pedagogical world of V. Sukhomlynsky is complex and diverse. It has constantly understood by scientists and educators of the new era. To deeply understand the teacher-thinker, it is necessary to rise above his professional impulses, to comprehend the depths of his humanistic worldview, which culminated in the pedagogy of higher wisdom i.e. the wisdom of education. His life was filled with joys and sorrows, hopes and disappointments, creative prospects and self-dissatisfaction.
DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis.

Singing voice synthesis (SVS) system is built to synthesize high-quality andexpressive singing voice, in which the acoustic model generates the acousticfeatures (e.g., mel-spectrogram) given a music score. Previous singing acousticmodels adopt simple loss (e.g., L1 and L2) or generative adversarial network(GAN) to reconstruct the acoustic features, while they suffer fromover-smoothing and unstable training issues respectively, which hinder thenaturalness of synthesized singing. In this work, we propose DiffSinger, anacoustic model for SVS based on the diffusion probabilistic model. DiffSingeris a parameterized Markov chain which iteratively converts the noise intomel-spectrogram conditioned on the music score. By implicitly optimizingvariational bound, DiffSinger can be stably trained and generates realisticoutputs. To further improve the voice quality, we introduce a \textbf{shallowdiffusion mechanism} to make better use of the prior knowledge learned by thesimple loss. Specifically, DiffSinger starts generation at a shallow stepsmaller than the total number of diffusion steps, according to the intersectionof the diffusion trajectories of the ground-truth mel-spectrogram and the onepredicted by a simple mel-spectrogram decoder. Besides, we train a boundaryprediction network to locate the intersection and determine the shallow stepadaptively. The evaluations conducted on the Chinese singing datasetdemonstrate that DiffSinger outperforms state-of-the-art SVS work with anotable margin (0.11 MOS gains). Our extensional experiments also prove thegeneralization of DiffSinger on text-to-speech task.
AHMoSe: A knowledge-based visual support system for selecting regression machine learning models

Decision support systems have become increasingly popular in the domain of agriculture. With the development of automated machine learning, agricultural experts are now able to train, evaluate and make predictions using cutting edge machine learning (ML) models without the need for much ML knowledge. Although this automated approach has led to successful results in many scenarios, in certain cases (e.g., when few labeled datasets are available) choosing among different models with similar performance metrics is a difficult task. Furthermore, these systems do not commonly allow users to incorporate their domain knowledge that could facilitate the task of model selection, and to gain insight into the prediction system for eventual decision making. To address these issues, in this paper we present AHMoSe, a visual support system that allows domain experts to better understand, diagnose and compare different regression models, primarily by enriching model-agnostic explanations with domain knowledge. To validate AHMoSe, we describe a use case scenario in the viticulture domain, grape quality prediction, where the system enables users to diagnose and select prediction models that perform better. We also discuss feedback concerning the design of the tool from both ML and viticulture experts.
Improved dynamic algorithms for longest increasing subsequence

We study dynamic algorithms for the longest increasing subsequence (LIS) problem. A dynamic LIS algorithm maintains a sequence subject to operations of the following form arriving one by one: insert an element, delete an element, or substitute an element for another. After each update, the algorithm must report the length of the longest increasing subsequence of the current sequence.