Reviewerly/exHarmony huggingface dataset:
Kegunaan radiografi panoramik pada masa mixed dentition

Objectives: The purpose of this review is to determine the usefulness of panoramic radiography during mixed dentition and also to capture panoramic radiographs during mixed dentition.&#x0D; Review: Mixed dentition is a period of mixed dentition and a period of transition from sequential deciduous teeth followed by the eruption of the replacement tooth, namely the permanent tooth. The mixed dental phase occurs in children aged 6-12 years, beginning with the eruption of the first permanent tooth, usually a central incisor or mandibular first molar. Changes in occlusion occur significantly during this time due to the loss of the deciduous teeth and the eruption of the replacement permanent teeth.&#x0D; Conclusion: The mixed dentition period can be classified into 3 phases, namely. (1) the first transitional period, occurs at 6-8 years of age. In this phase, the eruption of the permanent first molars and the replacement of the deciduous incisors with the permanent incisors occurred. (2) the inter-transitional period, after the first molars and permanent incisors erupt, there is a transient period of about 1-2 years before the second transition phase begins. In this phase, it is called inter-transitional because the maxillary and mandibular arches consist of deciduous and permanent teeth. In the inter-transitional phase it is relatively stable and no changes occur. (3) the second transitional period at age (10-13 years), the date of the mandibular canine at about 10 years of age usually begins the second transitional period.
Measurement of the Zakat Transparency Index of Amil Zakat Institutions (LAZ) in Banten Province, Indonesia

As an institution that manages social funds, transparency must be met for the Amil Zakat Institution. By measuring the zakat transparency index, the Amil Zakat Institution can find out the extent of the implementation of transparency in the organisation. Information disclosure to the public is believed to increase public confidence in paying zakat at the Amil Zakat Institution. This study aims to measure the zakat transparency index of the Amil Zakat Institution in Banten Province. The Simple Additive Weighting (SAW) method is used to calculate the transparency index of zakat institutions. The Simple Additive Weighting (SAW) method is a multiple attribute retrieval method, where the weight of each predetermined dimension is multiplied by each variable weight, then added up. The results showed that the Amil Zakat Institution in Banten Province was included in the reasonably transparent category with an index of 0.706.
Multimedia Instructional Module for the Arts and Culture Subject Applied to Middle School Students

Middle School and High School education inEcuador focuses on integrating educational innovation in the pursuit of educational prosperity based on government projects that sustain proper use of educational technology.The main objective of this research is the implementation of a multimedia instructional module for the subject of arts and culture applied to middle school students of the Educational Institution Eugenio Espejo, which serves as a tool for enhanced significative learning.This project was performed through three phases.First, the pedagogic methodology used by the teachers of the subject was diagnosed.Next, the development of the multimedia instructional module in the portable software eXeLearning INTEF-eXe Project version 2.2 was carried out.Finally, a statistical analysis was performed on the results obtained from the qualitative evaluation before and after the implementation of the multimedia instructional module, using the statistical programming language R, applying MANOVA, ANOVA and Paiwise T-tests in order to demonstrate the differences between the variables obtained from the grades of each partial.The study of the results performed in the average partial grades of the eighth, ninth and tenth grades, in a sample of n = 348 students demonstrates progressive improvement of the student grades, which demonstrates that students are motivated to work with educational technology, even in regions that have limited internet access, which is encouraging since it allows updating students' technological skills that meet today's demanding learning standards.
청소년의 신체상이 우울감에 미치는 영향 : 자기역량지각의 매개효과

Purpose: The purpose of this study was to examine the mediating effect of self-perception on the relationship between body image and depressive symptoms in middle school adolescents. Methods: This study performed a pathway analysis with a sample of 284 adolescents recruited from three middle schools. Self-reported questionnaires consisted of items regarding body image, self-perception, and depressive symptoms. Results: Body image, self-perception, and depressive symptoms showed a significant correlation. Self-perception was directly affected by body image and depressive symptoms were not affected by body image. The mediating effect of self-perception on body image’s impact on depressive symptoms was confirmed. Self-perception had a significant indirect effect on depressive symptoms, which means that self-perception has a full mediating effect on body image’s impact on depressive symptoms. Conclusion: The effect of body image on depressive symptoms was fully mediated by self-perception in early adolescents. It suggests that self-perception needs to be considered when providing nursing interventions for adolescents.
Pravuil: Global Consensus for a United World.

Pravuil is a robust, secure, and scalable consensus protocol for a permissionless blockchain suitable for deployment in an adversarial environment such as the Internet. Pravuil circumvents previous shortcomings of other blockchains: - Bitcoin's limited adoption problem: as transaction demand grows, payment confirmation times grow much lower than other PoW blockchains - higher transaction security at a lower cost - more decentralisation than other permissionless blockchains - impossibility of full decentralisation and the blockchain scalability trilemma: decentralisation, scalability, and security can be achieved simultaneously - Sybil-resistance for free implementing the social optimum - Pravuil goes beyond the economic limits of Bitcoin or other PoW/PoS blockchains, leading to a more valuable and stable crypto-currency
Bayesian Dependent Functional Mixture Estimation for Area and Time-Indexed Data: An Application for the Prediction of Monthly County Employment

The U.S. Bureau of Labor Statistics (BLS) publishes employment totals for all U.S. counties on a monthly basis. BLS use the Quarterly Census of Employment and Wages, where responses are received on a 6–7 month lagged basis and aggregated to county, and apply a time series forecast model to each county and project forward to the current month, which ignores the dependence among counties. Our approach treats these by-county employment time series as a collection of area indexed noisy functions that we co-model. Our model includes predictor, trend and seasonality terms indexed by county. This application is among the first in the U.S. Federal Statistical System to address the joint modeling of a collection of time series expressing heterogenous seasonality patterns between them. We demonstrate that use of a Fourier basis to model seasonality outperforms a locally-adaptive, intrinsic conditional autoregressive construction on our collection of time series where the degree of expressed seasonality varies. County-indexed parameters of the 3 terms are drawn from a dependent Dirichlet process (DDP) prior to allow the borrowing of information. We show that employment of both spatial and industry concentration predictors into the prior probabilities for co-clustering among the counties produces better prediction accuracy. Our DDP prior accounts for the possibility that nearby counties may express distinct underlying economic structures. A feature of our joint modeling framework is that it computes efficiently to support the monthly BLS production cycle. We compare the performances of alternative formulations for the dependent Dirichlet process prior on monthly, county employment data from 2002–2016.
Examining Social Robot Acceptability for Older Adults and People with Dementia

Social robots that aim to support the independence and wellbeing of older adults and people with dementia are being introduced into dementia care settings. However, the acceptability of robots varies greatly between people and the rate that robots are deployed into practice is currently low. This chapter defines robot acceptability and provides an overview of theoretical technology acceptance models. It reviews the empirical literature and identifies the individual and contextual factors that impact acceptability in relation to the needs of older adults and people with dementia, focusing on what potential robot users need to motivate them to accept robots into their everyday lives. Then the literature is discussed in the light of current discourses in gerontology, recommending what is needed to increase the acceptability of robots. The capacity of robots, to communicate in a human-like way needs to increase and robots need to be designed with in-depth end-user collaboration, to be person-centred and deployed in ways that enhance the strengths of people with dementia. Guidance for good practice in participatory design is provided. Longitudinal research that uses triangulated data from multiple sources. is recommended to identify the needs of individuals, significant others, and wider contextual factors.
SOLVING NONSTANDARD INEQUALITIES

This article discusses the problems of teaching non-standard tasks at school. Currently, one of the main tasks of secondary schools is to identify creative thinking abilities of students and solve problems of their development. The report was identified as the main tool for developing students ' mental and thinking abilities. Problem solving is the most productive form of learning mathematics, and this process should be a necessary component of all extracurricular activities conducted in mathematics. Problems at the mathematical Olympiad are non-standard problems. Olympic tasks will usually help you learn a lot, find independent non-standard methods of solving problems. The high school curriculum provides for the use of the definition of inequality, proving using known inequalities, using the relationship between the arithmetic mean and geometric mean of positive numbers, and proving them using methods other than reverse-trip methods
A new Video Synopsis Based Approach Using Stereo Camera

In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of summary videos. Within the scope of this resource, a new method for anomaly detection with object-based unsupervised learning has been developed while creating a video summary. By using this method, the video data is processed as pixels and the result is produced as a video segment. The process flow can be briefly summarized as follows. Objects on the video are detected according to their type, and then they are tracked. Then, the tracking history data of the objects are processed, and the classifier is trained with the object type. Thanks to this classifier, anomaly behavior of objects is detected. Video segments are determined by processing video moments containing anomaly behaviors. The video summary is created by extracting the detected video segments from the original video and combining them. The model we developed has been tested and verified separately for single camera and dual camera systems.
Leveraging the Invariant Side of Generative Zero-Shot Learning

Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions. Specifically, we train a conditional Wasserstein GANs in which the generator synthesizes fake unseen features from noises and the discriminator distinguishes the fake from real via a minimax game. Considering that one semantic description can correspond to various synthesized visual samples, and the semantic description, figuratively, is the soul of the generated features, we introduce soul samples as the invariant side of generative zero-shot learning in this paper. A soul sample is the meta-representation of one class. It visualizes the most semantically-meaningful aspects of each sample in the same category. We regularize that each generated sample (the varying side of generative ZSL) should be close to at least one soul sample (the invariant side) which has the same class label with it. At the zero-shot recognition stage, we propose to use two classifiers, which are deployed in a cascade way, to achieve a coarse-to-fine result. Experiments on five popular benchmarks verify that our proposed approach can outperform state-of-the-art methods with significant improvements.
Eliminating Intermediate Measurements using Pseudorandom Generators

We show that quantum algorithms of time $T$ and space $S\ge \log T$ with unitary operations and intermediate measurements can be simulated by quantum algorithms of time $T \cdot \mathrm{poly} (S)$ and space $ {O}(S\cdot \log T)$ with unitary operations and without intermediate measurements. The best results prior to this work required either $\Omega(T)$ space (by the deferred measurement principle) or $\mathrm{poly}(2^S)$ time [FR21,GRZ21]. Our result is thus a time-efficient and space-efficient simulation of algorithms with unitary operations and intermediate measurements by algorithms with unitary operations and without intermediate measurements. To prove our result, we study pseudorandom generators for quantum space-bounded algorithms. We show that (an instance of) the INW pseudorandom generator for classical space-bounded algorithms [INW94] also fools quantum space-bounded algorithms. More precisely, we show that for quantum space-bounded algorithms that have access to a read-once tape consisting of random bits, the final state of the algorithm when the random bits are drawn from the uniform distribution is nearly identical to the final state when the random bits are drawn using the INW pseudorandom generator. This result applies to general quantum algorithms which can apply unitary operations, perform intermediate measurements and reset qubits.
A compressive multi-kernel method for privacy-preserving machine learning

As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserving machine learning algorithms. Given two objectives, namely, utility maximization and privacy-loss minimization, this work is based on two previously non-intersecting regimes -- Compressive Privacy and multi-kernel method. Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors. The compressive multi-kernel method proposed consists of two stages -- the compression stage and the multi-kernel stage. The compression stage follows the Compressive Privacy paradigm to provide the desired privacy protection. Each kernel matrix is compressed with a lossy projection matrix derived from the Discriminant Component Analysis (DCA). The multi-kernel stage uses the signal-to-noise ratio (SNR) score of each kernel to non-uniformly combine multiple compressive kernels. The proposed method is evaluated on two mobile-sensing datasets -- MHEALTH and HAR -- where activity recognition is defined as utility and person identification is defined as privacy. The results show that the compression regime is successful in privacy preservation as the privacy classification accuracies are almost at the random-guess level in all experiments. On the other hand, the novel SNR-based multi-kernel shows utility classification accuracy improvement upon the state-of-the-art in both datasets. These results indicate a promising direction for research in privacy-preserving machine learning.
Imagination in the Generation of Pictures and Interpersonal Scenarios

Abstract In Imagination, Jim Davies explains that most humans have mental imagery: an ability to make pictures in the mind without immediate perceptual input-as we do when we dream. Davies writes programs that enable computers to do something similar. Given a few words of description, a computer can generate pictures with several objects arranged in appropriate ways. Jonathan Gilmore’s Apt Imaginings is about whether engagement in works of fiction is continuous or discontinuous with how we deal with people and objects in everyday life. He argues for discontinuity. In constructing a critical framework within which to evaluate these two books, I use Kenneth Craik’s theory of mental models. Craik’s theory enables us to understand how we humans have evolved to imagine possible futures, to plan and to act in relation to them, and also to imagine what might be going on in other people’s minds.
LEVERAGING THE MACHINE LEARNING TOOLS AND TECHNIQUES FOR ENHANCING THE EFFICIENCY OF SAFEGUARDS FOR CYBER SECURITY

Considering that the volume of messages overall is 269billion messages each day and that 49.7% of it is spam, messages from fraudsters. These cybercriminals expect to "phish" for by and by touchy data from their casualties or contaminate their PCs with infections or malevolent substance for unlawful monetary profits. This article, hence, clarifies the various ways these online tricks are sustained and presents a few examinations and counter-assault methodology recommendations by AI specialists to handle the issue of spam filtering. This paper reports diverse examination plans and arrangements proposed utilizing AI predictions, going from methods dependent on text classification to systems that analyze email content with attached pictures. The viability and proficiency of these AI apparatuses were found and examined. Taking everything into account, further examination of spam separating devices dependent on AI calculations was supported as cybercriminals ceaselessly developed new techniques that compromise and misuse these frameworks to stay away from spam channels.
Attention is the basis of intellectual activity

This article presents the impact of attention on the intellectual activity of students. In the paradigm of the humanistic approach to reforming the school education system, which emphasizes the importance of schoolchildren acquiring not only a certain set of knowledge, but also the formation ofpersonal qualities that meet the requirements of modern society.
The Interplay between Distribution Parameters and the Accuracy-Robustness Tradeoff in Classification

Adversarial training tends to result in models that are less accurate on natural (unperturbed) examples compared to standard models. This can be attributed to either an algorithmic shortcoming or a fundamental property of the training data distribution, which admits different solutions for optimal standard and adversarial classifiers. In this work, we focus on the latter case under a binary Gaussian mixture classification problem. Unlike earlier work, we aim to derive the natural accuracy gap between the optimal Bayes and adversarial classifiers, and study the effect of different distributional parameters, namely separation between class centroids, class proportions, and the covariance matrix, on the derived gap. We show that under certain conditions, the natural error of the optimal adversarial classifier, as well as the gap, are locally minimized when classes are balanced, contradicting the performance of the Bayes classifier where perfect balance induces the worst accuracy. Moreover, we show that with an $\ell_\infty$ bounded perturbation and an adversarial budget of $ε$, this gap is $Θ(ε^2)$ for the worst-case parameters, which for suitably small $ε$ indicates the theoretical possibility of achieving robust classifiers with near-perfect accuracy, which is rarely reflected in practical algorithms.
DISTANCE LEARNING TECHNOLOGIES OF POSTGRADUATE DENTAL EDUCATION SYSTEM

The implementation of distance learning is carried out with the help of modern systems of distance education. They allow to teach and to assess the knowledge of interns and doctors quickly and easily, regardless of their location. The aim of the study. A comparative review of the most well-known distance learning platforms, wich are designed to organize the learning process and control learning with the help of Internet technology. System of distance education is a virtual classroom with the possibility to train interns and doctors from different regions of Ukraine at the same time. There are many educational platforms for distance learning nowadays, such as Moodle (Australia), iSpring Learn LMS (Russia), Collaborator (Ukraine), eTutorium LMS (Ukraine), Opigno (Belgium), Atutor (Canada). Moodle is a free platform that allows users to create individual courses. It supports more than 100 languages. iSpring Learn LMS is a simple and user-friendly system that is a paid alternative to Moodle. Collaborator is a platform that works effectively on all modern devices and browsers and is virtually independent of the software of the user's device. eTutorium LMS is a virtual distance learning system that allows to create an online course of any complexity quickly. Opigno is a modern free distance education system based on Drupal (a popular content management system). Atutor, like Moodle, is an open web-based e-learning system. Conclusion. Distance learning systems differ not only functionally, but also in the way they solve problems. The simplicity of use of the platform depends on the degree of its adaptation to the needs of the user and the ability to use all existing features and functions of the system.
Dynamic Resource Provisioning using Cognitive Intelligent Networks based on Stochastic Markov Decision Process

One of the major challenges of the near future will be the ability to identify appropriate methodologies for the transmission of information on the channel’s status and on the active reallocation of system resources among the operating nodes of the network. A feasible way is to build a network of intelligent nodes. Distributed cognitive intelligence must make it possible to configure and adapt nodes so that they can modify their state to maximize an objective function. In this approach, the major problem is how to establish communications between the nodes before they had the opportunity to exchange information on the status of the transmission channel. This condition can be brought back to the estimate of the probability of a future event. Reinforcement Learning (RL) can be considered a possible way to control our system. For obvious reasons our system is stochastic, and we can also assume that the data that each node has on the transmission channel’s state is sufficiently detailed. Markovian Decision Processes (MDP) are perfect for describing such an architecture. This way, we are transforming the investigation for a good control system in the research for a good value function. Many heuristic algorithms can produce excellent results in the field of self-organizing networks (SONs). Keywords Channel, Redundancy, Smart-Environment.