Reviewerly/exHarmony huggingface dataset:
The Information Communication Technology (ICT) Gap and the Generation Gap in Local Government Towards “SMART ASN”

Abstract The role of ICT is very important, especially in the era of data explosion or big data, but the government, especially local governments, finds it difficult to take advantage of it due to the uneven development of ICT. This is very helpful for the government in providing services to the community. Based on this, the researchers are interested in examining how the local government applies the mandate of Government Regulation No. 11/2017 towards “SMART ASN” in the field of ICT and HR? What have local governments done to implement it? The purpose of this study is to determine the implementation of the mandate of Government Regulation No. 11/2017 towards “SMART ASN” in the field of ICT and HR carried out by the local government and know what the local government has been done. This research uses the descriptive qualitative method. To get all the information related to the research problem, the researcher used document study techniques to collect data. Data analysis is done by collecting, compiling, and interpreting the data and then making conclusions. From the data are got that there are still many local governments that have not carried out Government Regulation Number 11 of 2017, such as preparation and determination of needs, career development, and unclear career patterns, making the generation gap increase. There are still provinces that have not been able to fully access ICT, so that why they can’t utilize the big data optimally. The efforts of the government to implement PP No. 11 of 2017 are many local governments have implemented technical education and training organized by technical agencies, the government tries to make the Palapa Ring and merit system.
The Problem of "Stored Energy" – the most Important Point in the Classification of Identification Errors

The problem of establishing dependencies based on empirical data with a random component has been known since the 18th century. In the works of Gauss and Laplace, a parametric class of functions and a functional are introduced, the minimization of which leads to the search for the required function. In the twentieth century, the development of the ideas of cybernetics and the appearance of computers made it possible to approach the solution of the problems of identifying the dynamic properties of objects from the data of their normal operation. However, here it was required to solve a range of systemically related problems. Their modern classification puts the problem of "stored energy" in the first place. The mechanism of occurrence of the corresponding error and the determining factors are considered. Analytical relationships for the resulting error are obtained. Verification was carried out using computer simulation.
Controllable Text-to-Image Generation with Enhanced Text Encoder and Edge-Preserving Embedding

Abstract Text-to-image generation through generative adversarial networks (GANs) has become a popular research topic in the fields of natural language processing and high-quality image synthesizing. By learning latent word features of queries, many state-of-the-art GANs generate images from coarse to fine in a multi-stage trend. While this mechanism is able to synthesize realistic images stage by stage, failures containing semantic mismatches and shape distortions still exist. In this project, we adopted a multi-stage text-to-image GAN as a baseline, and remodeled this structure in two ways: 1) modify the text encoder by pre-processing text queries with pre-trained language models to gain deeply mined text information, and 2) develop an edge-preserving structure to capture feature mismatches, denoted as “edge loss”, between text latent information and generated image edges. Experiments on benchmark dataset demonstrate that models of our method outperform the baseline model and increase Inception Scores by over 30%, and that our approaches are able to effectively generate synthetic images using natural language descriptions.
JOB-DCA: A Cost Minimizing Jaya Optimization-Based Data Center Allocation Policy for IaaS Cloud Model

Cloud computing presents the new era of revolutionary computing of Internet to fulfill the user demands for computing that provides pool of services and resources that are globally distributed in different regions around the globe. The end user demands for the adequate resources are processed on pay-per-use. The end user computing is served through virtual machines (VMs), which are created and allocated at different data centers for the on-demand user request. Allocation of data centers is challenging one for the on-demand request. The data center allocation mechanism must consider different parameters of user data center and network. Most of the works address few of the parameters and applied optimization techniques. However, the implementation of new optimization techniques always opens a scope for the researchers in different fields. In this work, we discussed the Jaya optimization-based data center allocation (JOB-DCA) mechanism for infrastructure as a service (IaaS) cloud. The work considers selected parameters to find the optimized data center list and allocate to the on-demand resources with minimum cost. The presented JOB-DCA allocation technique tries to allocate the preferred data centers with reduced total VM cost. A new cost function is defined to minimize the cost and the existing benchmark cloud simulation tool known as CloudAnalyst is used to analyze the performance of the JOB-DCA technique. The proposed technique is compared with the benchmark mechanisms, and the results show 3.2% minimized cost in the JOB-DCA.
Defending Convolutional Neural Network-Based Object Detectors Against Adversarial Attacks

Artificial intelligence and machine learning are constantly growing in prevalence in everyday affairs, including self-driving cars and facial recognition security systems. Neural networks are at the center of these developments and allow autonomous vehicles or facial recognition systems to “learn” to classify images. As these networks become more commonplace, these networks must be able to address any situation with a minimal chance of failure. While a minor mistake in speech or facial recognition may not be particularly harmful, a missed traffic sign could result in harm, damages, or death. Researchers have developed methods to specifically target and trick neural networks into skipping or misclassifying important objects that it would otherwise detect. One such method is an adversarial attack, which is the emphasis of our study.
Multi-task continuous learning model

Abstract In continual learning, previous learned knowledge tends to be overlapped by the subsequent training tasks. This bottleneck, known as catastrophic forgetting (CF), has recently been relieved between vision tasks involving simple tasks. Nevertheless, the challenge lies in the continuous classification of the sequential sets discriminated by global transformations, such as excessively spatial rotations. Aiming at this, a novel strategy of dynamic memory routing is proposed to dominate the forward paths of capsule network (CapsNet) according to the current input sets. To recall previous knowledge, a binary routing table is maintained among these sequential tasks. Then, an increment procedure of competitive prototype clustering is integrated to update the routing of current task. Moreover, a sparsity measurement is employed to decouple the salient routing among the different learned tasks. The experimental results demonstrate the superiority of the proposed memory network over the state-of-art approaches by the recalling evaluations on extended sets of SVHN, Cifar-100 and CelebA etc.
Gesture Recognition in an IoT Environment: A Machine Learning-Based Prototype

The spread of IoT and wearable devices is bringing out gesture interfaces as a solution for a more natural and immediate human-machine interaction. The “Seamless” project is an industrial research and development experience, aimed to build a virtual environment where data collected by IoT sensors can be navigated through a gesture interface and virtual reality tools. This paper presents the portion of the project concerning gesture analysis, focused on the problem of automatically understanding a set of hand gestures, in order to give commands through a wearable control device. The tackled issue is to build a real time gesture recognition system based on inertial data that can easily adapt to different users and to an extensible set of gestures. This gesture data variability is addressed by means of a supervised Machine Learning approach that allows adapting the system response to different gestures and to various ways of performing them by different people. A context-aware adapter allows interfacing the gesture recognition system to various applications.
Sparse polynomial interpolation based on diversification

We consider the problem of interpolating a sparse multivariate polynomial over a finite field, represented with a black box. Building on the algorithm of Ben-Or and Tiwari (1988) for interpolating polynomials over fields with characteristic zero, we develop a new Monte Carlo algorithm over finite fields by doing additional probes. To interpolate a polynomial $$f \in {\mathbb{F}_q}[{x_1}, \ldots ,{x_n}]$$ with a partial degree bound D and a term bound T, our new algorithm costs $${O^ \sim }(nT{\log ^2}q + nT\sqrt D \log q)$$ bit operations and uses 2(n + 1)T probes to the black box. If q ⩾ O(nT2D), it has constant success rate to return the correct polynomial. Compared with the previous algorithms over general finite field, our algorithm has better complexity in the parameters n, T and D, and is the first one to achieve the complexity of fractional power about D, while keeping linear in n and T. A key technique is a randomization which makes all the coefficients of the unknown polynomial distinguishable, producing a diverse polynomial. This approach, called diversification, was proposed by Giesbrecht and Roche (2011). Our algorithm interpolates each variable independently by using O(T) probes, and then uses the diversification to correlate terms in different images. At last, we get the exponents by solving the discrete logarithms and obtain the coefficients by solving a linear system. We have implemented our algorithm in Maple. Experimental results show that our algorithm can be applied to the sparse polynomials with large degree within a few minutes. We also analyze the success rate of the algorithm.
Economic Conditions and Mechanisms of School Staff Professional Growth

The analytical materials show the relevant conditions and mechanisms of school staff professional growth. Special attention is paid to the issues of teachers' remuneration, including the dynamics of salary in nominal and real terms. Changes in the amount of salary are analyzed, and interregional comparisons are made to assess the degree of their differentiation across the regions of the Russian Federation. The authors evaluate the teachers’ workload, opportunities for professional growth, and available information and methodological resources for their work. Comparison of Federal statistical observation data and the results of sociological surveys conducted by different groups of scientists makes it possible to determine the quality of the created conditions and the reserves for their improvement.- Only 42% of Russians consider the teaching profession prestigious. 39% of teachers are satisfied with their work.- Despite an increase in nominal salary in 2013-2020, there was a decrease in real terms during this period.- Conditions for the development of teachers’ skills are differentiated by region. The percentage of teachers who have completed advanced training and/or professional retraining over the past three years varies from 26.4% to 98.2%.
Mongolian Part-of-Speech Tagging with Neural Networks

The purpose of this paper is to present a simple neural networks model—multilayer perceptron for Mongolian part-of speech tagging. We used about 1400 manually tagged sentences for training and testing from Mongolian Penn Treebank. The performance of the model is 80.78% which we consider a promising result. Also, another contribution of this work is that we make our testing data online for the sake of the development of Mongolian part-of-speech tagging research.
Metric Learning for Multi-label Classification

This paper proposes an approach for multi-label classification based on metric learning. The approach has been designed to deal with general classification problems, without any assumption on the specific kind of data used (images, text, etc.) or semantic meaning assigned to labels (tags, categories, etc.). It is based on clustering and metric learning algorithm aimed at constructing a space capable of facilitating and improving the task of classifiers. The experimental results obtained on public benchmarks of different nature confirm the effectiveness of the proposal.
Exploring Methods of Combining Eye Movement and Facial Expression for Object Selection

Eye movement has been proved feasible and potential for object selection. However, traditional time-threshold-based method (TT) where the user fixates at a target object for a certain duration called “dwell time” to confirm selection is typically slow and uncomfortable. We present and evaluated the methods of combining eye movement and facial expression for object selection. An experiment with 33 subjects was conducted to estimate the performances of TT, smile-triggered method (ST) and frown-triggered method (FT). The results show that ST and FT save time significantly at the same time reduce the accuracy for every selection. Besides, subjective questionnaire show that ST outperforms TT and FT with respect to the comprehensive subjective feelings while selecting. This paper provides novel eye-movement-based methods for quick object selection and indicates the potential of ST and FT to achieve more accurate selection.
Image Denoising Using a Conditional Generative Adversarial Network with Residual Dense Blocks

Image denoising is an important preprocessing step in image analysis. The image denoising models based on deep learning have been recently attracting considerable attention owing to its excellent performance. However, most existing methods are only suitable for certain types of noise, and these methods often cause the missing of detail information in images. In this paper, an image denoising method with generative adversarial network is proposed. In this framework, a conditional GAN with an embedment of residual dense blocks (RDBs) in the generator is used for image denoising, in which the images with some artificial noises are used as condition. Meanwhile, a sum of Wasserstein distance, perceptual loss and reconstruction loss is employed to act as our final objective loss function to train the model. By comparing with several experiments with different denoising models on several datasets, the generated results show that our proposed model can reduce the noise and recover the noise images effectively.
USE OF ASSOCIATION RULES TO PERFORM THE MINING OF A MARKETING DATABASE COMERCIAL

Currently, companies seek to find methods to analyze their customer behaviors and profiles. From this, a case study was carried out in a drugstore, in which the data mining technique and the Apriori algorithm were applied, for a better understanding. of your sales. First, a bibliographic survey on data mining and its tasks was carried out. Soon the sales made and their respective products were examined. Finally, marketing standards were presented according to the data analyzed, in order to assist the organizational management and marketing of the examined company.
FEATURES OF THE USE OF DISTANCE LEARNING TECHNOLOGIES IN THE ORGANIZATION OF THE EDUCATIONAL PROCESS AT THE UNIVERSITY

Подчеркивается необходимость своевременного вычленения и качественного анализа проблем дистанционного обучения, учета предпочтений и интересов обучающихся и преподавателей вуза, консолидации их усилий по преодолению трудностей и решению проблем, связанных с объективными и субъективными ситуациями, особенностями использования технологий дистанционного обучения при организации образовательного процесса в университете. Описаны особенности использования дистанционной формы организации обучения в вузе. Приведены данные, полученные при исследовании (опросе) влияния применения технологий дистанционного обучения на здоровье студентов 1–5-го курсов очной и заочной формы обучения направления подготовки 44.03.05 Педагогическое образование (с двумя профилями подготовки) Института психологии и педагогики Елецкого государственного университета им. И. А. Бунина. The article describes the need for timely identification and qualitative analysis of the problems of distance learning, taking into account the preferences and interests of students and university teachers, consolidating their efforts to overcome difficulties and solve problems related to objective and subjective situations, the peculiarities of using distance learning technologies in the organization of the educational process at the university. In the current situation, burdened by the spread of a new coronavirus acute respiratory infection, which is caused by the coronavirus SARS-CoV-2 (2019-nCoV), the world education system (including higher education) is forced to turn to distance learning technologies. These technologies allow you to work remotely, maintaining the necessary distance, protecting the health of people (both students, teachers, and employees of higher educational institutions), excluding their close interaction during training, scientific research, in the admission campaign, in extracurricular work, etc. The authors describe the features of using the remote form of organizing training at the university so that it contributes to the maximum saving of students ‘ health, has a positive effect on their physical and mental state; minimizes the viral load in a difficult epidemiological situation; increases and develops digital literacy of students; “opens” new opportunities for remote work for the teaching staff. The article provides the data obtained in the study (survey) of the impact of the use of distance learning technologies on the health of the full-time and part-time students of the 1st – 5th year of studies of the area of training 44.03.05 Pedagogical education (with two training profiles) of the Institute of Psychology and Pedagogy Bunin Yelets State University.
Implementasi Algoritma K-Means Dalam Pengelompokan Penyandang Disabilitas Menurut Kecamatan Kabupaten Simalungun

A person with a disability is someone who has physical or mental limitations that can interfere and is an obstacle for him to carry out normal daily activities. People with special needs (disabilities) are people who live with special characteristics and have differences with people in general. This study aims to classify areas of people with disabilities based on districts. In this study using data mining techniques with the k-Means clustering method. The k-means method is a clustering method that functions to break the dataset into groups. The data source of this research was obtained from the Central Statistics Agency (BPS) of Simalungun Regency. Data entered is data on the number of persons with disabilities in 2008-2017 consisting of 31 sub-districts divided into 3 clusters: high, medium and low clusters. From the k-means calculation results, 8 sub-districts as high clusters were obtained, 15 sub-districts as medium clusters and 8 sub-districts as low clusters. The implementation process using the RapidMiner 5.3 application is used to help find accurate values.
A Multimodal Fusion Model Based on Hybrid Attention Mechanism for Gesture Recognition

Gesture recognition based on multimodal information plays a significant role in the field of human-computer interaction. In recent years, although many researchers devoted themselves to the related work in this field, the correlation and complementarity of multimodal information have not been explored and utilized fully. Consequently, this paper proposes a multimodal fusion network based on the hybrid attention mechanism for gesture recognition, where: 1. the cross-attention mechanism is introduced to fuse and enhance multi-dimensional features mutually, such as video and audio features; 2. the single-attention mechanism is employed to balance the correlation and redundancy between one-dimensional representation and multi-dimensional representation, such as skeleton and video features. The proposed network aims to excavate the relationship between modalities from different perspectives, fuse various information in different fusion stages, and achieve high accuracy of recognition. The method is evaluated on the publicly available datasets, ChaLearn Montalbano dataset, and obtains 95.97% accuracy when fusing video, skeleton, and audio modalities, which outperforms state-of-the-art approaches.
Study of 2D SubMarine Tracking with Complete Worked Out Example Based on Kalman Filter

In many applications such as maritime surveillance and autonomous derivation, specific target tracking has become extremely important, and the characteristics of the research target will come from various distributed sources in different spaces and their noise measurements. We provide a new idea based on mobile submarine prediction data and ion pair measurement in this study, using Kalman filter based on accurate two-dimensional to approximate the results. However, we proposed to minimize the error covariance to calculate the total uncertainty measurement using the gain matrix K of Kalman filter. The actual measurement ix(k) of Kalman filter will be incorporated into each previously estimated sampling time. According to the correction equation to further improves the estimated value. The research results can be ground, the method can quickly converge to the true value, and its accuracy is better than the current passive omnidirectional sonar buoy submarine. This is an important result of the research.
Safer-Driving: Application of Deep Transfer Learning to Build Intelligent Transportation Systems

Deep learning methods are widely used in the field of transportation as well as in many areas. These days, deep learning methods produce very successful results thanks to big data, where data collection and storage are much easier than in past years. However, it can be quite difficult to collect data or find the right data in some cases. On the other hand, even if the desired data is collected enough, training deep neural networks to learn from these data may not be comfortable in terms of hardware needed for ordinary users. In these cases, transfer learning can be an exit point, which is very popular in deep understanding, especially when the amount of data is limited. In this study, we have aimed to give comprehensive guidance on using a transfer learning strategy to create a detection model to establish a safer transportation system. More specifically, we have developed a classification model to alleviate accidents due to deer on the road. We have implemented VGG-16 convolutional neural network architecture as a part of the structured transfer learning framework. Computational results show that high accuracy performance can be achieved by implementing VGG-16 with the respectively small size of a dataset.