Reviewerly/exHarmony huggingface dataset:
Development of a tool for determining the authenticity of signature using the raspberry pi board

Abstract Determining the authenticity of signatures is an important task in office / company administration because there are many cases of counterfeiting. The purpose of this study is to develop a tool for determine the authenticity of signature based. The device consists of three main parts, namely a camera module for reading signature images, a raspberry pi board for processing signature images; and an LCD module for displaying the results of signature authenticity detection. This study proposes a simple algorithm using the binary image method and canny edge detection to change images and compare pixel values between reference signature images and doubtful signature images. This process is done by comparing pixels with the same position from different signature images. The comparison results will base for the tool to determine the authenticity of signatures. The result is displayed on the LCD screen with the name of the owner of the signature obtained from the tool database. This system can store five signatures that serve as reference signatures and has six possible final decisions in determining the authenticity of signatures. The results showed that the system can determine the authenticity of signatures with a success rate of 82% from testing of ten different signature images.
Machine Learning-Based Approach for Opinion Mining and Sentiment Polarity Estimation

Opinions of others may be essential in making decisions or selecting from a variety of alternatives. Review of customer feedback helps to improve sales and eventually benefits the company. Most online businesses use recommendation systems which use data mining and machine learning algorithms to find the right product for the right customer at the right time to increase customer satisfaction. This study illustrates how to increase the quality of the product selection process for customers by reducing information overloading and complexity. The goal of this study is to propose a novel product ranking model considering user reviews which enable multiuser recommendation. Dataset was taken through some different supervised learning methods and the best accurate algorithm was proposed. Values are predicted considering positivity and negativity of reviews for a particular product using the proposed algorithm. Products are ranked according to the given value. New recommendation model and its workflow are illustrated here.
Ševkušić-Malinić, Slavica; Dušica-Teodorović, Jelena (Eds.): Leadership in Education. Initiatives and trends in selected European countries.

The EdLead project included several knowledge-generating events and activities.One was an international scientific conference entitled 'Challenges and dilemmas of professional development of teachers and leaders in education' , organised in Belgrade in 2015.This event
SIKADES: Empowerment-digitalism-based start-up to advance peace and order in Indonesia

Abstract The security is an important factor in investment, so it is not surprising for regions with a high level of security coupled with high investment. Indonesia as a large country spread over 34 provinces, the level of security of each region is also varies, but there are still many areas that have a high crime rate. Various factors that trigger high crime include high poverty, unemployment, education, and lifestyle. For this reason, an effective and efficient formula for enhancing peace, public order, and community protection starts from the lowest level, namely Village. The model used is Sikades, a security model based on the empowerment of criminals, recidivists, village thugs and unemployed who have been empowered to become Village Civil Service Police Personnel (From Zero to Hero) and integrated with online-based applications that help personnel and the community to give and receive reports on the current security situation in all villages in the district. This model provides a sense of security and comfort for the community.
The algorithm for definition of connective elements between phrases in the sequence of text statements

In the article the basic procedures for finding of connective elements and resolving conflicts of references is analyzed. On the basis of this, a generalized algorithm is proposed that combines advantages of existing procedures for search for connective elements between phrases. The advantages of the selected procedures and their sequence are described, the formal description of input data and the results of the algorithm are presented. To optimize the procedure for scanning the text, the algorithm is performed as an iterative reduction of the candidates for communication elements. This is achieved through the confirmation of non-conflict links and the gradual resolution of conflicts.
Integrated Simulation Environment for Investigation of Multi-Agent Systems in Smart Grids Applications

This paper presents a framework for simulation environment for modelling and testing smart grids controlled by multi agent systems. The environment is based on the co-simulation approach and consists of MATLAB/Simulink, where the power grid part is modelled, Java Agent DEvelopment framework, where the multi-agent system is programmed, and the interface between these two based on TCP/IP communication. The interface is designed to be general and it can be used also for purposes other than power systems application. Structure, integration within both ends, and functioning of the interface is the main focus of this paper.
Discriminant projection method based on optimized neighborhood graph for image feature extraction

The image data collected in reality often has high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. Based on the above considerations, this paper proposes discriminant projection method based on optimized neighborhood graph model based on graph optimization. The model has the following characteristics: (1) Graph constraint is introduced to maintain the local geometric structure of the data while using the label information and global information. (2) The $L_{2,1}$ norm is used to redefine LDA, and the scale factor is introduced, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in 2 image datasets.
ON SPIRITUAL AND MORAL ASPECTS OF TEACHING PHILOSOPHY IN HIGHER EDUCATION INSTITUTIONS

This scientific article deals with the spiritual and moral aspects of teaching philosophy in higher educational institutions. The author identifies and analyzes the main problems most frequently encountered in the process of teaching philosophy. The author drew attention to key issues related to the formation of a worldview, spiritual and moral guidelines and the education of the student's personality. In the course of the research, the author analyzed the spiritual and moral aspects with the involvement of a personal theoretical and practical arsenal and experience of teaching philosophy in higher educational institutions. In this regard, the theoretical and practical significance of scientific research consists in identifying general trends and spiritual and moral aspects of teaching philosophy. In the process of studying this problem, the author used general logical methods of theoretical knowledge and the following methods of scientific research: analysis and synthesis, comparative analysis, generalization, analogy and a systematic approach. The author focused the main emphasis in the research on classical philosophical works, on modern studies of philosophers, as well as socio-philosophical, pedagogical research in the field of teaching philosophy. An important point of this scientific research is the question of the spiritual and moral significance and the meaning of teaching philosophy in higher educational institutions. The spiritual and moral aspects of teaching philosophy identified, proposed and tested by the author can help to increase students' interest in the study of philosophy.
Quantitative Associative Classification Based on Kernel Mean Embedding

Quantitative associative classification (QAC) is favored for its strong explainability and satisfying predictive ability. However, existing QAC methods can't directly classify quantitative dataset basically, instead they work by partitioning quantitative data into a number of categories and then classify them, which cause information loss. Here, we propose a new QAC method which can directly estimate the probability of association rules using kernel mean embedding technology so that it can avoid the biases induced by partitioning and obtain competitive classification accuracy. Specifically, we implement an Apriori-like association rule discovery process and estimate the posterior probability of data by Bayes' rule with the terms being computed in a kernel mean embedding pattern. To verify our method, we take experiments on several data sets, and the results demonstrate that the method performs better than SVM, decision tree and partitioning Apriori algorithm. We also test the effect of the method's super-parameters including minimal support, minimal confidence and maximal attribute set size, and give an example of quantitative association rule set.
Machine Learning Model for Anomaly Detection in Big Data for Health Care Applications

Recently, enormous amounts of data are increased by the essentials of data security and investigation for big data. Anomaly detection system monitors the data to analyze and detect any intrusion/misbehave in the network or devices. With the traditional detection techniques, it is highly complex to perform analysis and detection process. Techniques for big data are widely incorporated for anomaly detection system in achieving efficient data analysis. Hence, it is attempted to introduce fuzzy logic-based anomaly detection. The efficiency of the result has been obtained with greater accuracy with high performance with the added advantage of reduced training time.
Residual Attention Network vs Real Attention on Aesthetic Assessment

Photo aesthetics assessment is a challenging problem. Deep Convolutional Neural Network (CNN)-based algorithms have achieved promising results for aesthetics assessment in recent times. Lately, few efficient and effective attention-based CNN architectures are proposed that improve learning efficiency by adaptively adjusts the weight of each patch during the training process. In this paper, we investigate how real human attention affects instead of CNN-based synthetic attention network architecture in image aesthetic assessment. A dataset consists of a large number of images along with eye-tracking information has been developed using an eye-tracking device ( https://www.tobii.com/group/about/this-is-eye-tracking/ ) power by sensor technology for our research, and it will be the first study of its kind in image aesthetic assessment. We adopted a Residual Attention Network and ResNet architectures which achieve state-of-the-art performance image recognition tasks on benchmark datasets. We report our findings on photo aesthetics assessment with two sets of datasets consist of original images and images with masked attention patches, which demonstrates higher accuracy when compared to the state-of-the-art methods.
Topic Evolution of Library and Information Science Studies in the Last Twenty Years: A Co-Word Analysis

Abstract Background and Objectives: Topic evolutions of scientific and academic disciplines can be clarified by drawing scientific maps and identifies emerging or developed topics of scientific disciplines, sub-topics and the relationship between different topics of a discipline. The purpose of this study is to draw a strategic diagram to analyze the developments of the last twenty years of library and information science field. Material and Methods: This was a Scientometrics study with co-occurrence analysis of words that was conducted on ten journals with the highest impact factor in the field of library and information science. Twenty years (1998-2017) publications were extracted from LISTA database and imported into SciMAT software. After preparing the data, all publications were divided into four time periods, strategic diagrams of each period were drawn and topic clusters were analyzed. Results: The largest clusters of the first two periods were "Information Retrieval" and "Bibliometrics", which in the next period "Citation-Analysis" appeared instead of "Information Retrieval" cluster, but nevertheless the largest node of this cluster was "Information Retrieval". These clusters were the most developed topics in the field of library and information science. Conclusion: Information retrieval and bibliometrics are at the forefront of library and information science. Sentiment analysis and information literacy with a cognitive approach are emerging topics in the field. Also, studies related to information production and related indicators have led to qualitative research.
Utilization of solar power for support facilities in the evacuation area during natural disaster

Abstract Products with the use of solar power in evacuation activities and displacement of natural disasters are very rarely found. Solar cell products are usually used by people on a certain scale, for example in accessories or lifestyle products. However, technology using solar panels is less developed, especially in products whose orientation is for natural disasters. Many cases were found during the evacuation of natural disasters, including many constraints Electricity is often a problem in areas where refugees are displaced after natural disasters. The problem of wasteful diesel generator energy resulted in the absence of an effective source of electricity and lighting, besides that information or distribution of the area was still fairly slow due to the portable speaker system and even door-to-door announcements. The method used is by combining aspects of technology into a conventional product by providing features needed in the event of a disaster. The method approach using the X-Problem method consists of immersion, convergence, divergence, and product adaptation designed with an effective and efficient function focus approach. From these problems, an emergency lighting product and emergency wireless loudspeaker can be designed to be provided in each of the evacuation tents. It is expected that the design of the product designed has a very important role in the evacuation process that occurs in the disaster environment, especially in Indonesia. The role of the product in creating an evacuation environment situation is more conducive.
Privacy and Data Balkanization: Circumventing the Barriers

The rapid growth in digital data forms the basis for a wide range of new services and research, e.g, large-scale medical studies. At the same time, increasingly restrictive privacy concerns and laws are leading to significant overhead in arranging for sharing or combining different data sets to obtain these benefits. For new applications, where the benefit of combined data is not yet clear, this overhead can inhibit organizations from even trying to determine whether they can mutually benefit from sharing their data. In this paper, we discuss techniques to overcome this difficulty by employing private information transfer to determine whether there is a benefit from sharing data, and whether there is room to negotiate acceptable prices. These techniques involve cryptographic protocols. While currently considered secure, these protocols are potentially vulnerable to the development of quantum technology, particularly for ensuring privacy over significant periods of time into the future. To mitigate this concern, we describe how developments in practical quantum technology can improve the security of these protocols.
Using Machine Learning Methods to Estimate the Cost of Housing

Introduction. Nowadays, the state has enshrined at the legislative level the definition of appraised value for tax purposes in sales of real estate as mandatory. The comparative approach most often used by appraisers has disadvantages such as the inability to find analogues in some cases and the need to make corrections, which affects the reliability of the results. The module of electronic determination of appraisal value (Module) similar to the object of property appraisal of the Unified database of appraisal reports works on the same approach and quite often overestimates appraisal value that leads to increase in the size of the tax during sales as the real estate cannot be sold for the price less than the estimated cost. Today to determine the price of an automated system correctly, it is necessary to fill the Unified Valuation Database in the State Property Fund with large knowledge bases - a huge IT system. So far, the thoughtless machine still determines the price by the average value. Currently there are often situations when the appraised value of real estate, determined by the Module, exceeds its real market value. Given that the approach used by the Valuation Module does not always give the correct result, there is a need to find a better method to determine the value of housing that could be used by the Module. The purpose of the paper. In this paper, an approach based on fuzzy logic was used to estimate the cost of housing in Kyiv. Fuzzy methods allow to apply a linguistic description of complex processes, to establish fuzzy relationships between concepts, to predict the behavior of the system, to create a set of alternative actions, to formally describe fuzzy decision-making rules. Results. The software implementation of the model in Python programming language was performed. Data for modeling were taken for the period July – October 2020 from a single database of property valuation reports. The sample contained 2133 records, it was filtered, divided into training and testing in the proportion of 85 : 15. To assess the quality of the program, the average relative error of the developed model was calculated. Keywords: fuzzy logic, machine learning, Python programming, linguistic variables, predictive model.
Single Image Super-Resolution Based on Generative Adversarial Networks

Traditional super-resolution algorithms are computationally intensive and the quality of generated images is not high. In recent years, due to the superiority of GAN in generating image details, it has begun to attract researchers' attention. This paper proposes a new GAN-based image super-resolution algorithm, which solves the problems of the current GAN-based super-resolution algorithm that the generated image quality is not high and the multi-scale feature information is not processed enough. In the proposed algorithm, a densely connected dilated convolution network module with different dilation rate is added to enhance the network's ability to generate image features at different scale levels; a channel attention mechanism is introduced in the network to adaptively select the generated image features, which improves the quality of the generated image on the network. After conducting experiments on classic test datasets, the proposed algorithm has improved PSNR and SSIM compared to ESRGAN.
A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function

Optimization methods are frequently applied to solve real-world problems such, engineering design, computer science, and computational chemistry.This paper aims to compare gradient-based algorithms and the meta-heuristic particle swarm optimization to minimize the multidimensional benchmark Griewank function, a multimodal function with widespread local minima.Several approaches of gradient-based methods such as steepest descent, conjugate gradient with Fletcher-Reeves and Polak-Ribiere formulations, and quasi-Newton Davidon-Fletcher-Powell approach were compared.The results presented showed that the meta-heuristic method is recommended for function with this behavior because is no needed prior information of the search space.The performance comparison includes computation time and convergence of global and local optimum.
Application of Data Mining Using the K-Means Algorithm in Rural and Urban Land and Building Tax (PBB-P2) Receivables Data in Bantul Regency

Abstract The rural and urban land and building tax (PBB-P2) receivable amount in Bantul is quite large. As of December 31, 2019, there were 3,344,145 PBB-P2 objects, worth for IDR 114,984,991,600. This number tends to increase from year to year, showing that PBB-P2 receivable collection process has not optimal. This study discusses data mining applications for data management using the K-Means clustering method. This paper uses the PBB-P2 existing receivable data, given by the Directorate General of Taxation before PBB-P2 becomes local taxes. The data is between the years of 1994 and 2012. Data is grouped based on the village area and the category of the receivables number. We cluster it into three types, namely the high, medium, and low accounts receivable clusters. Data mining is expected to improve the PBB-P2 receivable data management in the Bantul Regency to make better decision-making. This study’s results make it easier to analyze PBB-P2 receivable data pattern based on the grouping of village areas and the receivable number category. The analysis results are expected to provide input for BKAD Bantul to identify certain villages and categories of receivables that need more attention in the PBB-P2 collecting process in the Bantul Regency.
RNN Based Incremental Online Spoken Language Understanding

Spoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion, i.e., the NLU often has to wait for the ASR to finish processing on an utterance basis, potentially leading to high latencies that render the spoken interaction less natural. In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems. A lexical End-of-Sentence (EOS) detector is proposed for segmenting the stream of transcript into sentences for intent classification. Intent detection experiments are conducted on benchmark ATIS, Snips and Facebook's multilingual task oriented dialog datasets modified to emulate a continuous incremental stream of words with no utterance demarcation. We also analyze the prospects of early intent detection, before EOS, with our proposed system.
The Influence of Didactic Play on Reducing the Level of Anxiety in the Classroom

The article highlights the issues of the teacher's use of didactic games as a form of teaching organization and an effective means of preventing and reducing anxiety among younger students in the process of educational activities in the classroom. The concept of "school anxiety" is characterized. The importance of didactic games for the development of children’s ability and desire to learn, increasing their intellectual activity, enriching the experience of communicative interaction with classmates, forming a sense of camaraderie and responsibility for the team result is considered.&#x0D; The possibility of conducting educational games in the development of any subject content has been substantiated. Examples of didactic games for working in mathematics lessons with first-graders showing anxiety are given.