Reviewerly/exHarmony huggingface dataset:
Position of social sciences and humanities within the scientific disciplines and its measurement through CRIS system

The information system for research, development and innovation SK CRIS is the main tool for mapping Slovak science.The data on research were appropriate for the analysis at the time they were structured, on the national level, of high quality, consistency and covering as many components as possible and in mutual relations.The SK CRIS functionality brings the ability to offer aggregated information from these data and to present them according to various aspects.It is allowed by the implementation of the crucial standard for CRIS systems, supported by European Commission.Use of data format CERIF which mutual relations between objects and semantics allow record the objects with mutual ties.Our ambition is to demonstrate the possibility of using the SK CRIS data.In the light of the increasing importance of the societal impact of research, this article attempts to address the question as to how Social Sciences and Humanities (SSH) research outputs from 2019 are represented in Slovak research portfolios.The analysis prompts the conclusion that Social Sciences and Humanities research outputs in Slovakia in 2019 are appropriately represented.This can be documented by the proportion represented by the SSH research projects and involved organisations and researchers.
The Importance of SCADA Systems Educational Programs in Higher Education from a Cybersecurity Perspective

Nowadays manufacturing organizations are rapidly transforming to smart factories, in the context of a new stage of industry development known as Industry 4.0.While this new phase in the production and manufacturing sector is booming, associated technologies promise multiple benefits and opportunities across all major market sectors.From the Internet of Things to cloud-based design systems, implementing virtual technologies in the manufacturing area, not to mention critical industries, represents a great challenge in terms of cybersecurity and data privacy issues.In order to avoid major roadblocks in adopting Industry 4.0 technologies, SCADA systems had been developed and represent a hybrid system that focuses on traditional cybersecurity issues, plus Industry 4.0 own unique security and privacy challenges in terms of a more digitalized industrial sector.Taking into consideration the success that SCADA technologies have in digitalizing manufacturing industries as well as critical sectors like energetic production, transportation or healthcare, implementing and monitoring such a system, especially when interfaced with internet communication technologies, calls for special trained human resources.In this regard, the present paper provides an overview on the importance of SCADA systems within Industry 4.0, while highlighting different university specialization approaches.Modern cybersecurity curricula must be updated in order to sustain a deeper specialization that is required for understanding and developing SCADA systems, mainly by creating laboratories where students as future specialist can make test, develop new solutions and understand different technologies linked to different manufacturing sectors.Thus, the current paper provides a comparative presentation of different approaches that universities can apply in order to develop SCADA research laboratories, by incorporating efficient virtual techniques and technologies and build a more realistic and attractive learning platform, in terms of cost efficiency for student and engineering education.
THE EFFECT OF JOB DESCRIPTION AND COMPENSATION ON JOB SATISFACTION OF HEAD OFFICE EMPLOYEES CV CALYSTA PRIMA AESTHETIC

This study aims to determine the description of the job description and compensation and job satisfaction as well as the magnitude of the effect, either partially or simultaneously on the employees of the CV Calysta Prima Aesthetic center. The method used in this research is descriptive method and verification method. The population in this study were employees of the CV Calysta Prima Aesthetic center, with a total sample of 39 people with saturated sampling technique. Based on the results of the study, it was found that the job description and compensation were in the good category, while job satisfaction was in the medium/high enough category. Based on the results of the study, it was obtained that the job description partially gave an effect of 81.2%. The effect of compensation partially gives an effect of 84.9% on employee job satisfaction. The effect of job description and compensation simultaneously gives an effect of 86.9% on employee job satisfaction.
The Role of Parents and Manipulative Media in Introduction to Numbers in Early Childhood

ABSTRACT The concept of Numbers needs to be introduced in early childhood to help the growth and development of children in order to have readiness to enter further education. Introducing numbers in early childhood development through the role of parents and playing activities in everyday life and are natural. The purpose of this study was to analyze the influence of the role of parents and the use of manipulative media on the ability to recognize numbers in early childhood. This research was conducted in TK YKK 2 and TK Alfina and TK Al Ikhwan. The number of research samples was 109 children. The sampling technique used random sampling and cross-sectional research design. The independent variable in this study is the role of parents and the use of manipulative media, while the dependent variable is the ability to recognize the concept of numbers in early childhood. Statistical test using Rank Spearman and Logistic Regression Test. The results showed that the role of parents and the use of manipulative media together can improve the ability to recognize the concept of numbers in early childhood. The role of parents in guiding, motivating, and facilitating early childhood learning can improve children's ability to recognize numbers. The use of appropriate and interesting learning media can improve children's thinking patterns in the concept of numbers. Key words: Role of Parents; Manipulative Media; Introduction to Numbers
Privacy Protection Against Insider Attacks

A growing number of public and private sector organizations are recognizing insider threats as a critical area. In response, many steps are taken to defend assets against risks posed by employees and third-party trust. Insiders pose unique challenges for defenders. Traditional security tools are unlikely to audit insiders, let alone privileged users who have a potentially malicious intent. Although a high-risk activity, it is common to see users sharing passwords between colleagues or subordinates, defeating the purpose of authentication. This increases chances of Insider Attacks (IA), as it is hard to identify malicious insiders, given an attacker is entrusted with highly privileged access to read and write operations. Information Technology Organizations employ many workers with varying level of access, and every user is authenticated with unique login credenti¬als. Controls need to be put in place in order to secure the systems, since it can hamper login patterns. Research indicates that by analysis of system calls (SCs) that are generated upon user login can detect intrusions and read such patterns that are against the normal operations of the system. Information Technology Organizations employ many workers with varying level of access, and no two users have same login behavior. Given every user has a unique login pattern, this work proposes a system called Privacy Protection Against Insider Attacks (PPIA) which learns the login pattern of each user that is authenticated and employs data mining concepts to read user behavior and endeavors to detect insider attacks .Experimental results indicate that the approach is very effective and accurate..
Analysis and prediction of breast cancer through feature selection and classification techniques

In this modern era, rapid research is being conducted in the field of medical sciences, with datasets of patients regarding their symptoms and their corresponding disease being readily available to...
Deep learning to ternary hash codes by continuation

Recently, ithas been observed that { 0 , ± 1 } -ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform { − 1 , 1 } -binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.
A Comparative Study of Variables Related to Students’ Success in the 2015 International TIMSS Test

Objective: This study aimed to investigate the factors affecting students' academic achievement based on Thames international test data. Methods: The method of this study is quantitative comparative. The statistical sample of this study consists of eighth-grade students from 57 countries who participated in the 2015 Thames International Test. The data set was analyzed using the fuzzy logic approach. Results: The necessary and sufficient individual conditions showed that the conditions of the relationship between home and school, school social atmosphere, students' attitudes, and educational activities in the classroom are each a necessary condition, and family background is a sufficient condition for achievement (academic achievement). Conclusion: In the causal and combined causes, among the many causal paths, only one causal path based on theoretical and experimental adequacy criteria (coverage and adaptation coefficient) was of theoretical and experimental importance was necessary. In this causal path, family background and the relationship between home and school in combination provided a sufficient turning point in the occurrence of the desired result (academic success).
Modeling of Sea Surface Temperature through Fitting Linear Model with Interaction

Sea surface temperature (SST) is one of the attributes of the world climate system and global warming. The relationship between SST and other climate parameters can be represented in a linearity approach. Through this approach, SST variability shows monthly and yearly effects. Information on these two time effects is important for knowing the period of peak effect as well as other statistical measures in the linear fitting model. The models used include transformation and without covariate transformation, interaction and without covariate interaction, and with centering and with the addition of time covariates in the model. The linear fitting model chosen as the basis for construction is a model with a combination effect of covariate interaction and transformation giving an increase in the magnitude of multiple R2 (56.62%) and adjusted R2 (56.13%) respectively 0.31% and 0.43%. This indicates that the time covariate has a very strong significant effect on the model compared to the continuous covariate. In general, the model has a statistical significance of p-value &lt; 2.2e-16, as well as for the time covariate. However, because the model has an autocorrelation and a large AIC value, this effect is removed by means of an autoregressive moving average. The obtained linear fitting model for SST data is the model with AIC 403.2987.
Using Somatic, Auditory, Visual, and Intellectual (SAVI) technique to improve students’ speaking

The objective of this experimental study is to investigate the effect of using Somatic, Auditory, Visual, and Intellectual (SAVI) technique on students’ speaking at SMAN Model 12 Banda Aceh. Furthermore, it is also to investigate the students’ responses toward the implementation of SAVI technique in teaching speaking at SMAN Model 12 Banda Aceh. Two classes (respectively 33 students) are chosen as the sample. The students in experimental class were taught by using SAVI technique and the students in control class were taught without using SAVI technique. The data about the students’ ability were collected through giving test. Meanwhile, the students’ responses toward teaching speaking by using SAVI were collected by giving questionnaires. The data from test were analyzed by using SPSS version 23. Meanwhile the data from questionnaires were analyzed by using percentage formula suggested by Sugiyono (2015). The finding showed that using teaching speaking by using SAVI technique significantly improved students’ speaking ability, especially their speaking sub-skills. The study proved the improvement by the result of z-test in which the z-score for post-test result of experimental and control class is 3.73. It was found against the hypothesis that there is a significant difference in speaking achievement between the students who were taught by using SAVI technique and those who were taught without using SAVI technique at second year students of SMA 12 Banda Aceh.
Question Expansion in a Question-Answering System in a Closed-Domain System

A great challenge in Information Retrieval Systems (IRS) is to extract the information intention of the user from a command line interface query, so it can recover relevant documents.This problem gets worse in Question-Answering Systems (QAS) in a Closed Domain, for in this scenario, there's a higher divergence between the open language available for the user to elaborate questions and the limited vocabulary in the document collection available in the system (which is usually small).This work proposes and evaluates a system of Query Expansion (QE) for a closed domain QAS based on the semantic similarity between terms of the Word Net and a previously built semantic model using the system's knowledge base.The tests are made by answering questions about the two closed collections of documents showed this method is effective in improving performance of the Closed Domain QAS.
Performance Enhancement in Multimodal Biometrics for Authentication

The biometric system used for person identification is based on the physical modality such as fingerprint, iris, face, etc for providing security to the information.Normally, when a single modality is used for authentication or for encryption there might be a security issue if the hacker or intruder identifies the single modality.To overcome this issue, the proposed work uses multimodal system which combines fingerprint and iris biometrics for improving the security of the information.This Multimodal Biometrics in Information Security is done by extracting the feature points from the fingerprint and iris of the individual, followed by performing fusion of the biometric features and encrypting the fused matrix using Advanced Encryption Standards (AES) which could be used as a key for the authentication of an individual.In this paper, a new fusion technique namely modified-Canonical Correlation Analysis (m-CCA) has been proposed for fusion.The proposed method's performance is evaluated by constructing the confusion matrix and extracting the Genuine Acceptance Rate (GAR), which is observed to be performing better.
Modified Cosine Similarity Measure based Data Classification in Data Mining

Text data analytics became an integral part of World Wide Web data management and Internet based applications rapidly growing all over the world. E-commerce applications are growing exponentially in the business field and the competitors in the E-commerce are gradually increasing many machine learning techniques for predicting business related operations with the aim of increasing the product sales to the greater extent. Usage of similarity measures is inevitable in modern day to day real applications. Cosine similarity plays a dominant role in text data mining applications such as text classification, clustering, querying, and searching and so on. A modified clustering based cosine similarity measure called MCS is proposed in this paper for data classification. The proposed method is experimentally verified by employing many UCI machine learning datasets involving categorical attributes. The proposed method is superior in producing more accurate classification results in majority of experiments conducted on the UCI machine learning datasets.
Troubling the impact of food future imaginaries

Global scale transformation is urgently required if we hope to stabilise socio-ecological systems.While design contributes to social and ecological un-sustainability, it can also play a pivotal role in bringing us towards more positive, inclusive ways of living and being within the planetary ecosystem.Experimental, co-creative design provides powerful tools for prompting critical thinking and inspiring new imaginaries.We engage with these possibilities, and explore their role in societal transition.We present an experimental food design workshop that aims to engender fantastical and plausible possibilities for regenerative (more-than-human) future food practices.We reflect on how to move from such imaginaries to 'implementable nows' that is, transformative innovations that might be enacted today.We provide inspiration and methodological guidance for designers interested in the social imaginaries brought forth through world-making efforts; leapfrogging the adjacent possible and reorienting situated practices towards better -socio-ecologically just -futures.We present a two-part workshop that engages the methods and techniques of experimental food design
CONCEPT OF SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUES

Sentiment analysis is used to conclude the approach of a consumer with respect to some topic. Sentimental analysis, a sub discipline within data mining and computational linguistics, refers to the methodology for mining, understanding the opinions expressed by the consumer in various forms like forums, forms blogs etc. The goal of sentiment analysis is to identify emotional states in online text. We Know human’s learns from past knowledge and machines follows instructions given by humans. But what if humans can prepare the machines from the past data and to put output to work much faster well that what is machine learning is it’s not about learning it’s also about understanding. So we will learn about analysis of sentiments using machine learning techniques
The effect of the Corona pandemic on the educational process from the point of view of public education students in the Kingdom of Saudi Arabia.

This study aimed to explore the impact of Covid 19 pandemic on the educational process in public education schools in the Kingdom of Saudi Arabia through three main aspects: The first aspect: the availability of distance learning resources and facilities for public education students during this pandemic, the second aspect: the capabilities and of public education students to use distance learning resources during the Corona pandemic, the third aspect: the extent to which the practice of educational activities has been affected by distance learning during the Corona pandemic. The study used quantitative methodology and the descriptive survey approach in order to explore theses aspects. This approach depends entirely on distributing the questionnaires electronically to the participants. A questionnaire tool was used in order to collect the study data. The results of this study yielded several results: Regarding the availability of learning resources for students during this pandemic, it was found that they are generally available to all students. As for the capabilities and abilities of general education students to use learning resources, it is clear that most students were able to use these resources effectively. Finally, regarding the extent of how the educational activities affected during this pandemic, the results showed that they were not affected in general and that there was a positive impact on educational activities during distance learning, however, this positive impact not as expected.
Team Correlated Equilibria in Zero-Sum Extensive-Form Games via Tree Decompositions

Despite the many recent practical and theoretical breakthroughs in computational game theory, equilibrium finding in extensive-form team games remains a significant challenge. While NP-hard in the worst case, there are provably efficient algorithms for certain families of team game. In particular, if the game has common external information, also known as A-loss recall---informally, actions played by non-team members (i.e., the opposing team or nature) are either unknown to the entire team, or common knowledge within the team---then polynomial-time algorithms exist. In this paper, we devise a completely new algorithm for solving team games. It uses a tree decomposition of the constraint system representing each team's strategy to reduce the number and degree of constraints required for correctness (tightness of the mathematical program). Our approach has the bags of the tree decomposition correspond to team-public states---that is, minimal sets of nodes (that is, states of the team) such that, upon reaching the set, it is common knowledge among the players on the team that the set has been reached. Our algorithm reduces the problem of solving team games to a linear program with at most O(NW^(w+1)) nonzero entries in the constraint matrix, where N is the size of the game tree, w is a parameter that depends on the amount of uncommon external information, and W is the treewidth of the tree decomposition. In public-action games, our program size is bounded by the tighter 2^(O(nt))N for teams of n players with t types each. Our algorithm is based on a new way to write a custom, concise tree decomposition, and its fast run time does not assume that the decomposition has small treewidth. Since our algorithm describes the polytope of correlated strategies directly, we get equilibrium finding in correlated strategies for free---instead of, say, having to run a double oracle algorithm. We show via experiments on a standard suite of games that our algorithm achieves state-of-the-art performance on all benchmark game classes except one. We also present, to our knowledge, the first experiments for this setting where both teams have more than one member.
Diagnostic Systems for Breast Cancer Detection Using Fuzzy LOGI

This paper means to an incorporated perspective on executing robotized indicative frameworks for bosom malignancy discovery. The significant target of the paper is to be a guide for the peruses, who need to build up a robotized choice emotionally supportive network for recognition of bosom malignant growth. Due to the significance of settling on the correct choice, better characterization strategies for bosom disease have been looked. The arrangement exactness’s of various classifiers, specifically neural organization utilizing Kohenen's first form of Learning Vector Quantization strategy, fluffy classifier utilizing Fuzzy C-implies calculation and measurable classifiers utilizing Logistic Regression and Canonical Discriminant Analysis,  looked at. The intention is to decide an ideal order conspire with higher analytic exactness for this issue. For Study showed neural organization classifier symptomatic correctness’s which is higher than that of the other robotized analytic frameworks.