Reviewerly/exHarmony huggingface dataset:
Self-assessment of competence in online search for educational information by pupils with different educational motivations

The article presents the results of a study aimed at analyzing the relationship between the qualitative characteristics of online search for additional information which is necessary for completing educational tasks, and the educational motivation of secondary school pupils. The qualitative characteristics of online search for additional educational information included pupils’ subjective assessments of the frequency of using educational online search and competence in the relevant aspect of educational activities. The aim of the study is significantly revealed by the following research questions: 1) What is the dynamics of self-assessing frequency and competence in the field of online search for additional information throughout secondary school? 2) What motivates secondary school pupils to use online search for additional educational information? 3) What contribution does online search for additional educational information make to the academic achievements of secondary school pupils? Five hundred and eighty-two pupils of 5th -9th grades of secondary schools in Saint Petersburg were involved in the study. Empirical data were collected using questionnaire (for assess the quality characteristics of online search for additional educational information) and test (to assess educational motivation). According to empirical results, self-assessment indicators of frequency and competence in online search for additional information show consistent growth throughout secondary school and reach the highest level in the 9th and 7th grades, respectively. Productive use of online search for additional educational information is supported primarily by cognitive motivation; while for pupils with a predominance of other educational motives characteristic overestimate their competence in the online search for additional educational information that does not match their actual academic achievements. Based on the obtained results, the authors formulate a proposal to develop and implement elements of teaching pupil’s productive strategies of online search for additional educational information in the educational process of secondary school.
Uji Validitas Konstruk dengan CFA dan Pelaporannya

Confirmatory Factor Analysis (CFA) is the most reliable method of construct validity analysis in the fields of psychology, education and social sciences. From the author's observations on research articles as well as bachelor and graduate theses, and dissertations using CFA, it was found that there are a lot of misunderstandings and incompleteness in reporting CFA analysis. This paper is intended as an effort to improve this situation and provide recommendations in reporting data analysis using CFA. At the very least, this article is to show the important things that must be considered in understanding and using CFA and test theory in general.
Supporting open research data practice through data curation and discovery: A global perspective

Abstract This panel will address the issues associated with the practice and service of open research data curation and discovery from a global perspective. The sub‐fields of information science such as information retrieval, information curation, information practices and human‐centered data science have approached the open research data initiatives from multiple lenses. The issues of data creation, capturing, curation, sharing, discovery and reuse of cut across the sub‐fields. We will identify and discuss the emerging themes in open data curation and discovery drawing on active research projects, repository practices and research data capturing and reuse in a selection of disciplines from health domain to archaeology and cultural heritage.
Ceres

Knowledge graphs have been used to support a wide range of applications and enhance search and QA for Google, Bing, Amazon Alexa, etc. However, we often miss long-tail knowledge, including unpopular entities, unpopular relations, and unpopular verticals. In this talk we describe our efforts in harvesting knowledge from semi-structured websites, which are often populated according to some templates using vast volume of data stored in underlying databases.
nilde: Nonnegative Integer Solutions of Linear Diophantine Equations with Applications. R package version 1.1-3

nilde: Nonnegative Integer Solutions of Linear Diophantine Equations with Applications. R package version 1.1-3
SVA Library Research Guides: Distance Learning Support: Streaming Video

To support the transition to online courses, the SVA Library presents the following guide outlining the collections, services, and support available remotely to students, faculty, and staff.
Conscious self-regulation as a resource of efficiency of task solving for novices and experts

The article presents the results of comparing the development of conscious self-regulation and the effectiveness of solving professional tasks for novices and professionals. Participants were chemists with different levels of professional experience (N = 42), the group of novices included students of the Faculty of Chemistry (N = 21), the group of experts included chemists working in their specialty for more than ten years (N = 21). An analysis of objective indicators of the effectiveness of tasksolving has demonstrated that chemists with long work experience solve professional tasks faster and with fewer errors. Two types of strategies were identified in solving chemical tasks. Students have a «perceptual strategy» — they spend more time and effort analyzing the presented answer options, make more transitions between the presented task and answer options, and make longer fixations on the task area. Experts, in contrast, apply a «representative strategy» for solving problems, which is characterized by building a mental representation of a molecule of a substance, reducing the time it takes to solve a task, and increasing the duration of fixations on the problem area. Conscious self-regulation in this study was evaluated using the methodology «Style of self-regulation of behavior». Among the indicators of selfregulation, there are statistically reliably correlate both the indicator of the time taken to solve task and the indicator of errors made by the cognitive-regulatory process «target planning». A comparison of the regulatory indicators of students and experts revealed significant differences in the scales «Planning goals» and «Reliability». The data obtained allow us to conclude that development of conscious self-regulation of advancement and achievement of goals may turn out to be a significant resource for the effectiveness of a specialist in the field of chemistry. It is possible that the ability to plan professional goals and regulatory reliability develops with the acquisition of professional experience, which can contribute to the growth of the effectiveness of professional actions of specialists.
Improved GrabCut Algorithm Based On RANSAC For Tire Belt Image Segmentation

Tire quality is the foundation of tire production. With the increase of tire output, defect detection of tire forming process becomes more and more important. In the process of tire forming, the segmentation of the tire belt is the basis of detection. In this paper, a method combining RANSAC algorithm and GrabCut function is proposed to segment the tire belt. Firstly, the surface fitting is carried out by RANSAC algorithm, the drum surface is corrected to a plane and the difference between the drum plate and the drum seam is reduced at the same time. Then, the fixed rectangular frame is set up in combination with GrabCut algorithm to segment the belt, and the small connected domain is removed after the image binarized to process the results. Finally, the results are compared with the traditional GrabCut algorithm and U-net algorithm. The comparison results show that the algorithm in this paper can segment the tire belt image more accurately and effectively without the need for human interaction.
Application of Neural Network Model to Predict the Performance of Zeolites in Water Absorption from Fuels

Removal of slight water from organic components is a problem in industries, because the processes such as distillation need to very much energy to removal slight water from organic components. Zeolites, cause to especial structure, often able to adsorption water molecules from organic liquids. Cause to adsorption methods able to preparation anhydrous liquids, at this research, we have used three hydro fill zeolites 3A, 4A and 5A for removal water from amine component. The adsorbents have been used at different operation condition in shaker. These results as shown, adsorption water dependent to amount of zeolite adsorbent, water count ant in mixture and time of adsorption. In this study, neural network modeling was used to predict the experimental results and showed that the genetic algorithm can predict the experimental results with the least error.
Converting polyethylene to valuable alkanes

A catalytic system consisting of porous silica and platinum nanoparticles can convert high-density polyethylene to compounds used for making diesel fuel and lubricants ( Nat. Catal. 2020, DOI: 10.1038/s41929-020-00519-4 ). The synthetic system mimics natural enzymes that digest proteins by repeatedly snipping the molecular chains. It could provide a way to turn plastic waste into products that are more valuable than those made via melt-processing and other methods for recycling plastics . To make the catalyst, researchers led by Wenyu Huang, Aaron D. Sadow, and Frédéric A. Perras of Ames Laboratory deposited roughly 3 nm diameter platinum nanoparticles on larger amine-functionalized silica spheres. Then they grew a shell of silica riddled with nanometer-sized pores around the silica spheres. NMR spectroscopy and other types of analyses show that the structure of the catalyst, the length and width of its pores, and polymer-surface interactions drive polyethylene chains to snake their way into
DAMPAK PENGGUNAAN GAME ONLINE TERHADAP SISWA DI MTsS ULEE TUTUE ACEH UTARA

Online game is one type of game that involves many players who use LAN (local area network) or the Internet. Its presence is agreed to have neglected students by spending to play online games which in turn makes student achievement decreases. This issue is the focus of research, mostly related to the use of online games and their solution to the students of MTsS Ulee Tutue. Qualitative research uses observation, interview and documentation techniques for data collection. This research resulted in positive and negative findings. The positive impact can be made as entertainment, as a venue for concentration training and making friends. Besides positive thinking is that students can control themselves, train sports, train intelligence, creativity, logic and nerve reflexes of the brain and hands when playing games. Meanwhile, the negative is to waste time, waste and take payment as a student. The solution is to solve things that are new in the world of the internet, teach time discipline, and improve social relationships with friends and stay away from online games gradually.
Session and Authentication

Our the next topics are session and authentication, which are fundamental concepts in any modern web application.
Migratable AI: Personalizing Dialog Conversations with Migration Context

The migration of conversational AI agents across different embodiments in order to maintain the continuity of the task has been recently explored to further improve user experience. However, these migratable agents lack contextual understanding of the user information and the migrated device during the dialog conversations with the user. This opens the question of how an agent might behave when migrated into an embodiment for contextually predicting the next utterance. We collected a dataset from the dialog conversations between crowdsourced workers with the migration context involving personal and non-personal utterances in different settings (public or private) of embodiment into which the agent migrated. We trained the generative and information retrieval models on the dataset using with and without migration context and report the results of both qualitative metrics and human evaluation. We believe that the migration dataset would be useful for training future migratable AI systems.
LibGuides: Library Services for Arcada Staff: På svenska

Welcome to the Libguide for staff at Arcada UAS. Here you find information about the materials and services offered by the Arcada library.
Factors on the Execution Times of Metropolis Resampling and its Variations

Particle filter is a serial Monte-Carlo estimation method. It is suitable for the applications whose system or measurement model is highly non-linear and uncertainties are large. The standard particle filter encounters degeneracy problem. One of the important solution is resampling. The most common resampling method is Systematic resampling. It requires collective operations over the weights. This causes numerical instability problem for Systematic resampling. Furthermore, interaction between all weights causes less-readily parallelization of Systematic resampling on graphics processing unit (GPU). To overcome these problems, Metropolis resampling is proposed. Since it only uses ratio of two weights rather than collective operations, it does not suffer from numerical instability and it is more suitable for the parallel implementation of it on GPU. Although it is fast in theory, it suffers from non-coalesced global memory access patterns when implemented on GPU. Metropolis-C1 and Metropolis-C2 are proposed to overcome this problem. In this study, we investigate the factors on the execution times of Metropolis, Metropolis-C1 and Metropolis-C2. There are mainly three factors. These are physical resources and limitations of the GPU, non-coalesced global memory access patterns and the number of particles. We discuss how they affect the execution times of these resampling algorithms in detail.
Fractal behaviour of microseismic energy associated with mining-induced tremors

ABSTRACTAccording to the real-time monitoring data from N1202 island workface of Yuwu coal mine, in Shanxi Province of China, the microseismicity is studied during the actual mining process in the field, the moment magnitude and energy distribution of microseismic events are obtained, the source locations of mining tremors caused by 3 large magnitude microseismic events are determined, and the moment magnitude and energy are calculated. Based on fractal and chaos theory, the mutual information method is introduced to compute the delay time of time series of microseismic energy, the G-P algorithm is introduced to calculate the fractal dimensions of mining tremors with different energy, which is in a declining trend and the lowest on the day of mining tremors. The moment magnitude, energy and fractal behavior of energy under different mining tremors will provide a valuable reference to the early warning of mine dynamic disaster.
A Comparison Study on Various Continuous Integration Tools in Software Development

Continuous integration (CI) frameworks are currently the basis for a few software development projects that reduce the time and cost of the software. Most of the companies provide CI tools with similar facilities and some companies build their own CI tool. However, it does not mean that the costliest product is better than a low-cost or open-source device. Generally, all of these tools are intended to make the Product Building Software Process easy and automated by quality assurance and time reduction. In spite of this fact, ongoing integration tools have their own merits and demerits, so it is very important and sometimes difficult to choose the right CI tool for a project. The wrong choice of tools can reduce the overall flexibility and quality of the software. A few continuous integration tools, such as Jenkins, TravisCI, Codeship, Bamboo, TeamCity and CircleCI, which implement CI practices, are generally adopted by programmers, developers. The main objective of this paper is to demonstrate the analysis, usefulness and comparison of selected tools and to help in selecting the most compatible CI implementation tool that meets project requirements.
Corpora and the discourse of advertising

It is not an overstatement to say that these days we are saturated with advertising. We do not even need to enter a commercial space such as a shopping mall or flick through a glossy magazine where we can reasonably expect some advertising practices taking place; public spaces that traditionally had nothing to do with commercial activities such as university buildings, clinics and hospitals all display adverts of many kinds. Every time we do searches on Google, read an online newspaper, or open a social media site, we are immediately bombarded with adverts or branded content of some sort that try to attract our attention and make us click, like, share or download something. In times of late capitalism, mass production and information overload, advertising has become the key marketing tool for attracting attention and converting non-consumers to consumers, making it an all-pervasive form of discourse. Although advertising has a well-defined and easily recognizable purpose (generating revenues), it comes in a variety of discursive, generic and semiotic manifestations. This makes advertising an important and fertile but also challenging ground for a corpus-based (critical) discourse analysis. This chapter reviews contributions of corpus-based research to our understanding of the discourse of advertising. It begins by a brief overview of some of the major developments in advertising including some of its controversial practices. Subsequently, research studies that have used a corpus approach to investigating aspects of advertising are reviewed. This is extended by a focal analysis, which demonstrates how corpus tools can be effectively adopted to explore discursive practices of new forms of digital advertising. The focus is on native ads. The chapter concludes with observations regarding opportunities and limitations of a corpus-based discourse analysis to study advertising and discusses avenues for future research.
LibGuides: Digital Reference Services: Fun or Learning Games

Online research, learning, and educational activities. Ideas for playing to socialize or learn at home
Research Trends and Directions of Tourism Education: Analysis of Tourism-related Research Listed in Korea Citation Index (KCI)

Today’s education market is actively discussing the role and direction of college education due to diverse changes in the educational environment such as the difficulty of finding jobs, the decrease in the number of school-age people, and online education. For the tourism industry, which has a high reliance on human services, it is significant to cultivate excellent manpower through effective college education, and it is timely to discuss how to educate students more effectively. Although it is important to promote tourism education research that can improve the educational environment and effectiveness by understanding the inherent and external factors involved in tourism education, prior literature has been limited in reviewing previous studies and giving research directions. This study aims to analyze the research trends of the tourism education and present the research directions. In order to achieve this, 202 papers from six major journals listed in the Korea Citation Index were collected by the year of publication, research subject, and research method. The result indicated that while curriculum topics are the most popular, there is a low interest in practical educational research such as students’ field practices. As for the educational environment, most studies have focused on students’ learning environment, and topics of campus life have been overlooked. Further, as the majority of studies have employed quantitatively-oriented research methods, it is suggested to promote other methods including mixed methods research. We also encourage researchers to target educational suppliers such as professors and industry professionals. Based upon the research findings according to the classification nomenclature, the present study can improve tourism education by addressing the implications and future directions for tourism education research.
A Fuzzy Expert System for Malaria Disease Detection

It is found that the malaria disease is a prime and major cause to the human health. The pernicious trappings of malaria stooge to the physical body cannot be making light of it. In this research work, a fuzzy-based expert system for the total handling of malaria disease had granted for providing judgment support platform to the specialist and healthcare researchers in the same endemic province. The proposed and implemented system consists of major components which include the cognitive content, the fuzzification, the inference engine and de-fuzzification for decision making. The fuzzy inference engine developed during this work is that the root sum square. This method is the depiction of inference that was designed and developed to infer the info from the fuzzy-based rules used in this algorithm. Triangular fuzzy membership function was accustomed that shows the degree of attendance of every input specification, and therefore, the de-fuzzification technique employed during this research is that the centre of gravity. This fuzzy-based expert system had been developed to help and to support clinical perception, diagnosis and therefore the expert’s proficiency. For validation and empharical analysis, the data of thirty patients with malaria defection was used. The results that were calculated are within the range of that was predefined and predicted by the territory proficient.
Data Train Reduction on Data Image With K Support Vector Nearest Neighbor (Case Study : Maize Leaf Image)

In this study, we applied the K Support Vector Nearest Neighbor algorithm to reduce data train on data image. The data image that we used is the maize leaves image infected with fungi and healthy maize leave. The aim of data train reduction in this study is to get faster and more accurate prediction results. This because by using the K Support Vector Nearest Neighbor algorithm, a support vector that is formed from the algorithm really characterize the objective function of the problem. The accuracy obtained from this study is 0.20 or 20% mean error for the value of nearest neighbor K  = 3 and using K Nearest Neighbor as a model construction algorithm. The error value is smaller than when we compared to the construction of the model without performing data train reduction. The error value if not doing any reduction is 0.209 or 20.9%. Whereas in terms of time efficiency, working with the K Support Vector Nearest algorithm is 24 seconds faster than without performing data train reduction