Reviewerly/exHarmony huggingface dataset:
A Study on the Types and Effects of the Career Education Programs on the Decision of Entrance into vocational high schools

본 연구는 학생들에게 제공되는 진로교육 활동이 직업계 고등학교 진학 결정에 미치는 영향을 알아보고 그 효과를 검증하기 위하여 수행되었다. 자료는 초·중등 진로교육 현황조사(2018)의 자료를 활용한 패널 연구로 수행되었다. 각 진로 활동별 참여 여부에 따른 일반고등학교와 직업계고등학교 진학을 결정한 학생들의 비율은 교차분석을 통해 알아봤으며, 각 진로 활동의 만족도·도움이 된 정도에 따른 진학 결정의 영향을 로지스틱 회귀 분석을 통해 알아봤다. 연구 결과 첫째, 학교에서의 진로 상담과 진로캠프, 외부에서 진로 상담과 학과체험의 참여 여부는 직업계고 진학에 유의미한 영향을 줄 수 있다. 둘째, 진로교육 활동의 만족도와 도움 정도가 직업계고등학교 진학결정에 유의미한 영향을 주지 못했다. 위 결론을 통해 다음 시사점을 도출하였다. 첫째, 진로교육 활동의 만족도 보다 참여 여부 자체가 직업계고등학교 진학에 유의미한 영향을 줄 수 있다. 둘째, 중학교에서는 진로상담을 활성화하고 진로캠프를 운영하여 소질과 적성에 따른 직업계고등학교 진학을 유도할 수 있으며, 직업계고등학교에서는 진로 상담과 학과 체험을 제공하는 것이 효과적이다.This study was conducted to find out the impact of career education activities provided to students on their decision to enter vocational high schools and to verify their effectiveness. The data were conducted by panel research using data from the Survey on Current Status of Career Education in Elementary and Secondary Schools (2018). The ratio of students who decided to go to general and vocational high schools based on their participation in each career activity was examined through cross-analysis, and the impact of the decision to enter each career high school based on the degree of satisfaction and help was investigated through logistic regression analysis. First of all, the participation of career counseling and career camp in schools and experience in departments outside can have a significant impact on entering vocational high schools. Second, the degree of satisfaction and help in career education activities did not significantly affect the decision to enter vocational high schools. The above conclusions have led to the following implications: First, participation itself, rather than satisfaction with career education activities, can have a significant impact on entering vocational high schools. Second, middle schools can promote career counseling and operate career camps to induce vocational high schools based on their aptitude and aptitude, and vocational high schools provide career counseling and academic experience.
The Impact of Innovative Efficiency on Performance of Firms

This study examines whether the firm with high innovation efficiency realizes high operating performance. We measured innovation efficiency by the ratio of patent applications for R&D expenditure or R&D stock and measured operating performance by the ratio of operating income or operating cash flow to total assets for the following year. The sample consists of 1,880 manufacturing firm-years, which listed on the Korean Exchange between 2014 and 2017. We analyze the effect of innovation efficiency on operating performance using a model of Hirshleifer et al. (2013) results show that both innovation efficiency variables have a significantly positive relationship with the total asset operating margin. Besides, the following year's performance, measured by the total asset operating cash flow ratio, also shows a positive relationship with the two innovation efficiency variables at the 5% and 1% significance levels, respectively. The results indicate that high innovation efficiency firms that link the outcomes of R&D to more patent applications realize higher operating performance. Also, we divided the R&D-intensive and non-R&D-intensive industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. However, the effect of innovation efficiency on the operating cash flow is only significant in R&D-intensive industries. This study suggests that the effects of innovation efficiency are more consistent in the R&D-intensive industry. Additionally, we divided the high patent application and low patent applications industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. This study suggests that the effects of innovation efficiency are more consistent in the high patent application industry. We show that a firm's innovation efficiency is a critical factor for a firm's performance, while prior studies on the R&D performance have not considered the innovation efficiency of each firm. The evidence suggests that firms not only consider R&D expenditures but also improve the performance of companies by increasing innovation efficiency. Investors need to consider their innovation efficiency when evaluating the value of firms.
A Parallel Implementation of Hypothesis-Oriented Multiple Hypothesis Tracking

Hypothesis-oriented Multiple Hypothesis Tracking (HOMHT) recursively generates hypotheses on the origins of measurements and manages them, therefore it is computationally intensive. To speed up HOMHT for tracking hundreds of targets in real time, we propose a parallel implementation of this algorithm which distributes hypotheses into independent worker threads residing in multiple CPU cores. The implementation in this paper is based on object-oriented programming: each hypothesis object manages its target data all by itself and the generation and pruning of a hypothesis is achieved by its copy constructor and destructor functions. We evaluate this method by tracking 150 targets through 3 heterogeneous sensors in real-time with 32-best hypotheses running in 1, 2, 4, 8, 16 and 32 worker threads respectively. The results validate the method's scalability in which measurement fusion latency is approximately inversely proportional to worker thread count. We also make a pressure test by tracking 500 targets through 3 sensors, and HOMHT is able to run concurrently in real-time with 32 worker threads.
Consistency and Certain Answers in Relational to RDF Data Exchange with Shape Constraints

We investigate the data exchange from relational databases to RDF graphs inspired by R2RML with the addition of target shape schemas capturing fragments of SHACL and ShEx. We study the problems of consistency i.e., checking that every source instance admits a solution, and certain query answering i.e., finding answers present in every solution. We identify the class of constructive relational to RDF data exchange that uses IRI constructors and full tgds (with no existential variables) in its source to target dependencies. We show that the consistency problem is coNP-complete. We introduce the notion of universal simulation solution that allows to compute certain query answers to any class of queries that is robust under simulation. One such class are nested regular expressions (NREs) that are forward i.e., do not use the inverse operation. Using universal simulation solution renders tractable the computation of certain answers to forward NREs (data-complexity).
Supervised Machine Learning algorithm using Sentiment Analysis based on customer feedback for smart phone product

Nowadays, there are several e-commerce giants namely Amazon, Flipkart and e Bay which have been built on forefront with Machine Learning (ML) that certainly manages the world.This is particularly seen currently in the sector of e-commerce and provided its consequences.However, the ML application in general and in its advancement has produced deep impact in our life style.In the e-commerce field, the technique of ML is implemented and accomplished with better results.Therefore, the research of AI has attained an excellent level with sublevel of ML and deep learning application with a minimal method that is proceeding to concrete future business.Sentiment Analysis (SA) is a technique used for analyzing data that has expressed in terms of text and even for discovering sentimental and emotional content from the text.SA is a process of deep learning method which is an advancement of ML, utilized for finding information with positive, neutral and negative from the review of customers.The identification process and analysis of customer feedback is important using Natural Language Processing (NLP), is said to be sentiment analysis.In this research, the dataset has considered about smart phone products with 4000 customer's feedback and ratings for Prod_ID as input.It is utilized for the analysis based on related categories namely Prod_ID, Prod_name, Brd_name, Rating, Rvew and Rvew_vote.The performance evaluation of classifier can be measured in terms of the accuracy.Based on the results it is observed that Neural Network (NN) has produced better accuracy than other classification algorithm with 98%.
A New Motion Model Selection Approach for Multi-Model Particle Filters

One of the important factors in real-time tracking of the moving radar targets is the speed of the algorithm.In the multi-model particle filters (MMPFs) which is frequently preferred tracking of such targets, the numbers of particles and motion models are important parameters determining the speed of the filter.Reducing the number of particles and/or the model transitions processes as much as possible will facilitate real-time tracking of moving targets by accelerating the algorithm.In this study, for reducing the time cost of the MMPF, a new approach called weighted statistical model selection (WSMS) which reduces the number of model estimation calculations is proposed.A new basic MMPF algorithm that allows the use of the WSMS approach is also constituted.In order to evaluate the success of the WSMS; the MMPFs integrated with the WSMS, are simulated for different noise variances, particle numbers, and scenarios.The simulation results are compared based on processing time and prediction error criterions.The results demonstrate that the WSMS approach increases the speed of the algorithm by reducing the processing time at high rates without any change in the prediction error and, thus it can be used in real-time tracking of the moving targets.
Applying Machine Learning for Automatic User Story Categorization in Mobile Enterprises Application Development

&lt;p dir="ltr"&gt;&lt;span&gt;Mobile enterprise applications (apps) are developed in dynamic and complex environments. Hardware characteristics, operating systems and development tools are constantly changing. In larger institutions, comprehensive corporate guidelines and requirements have to be followed. In addition, larger enterprises often develop numerous apps and lack an overview of development projects. Because of the size of such companies, a comprehensive direct information exchange between developers is often not practicable. In this situation, IT support is necessary, for example to prevent unnecessary duplication of work in the development of software artifacts such as user stories, app screen designs or code features within the company. One approach to overcome these challenges is to support reusing results from previous projects by building systems to organize and analyse the knowledge base of enterprise app development projects. For such systems an automatic categorization of artifacts is required. In this work we propose using a machine learning approach to categorize user stories. The approach is evaluated on a set of user stories from real-world mobile enterprise application development projects. The results are promising and suggest that machine learning approaches can be beneficially applied to user story classification in large companies.&lt;/span&gt;&lt;/p&gt;
Thoughts about using Constraint Solvers in Action

SMT solvers power many automated security analysis tools today. Nevertheless, a smooth integration of SMT solvers into programs is still a challenge that lead to different approaches for doing it the right way. In this paper, we review the state of the art for interacting with constraint solvers. Based on the different ideas found in literature we deduce requirements for a constraint solving service simplifying the integration challenge. We identify that for some of those ideas, it is required to run large scale experiments for evaluating some of the ideas behind the requirements empirically. We show that the platform is capable of running such an experiment for the case of measuring the impacts of seeds on the solver runtime.
Corrections to “Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning” [Aug 21 3458-3470]

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the results of “Fully-supervised (Upper bound)” in Tables III and IV were inadvertently set to intermediate records that were used as placeholders. This error has no effect on any of the interpretations and conclusions. <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tables I</xref> and <xref ref-type="table" rid="table2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</xref> of this amendment show the corrected results (highlighted in italics) of the original Tables III and IV.
Text and Data Mining in EU Copyright Law

Text and data mining can be a useful tool in such diverse fields as scientific research, journalism, culture and not least training of artificial intelligence and its importance is likely to only g ...
ANALISA DAN PERANCANGAN SISTEM APLIKASI PEMBAYARAN ADMINISTRASI MENGGUNAKAN RFID BERBASIS CLIENT SERVER

financial information in schools. Payment information which is the basis for schools to determine future policies. The object of research in this thesis is the Demak Development High School which is one of the agencies engaged in the field of science and education.&#x0D; The research objective is to design an effective and efficient school payment administration recording information system and design a school payment administration recording information system that has a system security that can maintain the accuracy of the data. The research method used is the Borg and Gall R&amp;D Development Research model, the Prototyping System Development Method to produce a product in the form of a Prototype with 6 stages: Research and Data Collection, Planning, Initial Product Development, Product Testing, Product Revision, Final Trial.&#x0D; The conclusion from the results of this study is that this system can facilitate the recording of administrative payment transactions so that there are no mistakes in recording, users can easily present payment reports more quickly so that they can minimize recording time and recording errors, and make it easier for users to print payment reports with affectic and efficient results so as to support and accelerate decision making
Implementasi Sistem Informasi Pelayanan Kesehatan Pada Klinik Smart Medica

The clinic is one of the public services engaged in the health sector which is inseparable from the development of information technology. Smart Medica Clinic in data processing is still done manually where information about the Smart Medica clinic is difficult to obtain, such as information about the doctor and also the doctor's schedule on duty while the Smart Medica clinic is one of the largest clinics in Sekayu Regency and also already famous and has many patients. With this situation, web-based information systems are built so that services to the community can be comprehensive and the Smart Medica Clinic can provide information that is needed by patients up to date and also patient satisfaction is fulfilled. This information system was built using the WaterFall method, using the PHP programming language and the My Sql database.
Illumination of a cylinder block transverse hole for machine vision inspection

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
A Study on Radar Target Detection Based on Broad Learning System

Target detection is one of the most important tasks of radar. Statistical signal processing methods for target detection have made several achievements; meanwhile, neural network-based methods have also received increasing attention. The newly proposed broad learning system (BLS) is an effective and efficient incremental learning system without deep architecture, and it has been successfully applied to the classification problem. In this article, the BLS is applied for radar target detection. We design a BLS target detector using the incremental learning algorithm, and it's very attractive that the BLS detector shows a better performance in the experiment by comparison with the typical detectors.
THE ROLE OF INSTITUTIONAL MECHANISMS IN CREATING ONLINE TRUST: CROSS CULTURAL INVESTIGATION

Online trust, or E-trust, is designed to encourage customers’ purchasing decisions in online environment as a safe and convenient method for fulfilling their needs and/or wants. Creating and maintaining e-trust between customers and e-vendors is a significant factor that encourages customers to adopt online purchasing behaviour. Therefore, organizations created institutional mechanisms that may increase their customers’ trust towards online purchasing environment. However, E-commerce Institutional Mechanisms (such as secure e-banking systems, data encryption and escrow.com services), generate different customer influences. Therefore, the purpose of this study is to better understand these influences that lead to asymmetric customers’ behaviour within different cultures and societies.To achieve this objective the study utilizes the ‘Uncertainty Avoidance’ dimension of Hofstede’s (2010) model of national cultures to understand these cultural differences. This research answers two major research questions: First, how do institutional mechanisms, particularly data encryption and e-banking systems, influence the purchasing decisions of customers from high and low uncertainty avoidance cultures? Second, how can e-vendors establish cross-cultural e-trust among e-commerce customers through utilizing institutional mechanisms? This paper reports on the results of a field study of semi-structured, with open questions, interviews conducted in real e-commerce business environmental settings. The interviews were conducted with e-business owners and e-commerce customers from both the UK and Jordanian cultures.This research's originality derives from that it produces an empirical and a dedicated customer’s perspective, in relation to e-trust, from two very contrasting countries and cultures. The first is the UK, representing the low uncertainty avoidance culture. The second is Jordan, representing the high uncertainty avoidance culture. The findings of this research contributes to the practical implications of business organizations adopting the e-commerce business module. The findings confirm and explains similarities between the UK and Jordanian ecustomers in terms of online purchasing decisions and perceiving e-vendors’ institutional mechanisms. The customers tend to rely on the business brands in terms of creating online trust rather than the E-commerce Institutional Mechanisms, such as e-banking systems and data encryption.
apricot: Submodular selection for data summarization in Python

We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greedy selection algorithm that offers strong theoretical guarantees on the quality of the selected set. Two submodular set functions are implemented in apricot: facility location, which is broadly applicable but requires memory quadratic in the number of examples in the data set, and a feature-based function that is less broadly applicable but can scale to millions of examples. Apricot is extremely efficient, using both algorithmic speedups such as the lazy greedy algorithm and code optimizers such as numba. We demonstrate the use of subset selection by training machine learning models to comparable accuracy using either the full data set or a representative subset thereof. This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets. The code and tutorial Jupyter notebooks are available at this https URL
Applying Structural Equation Model to develop A New Model of Happiness at the Workplace in Promoting Employee Happiness in Public Organization

Happiness at the workplace refers to how satisfied people are with their workplace and lives. Happy people are productive people while those people who are unhappy may not pay full attention to any task. Furthermore, employees in the public organizations in UAE are not well satisfied due to poor workplace conditions. In some ministries in UAE must equipment are not well maintained to ease the work of an employer. The main aim of this paper to develop a Model of happiness at the workplace in promoting employee happiness in public organization using structural equation Model. The data were obtained from the employee of Dubai Electricity and Water Authority in UAE. Since the population of this study is 11787 therefore, the sample size was 370. surveyed through the self-administered Google form and by posting. The data was screened, and out of the 370 questionnaires distributed, 260 were completed and were received. Analysis of Moment Structures (AMOS) in Structural Equation Modelling (SEM) confirmatory factor analysis (measurement and structural measurement models) were used to analyzed the data. Although this study has conceptualized the improving happiness at the workplace in promoting employee happiness in public organization. Additional research is needed among other states in UAE such Dubai. The motivational formulation of employee happiness, job involvement, work place climate and its impact on organizational performance is a point of future research.
A DISTANCE LEARNING COURSE IMPROVES DIAGNOSTIC ABILITIES AND SELF EFFICACY FOR ORAL MUCOSAL LESIONS

Objective: Evaluate the effect of an e-learning course on accuracy and self-efficacy for oral mucosal lesion management. Study Design: The sample of this quasi-experimental study (pre-and post-test) was composed of participants enrolled in a e-learning course on oral medicine held in the Moodle platform. Before and after the completion of the course (self-instructional, 60 hours), 1878 participants answered a test based on 30 clinical cases of oral mucosal lesions. The questions were: (1) to classify of the lesion nature (benign, potentially malignant, or malignant), (2) to form a clinical impression, and (3) to decide whether or not to refer the case to a specialist. Results: After the course, an increase of 16.8% and 14.7%, respectively, on classification and clinical impression was found (P< .01; Wilcoxon test). In addition, a reduction of 19.4% in the intention to refer the case to a specialist was observed, indicating an improvement in self-efficacy to manage oral lesions (P< .01; Wilcoxon test). Conclusion: In conclusion, a e-learning course may be an useful tool in continuing education actions. Objective: Evaluate the effect of an e-learning course on accuracy and self-efficacy for oral mucosal lesion management. Study Design: The sample of this quasi-experimental study (pre-and post-test) was composed of participants enrolled in a e-learning course on oral medicine held in the Moodle platform. Before and after the completion of the course (self-instructional, 60 hours), 1878 participants answered a test based on 30 clinical cases of oral mucosal lesions. The questions were: (1) to classify of the lesion nature (benign, potentially malignant, or malignant), (2) to form a clinical impression, and (3) to decide whether or not to refer the case to a specialist. Results: After the course, an increase of 16.8% and 14.7%, respectively, on classification and clinical impression was found (P< .01; Wilcoxon test). In addition, a reduction of 19.4% in the intention to refer the case to a specialist was observed, indicating an improvement in self-efficacy to manage oral lesions (P< .01; Wilcoxon test). Conclusion: In conclusion, a e-learning course may be an useful tool in continuing education actions.