Reviewerly/exHarmony huggingface dataset:
The Impact of Profitability and Financial Performance on Improving Productive Efficiency in Jordanian Industrial Companies

The study aims to identify the effect of profitability and financial performance on improving productive efficiency in Jordanian industrial companies. In order to achieve this objective, the researcher designed a questionnaire that was distributed to the study population that is composed of Jordanian industrial companies. In order to analyze the data collected some analysis instruments were used, namely: Cronbach alpha, multiple linear regression analysis, Sample t-Test analysis. Accordingly, the study found that there is a statistically significant impact of the profitability and financial performance on improving productive efficiency in Jordanian industrial companies. Based on that, the researcher presented some important recommendations to the Jordanian industrial companies: firstly, increasing the interest in profitability and financial performance to improve productive efficiency. Secondly, increasing the interest in the truthiness and reliability of financial data and statements that reflect the profitability, financial performance, and productive efficiency. Lastly, enhancing awareness among operators and management in relation to the impact of profitability and financial performance on improving productive efficiency to avoid the weaknesses and focus on the strength in the firm operations.
Pembuatan Instrumen Soal Inovatif Menggunakan Aplikasi Hot Potatoes bagi Guru SDN Sukarapih 01 dan 02 Kabupaten Bekasi

Information and technology that are growing rapidly affect the learning process in elementary school.The students of elementary school tend to be more interested in the use of game learning media.It becomes a challenge for the teacher to create a game by including customized teaching material so that students are motivated to find answers and learn from the game.SDN Sukarapih 01 dan 02 Bekasi are located in Kabupaten Bekasi, West Java and have not utilized contemporary teaching methods by utilizing technology.The ABDIMAS activity aims to provide learning media information and references that can be applied in elementary schools, in this case the application of hot potatoes applications to create question instruments that are innovative and attract students' interest.In the implementation of the activity, an introduction, training and application of hot potatoes were conducted to make a question instrument for the teacher.The results obtained from this training were quite positive, the partners were very enthusiastic in participating in the training, were active in participating in the training material and actively asked when operating the hot potatoes application.They expressed satisfaction with the training that had been carried out.
Aplikasi Deteksi Perubahan Wilayah dengan Menggunakan Metode Post-Classification

Changes that occur in the region is one of the problems that are considered significant and strategic that occur in each region specifically in the region ofb. one of the important issues for planners and decision makers in urban and regional policies. Data, information, and tools sometimes turn into a burden in the process of detecting changes in land use. Along with advances in technology to detect changes in an area that are usually done manually (visible, ordinary photos), now it has begun to shift to the use of image technology (satellite), where this is caused by satellite technology, enables the detection of regional changes to be carried out on a wide scale, the time required is more effective and effective compared to conventional regional change detection techniques. Change detection (change detection) is a process of identifying changes in the shape of the surface in a vegetation cover or as a spectral / spatial movement of vegetation bodies over time. Among the change detection methods is to use Post-Classification with consideration of the ease of implementation, where this method works by comparing 2 or more temporal images. &#x0D; &#x0D; Keywords: Detection of Regional Change, Post-Classification Method, application.
Investigating the use of Virtual Reality in teaching chemistry to undergraduate students

Virtual Reality (VR) has become a much more common household commodity thanks to the proliferation of more affordable VR devices. Whilst its use in the gaming industry is widespread, its application in pedagogical environments is underdeveloped, particularly in chemistry. Hence, whether VR will aid or hinder the teaching and learning of chemistry is currently a topic of research and debate (Won, Mocerino, Tang, Treagust & Tasker, 2019).This project seeks to generate a range of VR materials designed to support students learning undergraduate chemistry, with the specific topics decided in consultation with undergraduate student researchers and various academic members of staff. This work is being undertaken in the X-reality (i.e. VR and other forms of augmented realities) laboratories of the Faculty of Science at the University of Sydney. Preliminary materials are being generated and will pilot tested with both students and teaching staff, with all data being audio recorded using a think-aloud protocol. Follow up interviews will also be conducted with all participants. Student understanding will then be tested with common theoretical questions and concept inventories. The results of these trails will be discussed and their implications on the use of VR in the teaching and learning of chemistry considered.
Scaling the AWS EKS, ECS, and ECR Containerized Environments

In the previous chapter, I showed you how to secure your AWS tenant using AWS best practices. In this chapter, you will dive into the world of scaling containers on AWS. Amazon AWS allows you to scale the AWS container services in several ways.
ANALISA KINERJA PEGAWAI UNIVERSITAS METHODIST INDONESIA MENGGUNAKAN FUZZY MULTI ATRIBUT DECISION

Implementation of performance appraisals needs to be carried out by each organization or agency involving management. In this case the Indonesian Methodist University has Human Resources who carry out their duties in serving service activities with the aim of being able to manage and serve effectively and efficiently. In the role of decision support systems in the context of the overall information system is intended to improve performance through the application of information technology and determine the approach used in the decision making process, to evaluate interactive selection. This software was built using the Fuzzy model with the Multi Attribute Decision Making method. This software provides an assessment based on Job Execution Assessment (DP3), namely loyalty, work performance, responsibility, obedience, honesty, cooperation, initiative and leadership so that the results can be used as decision support for promotion. The importance of the assessment for work requires that the assessment must be truly objective, namely measuring the performance of employees at the true Methodist University of Indonesia ,. This means that the implementation of the assessment must reflect the actual implementation of work and evaluate employee behavior that reflects the success of the implementation of work that results in the satisfaction of people who receive services.&#x0D; &#x0D; Keywords: Decision Support, Fuzzy, Multi Attribute Decision Making, Employee Performance
Sparse Time Series Representation in Public Events Early Warning Model

To solve the problem of mechanisms’ delay in early warning for unexpected public security incidents, this paper proposed a highly effective time series sparse representation for determining early warning for public incidents on the basis of position and reinforcement learning. Fractal and sparse were introduced to denote multi-dimensional time series, which effectively reduced the dimensions of time series while avoiding their distortion. Reinforcement learning was used to build the model of early warning for public incidents. Paths to anomaly series or early warning for public incidents were obtained by the agent through iteration training. Then weight parameters of neural networks were analyzed for the determination of early warning for incidents. Simulation showed that under this algorithm, convergence happened when the number of steps was in the range from 500 to 800, 37.5% smaller than that when using the original data. This result of the experiment demonstrated that this method greatly improved the efficiency of early warning for public incidents.
Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa

Ever since the evolution of web technology in the 1990's, the web traffic got subjected to many developments. Web 1.0 and 2.0 are some of the innovations that made the world as a global village and increased the communication among various players in the world. Compared to the former, the latter has more features that opened gates for communication networks across the world. In spite of the sea change that Web 2.0 brought among the federal establishments of Nigeria, full level of adoption is did not occur due to numerous hindrances. The study outcome revealed that majority of the federal establishment does not use the technology for external collaboration. The research also shows that majority of the establishments still use collaboration tools such as letters, telephone and email as means of collaboration with external audience. This is because of the hindrances that are not facilitating the smooth flow of technology adoption.
1195. Hand Hygiene Compliance of Patients’ Family Members in India: Importance of Educating the Unofficial “Fourth Category” of Healthcare Personnel

Abstract Background In India,due to manpower constraints, patients’ family members are often actively involved in healthcare activities of their near and dear ones. They have significant contact with the patient at all the 5 moments for hand hygiene (HH) as listed by WHO. At our tertiary care hospital in north India, we have been monitoring HH compliance (HHC) for nearly a decade. In this study, we analyzed the impact of decade long awareness campaigns to improve HH compliance in our patients’ attendants. Methods Trained infection control nurses directly observed the compliance to hand hygiene at each of the five moments for patients’ attendants over different areas of the hospital over a period of 5 years (January 2014–December 2018). Compliance was calculated as percentage of events over total opportunities and compared. Results 7290 opportunities were observed with an overall compliance of 46.3%. The overall HHC in patient attendants has increased from 35.5% in 2014 to 48.2% in 2018 (P &lt; 0.0001). Compliance at WHO moment 1, 2, 3, 4 and 5 was 51.2%, 47.8%, 67.8%, 48.9% and 24.4% respectively. Among family members, mothers of newborn babies had a much higher HHC (77%) than others (44.7%) (P &lt; 0.0001). Also, the compliance was higher in medical vs. surgical wards and pediatric wards vs. adult wards (P &lt; 0.0001 in both). Conclusion This is the first study about family members’ HHC in a hospital setting in a low and middle-income country. Once trained, family members exhibit fairly good hand hygiene compliance while involved in healthcare activities of their patients. Mothers of newborn babies exhibit exemplary hand hygiene compliance while caring for their babies in our hospital. It is worthwhile empowering and educating patient attendants about the importance and process of hand hygiene as it is likely to result in immense benefit for patients. Disclosures All authors: No reported disclosures.
Speeding up Travelling Salesman Problem Using Hybrid Algorithm

Traveling salesman problem (TSP) is one of the most popular optimization problems and attracts great attention from many researchers. Several studies have suggested different approaches to solving two-dimensional TSP. This research will present an enhancement for genetic algorithm by using a 2-opt method to solve TSP, such that we will use a 2-opt method to generate the initial population for genetic algorithm, and the roulette wheel choice strategy, the order crossover operator and 2-opt method for mutation operator will be highlighted.&#x0D; The experiments of TSP using five real TSP problems taken from the Traveling Salesman Problem Library (TSPLIB), including eil51, st70, pr76,eil76, rd100 (the numbers attached to the problem names represent the number of cities). The error rate of proposed algorithm is (2.233818, 0.030299, 0.057161, 0.084524, 0.123523) . While the error rate of the traditional GA is (2.300088, 0.052921, 0.064016, 0.091775, 0.14666). The results showed the proposed algorithm is acquired higher quality solutions within a reasonable computational time compared to the traditional genetic algorithm. The programming language Pythom3 was used in programming the proposed method.
Potholes Road Classification by Shape and Area Features

Background of the study: Generally, during the rainy season, many potholes asphalt road are found. The high rainfall results in the fragile contour of the asphalt road and triggers a traffic accident. In the last decade, the development of potholes asphalt road detection has various method approaches.Purpose: The research used precision to get a performance of the system.Method: In this study, the development system can classify potholes asphalt road by a simple algorithm. It also considers the time and space complexity.Findings: The algorithms as possible and only uses the handy-camera device to capture data which the level of performance as good as the results of previous research. Capturing data is also various distances with 450 point angles. For classification steps, the system applied two main features, area and shape feature of the object. The used parameters for these features are the length of major and minor axis object. It used to calculate area and eccentricity values.Conclusion: In conclusion, the experiment result reaches 81.696% of the 1125 frames used.
Translation Operator in Graph Signal Processing: A Generalized Approach

The notion of translation (shift) is straightforward in classical signal processing, however, it is challenging on an irregular graph structure. In this work, we present an approach to characterize the translation operator in various signal domains. By a natural generalization from classical domains, one can characterize an abstract representation for the graph translation operator. Then we propose an isometric translation operator in joint time-vertex domain consistent with the abstract form of translation operators in other domains. We also demonstrate the connection between this notion and the Schrödinger equation on a dynamic system which intriguingly describes the idea behind translation on graph.
Data Sharing Incentives and Metrics for Open Data

Metrics and incentives for open data sharing are presented. A cultural shift to open science is occurring and the science community is transitioning from traditional data practices to open data practices. Open data is accompanied by responsibilities that can be opportunities for stakeholders throughout the data lifecycle. Recognition can serve as incentives for open data practices. In terms of metrics, we need to ask whether the data usable and how they are being used. Metrics also can serve as incentives for open data sharing when recognized by hiring, tenure, promotion, and sponsorship committees. Examples of community efforts to address these issues also are shared.
Analisa Kehandalan Ekstraksi Fitur Ordo Kedua terhadap Ordo Pertama untuk Identifikasi Ciri Berbasis Tekstur Warna

The feature extraction method is done by first converting RGB images into grayscale images. Based on the grayscale image, two different processing methods are carried out, namely first order feature extraction and second order feature extraction. The extraction features of the first order used 5 characteristic parameters, namely Mean, Variance, Skewness, Kurtosis, and Entropy, while the extraction features of the second order used 6 characteristic parameters, namely Angular Second Moment, Contrast, Correlation, Standard Deviation, Inverse Difference Moment and Homogenity. The 11 characteristic parameters will then be classified using the LVQ artificial neural network method to find the final weight used as the reference weight for characteristics identification based on color texture. In this research, 30 image samples were used (15 image samples in category A and 15 image samples in category B) which were divided into 16 image samples for training and 14 image samples for testing. The results of the research analysis show that the second order feature extraction method is more reliable than the first order feature extraction method.
한국 근·현대 재가불교운동의 특징과 전망

The social activity of Korean Buddhist believers in Korea began in modern times. It was to check the Japanese Buddhist missionary activity and Christianity from the West in Korea. The Korean Empire established a Jonggyohoe in Anyangam outside Dongdaemun. Jonggyohoe were organized in central and provincial areas but it was abolished in 1904. Around 1910, a group was held to check Christianity and for the development of Buddhism was also established. The social activities of the Buddhist Women's Association were unique. A new Jaegabulgyoundong began in the 1960s. At first, groups not related to the denomination were active. After that, groups belonging to the denomination were active. It played an important role in Korean modern Buddhism. Furthermore, Korean Buddhism played a major role in democratization. As a result the appearance of Korean Buddhism, which relies on the power of public power, was dealt with. Criticized the social reality and promoted Buddhist reform. In the 1990s, Korean Buddhist believers began their own Buddhist movement. They promoted the development of Buddhism. They showed an active interest in solving social problems. It was critical of the denomination to break the Vinaya.
Deep topic modeling by multilayer bootstrap network and lasso

Topic modeling is widely studied for the dimension reduction and analysis of documents. However, it is formulated as a difficult optimization problem. Current approximate solutions also suffer from inaccurate model- or data-assumptions. To deal with the above problems, we propose a polynomial-time deep topic model with no model and data assumptions. Specifically, we first apply multilayer bootstrap network (MBN), which is an unsupervised deep model, to reduce the dimension of documents, and then use the low-dimensional data representations or their clustering results as the target of supervised Lasso for topic word discovery. To our knowledge, this is the first time that MBN and Lasso are applied to unsupervised topic modeling. Experimental comparison results with five representative topic models on the 20-newsgroups and TDT2 corpora illustrate the effectiveness of the proposed algorithm.
Evaluation of Mobile Augmented Reality for Indoor Navigation

In the last three years, several advanced toolkits for developing mobile augmented reality applications have been released. This in combination with the increased computational power of commerciall ...
Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation

Cell detection and segmentation is fundamental for all downstream analysis of digital pathology images. However, obtaining the pixel-level ground truth for single cell segmentation is extremely labor intensive. To overcome this challenge, we developed an end-to-end deep learning algorithm to perform both single cell detection and segmentation using only point labels. This is achieved through the combination of different task orientated point label encoding methods and a multi-task scheduler for training. We apply and validate our algorithm on PMS2 stained colon rectal cancer and tonsil tissue images. Compared to the state-of-the-art, our algorithm shows significant improvement in cell detection and segmentation without increasing the annotation efforts.
EDUCATOR V. N. SOROKA-ROSINSKY AND HIS PEDAGOGICAL HERITAGE

The article is devoted to the outstanding Russian educator Victor Nikolaevich Soroka-Rosinsky (1882-1960). The paper provides his biographical data, characterizes comprehensively his practical pedagogical activity, and analyses his most important theoretical works. Special attention is paid to the period when Soroka-Rosinsky worked as a director of the educational institution known as “The School named after F. M. Dostoevsky” (ShKID). In this connection, the researcher provides A. S. Makarenko’s commentaries on the ShKID based on the book written by its former pupils G. G. Belykh and A. I. Yeremeev; the researcher draws an analogy with another once popular pedagogical book - “Last Gymnasium”. It is emphasized that V. N. Soroka-Rosinsky was selflessly devoted to the education of the younger generation.
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier’s respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision.
Residual Nominal Automata

Nominal automata are models for accepting languages over infinite alphabets. In this paper we refine the hierarchy of nondeterministic nominal automata, by developing the theory of residual nominal automata. In particular, we show that they admit canonical minimal representatives, and that the universality problem becomes decidable. We also study exact learning of these automata, and settle questions that were left open about their learnability via observations.
Indonesian Language Learning Based on Teachers’ Directive Speech Act Strategy (TDSAS) and Students’ Positive Affective Color Response (SPACR) with a Synectic Model

Love and self-esteem can be fulfiled by the teacher through teachers’ directive speech act strategy (TDSAS). Teachers need to use teachers’ directive speech act strategy to make the students feel loved and respected and emerge the students’ positive affective color response (SPACR) which creates a fun, effective and conducive learning process. Some verbal abuses made by the teachers affect the students’ psychology (inferiority, trauma, laziness, and has no motivation to come to school). This study aimed to portray the Indonesian language learning in junior high school using TDSAS-SPACR with a synectics model to improve the quality of the process and learning outcomes. Using the descriptive-qualitative method, the results showed that the TDSAS based learning which emerges SPACR creates an effective and fun Indonesian language learning in junior high school.