Reviewerly/exHarmony huggingface dataset:
Research on How Educational Training for Public Servant Affects Organizational Success - Focused on the Mediating Effect of Job Satisfaction -

In this research, the effect of public servant educational training on organizational success was empirically analysed. For this purpose, variable for analysis was set to four parts, public servant’s educational training, work autonomy, work performance capability, job satisfaction, and verified a relation between variables and Federal-Provincial Government’s organizational success. Subject of analysis was aggregated from the 2018 Survey of Public Servant’s Life, conducted by Korea Institute of Public Administration with information from Public Information Portal, and used for the analysis. Moreover, research empirical analytics subjects were from 46 Federal Departments, and 17 Provincial Government’s general Public Servants. Analysis results tell that Public Servant’s educational training affected their work performance and work autonomy positively and the theorem that their performance would be affected positively was confirmed. Moreover, Public Servant’s work satisfaction was affected by their work performance and work autonomy positively. Lastly, Public Servant’s work satisfaction also affected organizational success positively. All of the theories presented by the researcher were adopted, which could be used for explaining that the study on education and training of public servants, which had been relatively lacking, has been expanded, and the variables of work autonomy and work performance can be used to explain the relationship between variables in job satisfaction and organizational performance. In conclusion, it was analyzed assuming that the education and training of public servants would affect the autonomy of work and the performance of work at the individual and organizational level, which would soon spread to the national level. However, there were limits for the current data, and it was not analysed practically. In future research, the direction of future research is to analyse how public education and training of public servants find and influence the components that are transferred from individual and organizational level to national level.
THE USAGE OF INTERNET: A THREAT TO STUDY HABITS OF THE STUDENTS

The usage of technology in every walk of life in India has changed the entire situation prevailingover the country. Particularly, in Education, it brought a revolutionary change by adding the number ofeducational apps through the internet. However, the Government is also in favour of education at your doorstepthrough the online education system. It provides study material, video lessons by experts, testing and evaluationand so on. In the same way, students from urban and rural areas are also rapidly dependent on internet. As thereare a number of internet sources are available, there is a huge debate on the usage of the internet anddependency on it in the education field. The present paper titled; The Usage of the Internet: A Threat toStudy Habits of the Students focuses on the usage of the internet and its influence on the study habits of highschool students in the Rayalaseema region. It examined fifty students from five High schools taking ten fromeach school. The result of the study found that there is huge damage to the study habits of the students due toless control over the usage of internet.
Text to English and Text to Filipino Sign Language Translator: An Android-based Mobile Application with Text Analysis Using Knuth-Morris-Pratt Naive Algorithm

The inability of an individual to hear sounds causes trouble in communicating among other individuals. Sign language helps the deaf community express what they want to say through different hand gestures, facial expressions and body movement. However, not everyone is knowledgeable of sign language. This study aims to develop an android based mobile application translator system which translates text into sign language. The system is called SiTa: Sign Talker. It accepts English and Filipino words or phrases then translates to American Sign Language (ASL) and Filipino Sign Language (FSL). The text analysis algorithm used by the proponents is the Knuth-Morris-Pratt Naive Algorithm which searches the corresponding sign language animation stored in the application that matches to the entered words or phrase by the user. Besides that, the developed application was evaluated through survey for the performance in term of functionality, reliability, usability, efficiency, maintainability, and portability. The survey results obtained showed that most participants were satisfied with the overall performance of the development system application.
‘매체 언어생활 성찰’의 교과서 학습 활동 개선 방향

The purpose of this study is to analyze how the curriculum contents related to the reflection of the use of language in media are embodied in textbooks and to suggest improvements. For this purpose, I analyzed how the reflection of the use of language in media, which is a content element of the 2015 revised Korean language curriculum, was implemented in the ‘Language and Media’ textbook. Based on the results of the analysis, I suggested how to design textbook activities of reflection of use of language in media. First, in order to enable reflection as the subject of ''we'', it was suggested to construct a learning action to synchronize group reflection based on the common interests of class members and to ensure interactive continuity throughout the reflection process. Second, in order to allow learners to experience the attributes of reflection more fully, it was proposed to construct the contents of the reflection behavior by harmoniously reflecting the cognitive, affective, aesthetic, and moral attributes of the reflection behavior in learning activities.
언어 네트워크 분석을 활용한 한국어 쓰기 교육 연구 -논증 텍스트에 나타난 핵심 어휘를 중심으로-

The purpose of this study was to examine the use of linguistic elements in argumentative texts written by foreign Korean learners in comparison with texts of Korean native speakers, and to clarify their characteristics in terms of text genre. For this reason, after extracting key words that are judged to be statistically significant using text mining, we visualized the relational network between various key words and compared them. As a result of the language network analysis, we were able to compare the argument methods of the two groups, and also found the differences in the viewpoints to observe the situation. This is a result that shows that the genre knowledge of the argument text of foreign learners is insufficient compared to Korean speakers. Therefore, it can be seen that learning and training related to text genre and verbal expression are required in the field of Korean education. The contents of the process and results of this study can be used for genre-based writing education. (Sangmyung University, Seowon University)
Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions.

The widespread adoption of encrypted communications (e.g., the TLS protocol, the Tor anonymity network) fixed several critical security flaws and shielded the end-users from adversaries intercepting their transmitted data. While these protocols are very effective in protecting the confidentiality of the users' data (e.g., credit card numbers), it has been shown that they are prone (to different degrees) to adversaries aiming to breach the users' privacy. Traffic fingerprinting attacks allow an adversary to infer the webpage or the website loaded by a user based only on patterns in the user's encrypted traffic. In fact, many recent works managed to achieve a very high classification accuracy under optimal conditions for the adversary. This paper revisits the optimality assumptions made by those works and discusses various additional parameters that should be considered when evaluating a fingerprinting model. We propose three realistic scenarios simulating non-optimal fingerprinting conditions where various factors could affect the adversary's performance or operation. We then introduce a novel adaptive fingerprinting adversary and experimentally evaluate its accuracy and operation. Our experiments show that adaptive adversaries can reliably uncover the webpage visited by a user among several thousand potential pages, even under considerable distributional shift (e.g., the webpage contents change significantly over time). Such adversaries could infer the products a user browses on shopping websites or log the browsing habits of state dissidents on online forums and encyclopedias. Our technique achieves ~90% accuracy in a top-15 setting where the model distinguishes the article visited out of 6,000 Wikipedia webpages, while the same model achieves ~80% accuracy in a dataset of 13,000 classes that were not included in the training set.
VOLUMETRIC EFFECTS FOR IMAGE FORMATION OF 3D ASYMMETRIC EDGE

The peculiarities for formation of the image of volumetric asymmetric absolutely absorbing edge (the main fragment of constant thickness thick plates) in a diffraction-limited projection system are investigated in analytical form applied to 3D-objects dimensional inspection. Structures and profiles of image intensities for front and back object sides are studied respectively at small and big apertures of the 3D-object optical system for various ratios of object bevel c , the Fresnel zone size d ~ ( l - the light wavelength, d - object thickness) and an angular aperture of the optical system. It is shown that in case when the bevel с &lt;&lt; d , the shift of intensity profile of the 3D-edge image, proportional to Fresnel's zone and bevel size, takes place. Formulas for the image profile of the back side are obtained and investigated in case of strong volumetric effects, when the focus-row depth of the system is much less than the object thickness. The obtained results are in good agreement with results of computer simulations.
Deep Representation Learning on Complex Graphs

Author(s): Zheng, Cheng | Advisor(s): Wang, Wei; Kao, Jonathan C. | Abstract: Graph representation learning serves as the core of many important tasks on graphs, ranging from friendship recommendation, name disambiguation, drug discovery, and fraud detection. Recently, deep learning has revolutionized various domains such as computer vision, natural language processing, speech recognition, etc. Inspired by the success of deep neural networks, there has been an increasing interest to learn graph representations with deep learning models such as autoencoders, convolutional neural networks, etc. However, graphs in real-life applications usually have complex structures such as sparse connections, task-irrelevant information, and rapidly evolving structures. The complexity poses great challenges to the existing frameworks, such as network embedding models with random walks and graph neural networks based on the neighborhood aggregation. In this dissertation, we propose several deep learning frameworks to tackle the aforementioned problems of graph representation learning on complex graphs. We propose a novel model to learn network representations with adversarially regularized autoencoders to overcome the sparse sampling issue of random walks on graphs. To resolve the task-irrelevant noise, we propose a general framework that is trained to simultaneously select task-relevant edges and learn graph representations by the feedback signals from downstream tasks. To learn from the dynamic evolving graphs, we propose to extract local features by performing convolutions in nodes' neighborhoods defined in joint temporal-structural space. The methodologies presented in these frameworks span different research areas, including deep network embedding, graph representation learning, temporal graph modeling, and node classification on graphs. As a result, these methodologies not only tackle specific challenges in the graph learning tasks mentioned above but also shed light on other applications like social network analysis.
AI Research at Scale-Opportunities on the Road Ahead

This article consists only of a collection of slides from the author's conference presentation.
Evaluating mobile remote presence bots for medical consultation in nursing homes

Abstract Interviews with nursing home residents and medical practitioners who used a telepresence robot aka mobile remote presence bot (MRP) for medical consultation suggest that consulting via MRP is viable option for certain types of consultations. Participants perceived communication via the MRP as generally positive and the interaction felt natural even though it clearly wasn't the same as face‐to‐face conversation. The interpersonal quality of communication was slightly reduced; consultations may become more effective however this may impact on the emotional relationship between patients and doctors. Medical practitioners would be able to spend more time in conversation with their patients since time spent travelling to residential homes is greatly reduced. Consulting via MRP also offers a safe option during isolation regimes like the ones that have been implemented in long term care facilities around the world during the SARS‐CoVID‐2 pandemic.
Distributed Modular Multiplication to Be Processed by a Network of Limited Resources Devices

Asymmetric cryptography algorithms, still considered the most robust tool available in the cryptography domain. It incorporates intensive modular exponentiation calculations, which entail considerable computing power, memory and storage space. Common IoT device is equipped with limited computing resources and so, for security purposes, it can execute symmetric and limited asymmetric cryptography. In most cases, IoT devices do not have the capacity required for executing massive modular multiplications of numbers of magnitude of 4 K bits and more. This leads to the lack of asymmetric cryptography in the IoT domain resulting with a reduction in security means to cope with the raising security challenges. The security problem increases as the number of IoT devices is expected to significantly grow soon. We propose a new implementation of asymmetric cryptography, which splits its heavy calculations into micro-processes, where each micro-process is distributed to an appropriate IoT device, connected to the network. The accepted result is transferred back to the distributing IoT. Once all the results are accumulated, a consolidation process is activated to generate the final calculation result, which then is fed into the cryptography process, to generate the Encrypted/Decrypted string, proving the ability to maintain asymmetric cryptography also at the IoT level without compromising security.
Corporate Responsibility Reporting in Africa: The Effect of Macroeconomic Indicators and Political Regime

The main aim of this paper is to empirically assess the relationship between macroeconomic indicators, political regime, and corporate responsibility reporting in African countries. Using ex post facto research design, the paper stands out from previous analyses by including other independent variables, such as regulatory quality, rule of law and government effectiveness in assessing the relationship between corporate responsibility reporting, macroeconomic indicators, and political regime. The population comprises 58 African countries, and the sample is based on 48 African countries. Secondary data was employed to gather information on the variables. The study used a multiple regression model to examine the relationship between foreign direct investment, gross domestic product, inflation, political regime, and corporate responsibility reporting in African countries. The study found an insignificant positive relationship between the number of corporate responsibility reports, political regime, foreign direct investment, gross domestic product, and inflation after applying controls for government effectiveness, regulatory quality, and rule of law. The paper assists in understanding the relationship between political regime, macroeconomic indicators, and corporate responsibility reporting in Africa. Also, an understanding of how institutional factors influence corporate responsibility reporting in African countries could help enforcement institutions, such as the stock exchange, industry regulators and key players that monitor corporate, social, and environmental responsibility issues, to take the necessary steps to improve responsibility reporting provided by organizations in Africa. To conclude, the paper makes a unique contribution to the assessment of the relationship between macroeconomic indicators, political regime, and corporate responsibility reporting.
LibGuides: Primary & Secondary Sources: Primary Sources

This guide explains the differences between primary and secondary sources, including how to identify them, the different formats, and finding them.
Implement Tests in Our Daily Work Routine

We discussed many types of tests – unit, integration, performance, UI, and snapshot tests. But if you ask me what the most challenging task in writing tests is, I would say actually writing them.
Follow the Digital Thing

Abstract Acknowledging how Bitcoin is geographically contingent and diverse, the follow the thing research design outlined allows for tracing the connections between different aspects of its protocol that are practised by a multitude of people in various places. This is done by documenting traditional follow the thing work and explaining how knowledge can be gathered from such a technique before adapting this research process for the task at hand. The breakdown then shifts into sketching a specific yet malleable research method that harnesses the flexibility necessary for researching the complex political economies of Bitcoin and other blockchains. This multi-sited and multi-angled approach creates a partial but far-reaching account of algorithmic decetralisation.
English and new media

This chapter explores some key aspects of the relationship between new media and the use of English in a global context. It focuses on how various aspects of new media have changed the kind of English used, changed how people do things with texts in English and facilitated new ways of analysing English texts. New technologies are key resources for globalisation and they seem to have assured a dominant position for English. Many more people use English as a second, third or even fourth language, which means that the use of English may continue to grow, even as other languages are also increasing their presence on the internet. The chapter explains new practices involving the use of technology applied to the production, mediation and provision of English language texts. It considers some aspects of the relationship between computer technology and English in the context of more conventional approaches to reading.
Zich verzoenen met het voorzetselvoorwerp

Reconciling with the prepositional object. On the influence ofVandeweghe (2011) In this paper I argue for the positive aspects of the claim in Vandeweghe (2011) that there are two types of prepositional object: a primary and a secondary type, which have a different relationship to the predicate. The most discussed consequence of this theory is that it allows a simple sentence to contain two prepositional objects provided that they have a different hierarchical status. Here I focus on sentences with only one possible secondary prepositional object and no other objects. These sentences show that a fixed preposition is not a sufficient condition for a PP to be considered a secondary prepositional object; the preposition should also be used in a non-literal, abstract way. When it is difficult to decide whether this is the case, other semantic properties have to be taken into account, such as the reciprocal character of the predicate.
Improving Communication Efficiency for Encrypted Distributed Training

Secure Multi-Party Computation (SMPC) is usually treated as a special encryption way. Unlike most encryption methods using a private or public key to encrypt data, it splits a value into different shares, and each share works like a private key. Only get all these shares, we can get the original data correctly. In this paper, we utilize SMPC to protect the privacy of gradient updates in distributed learning, where each client computes an update and shares their updates by encrypting them so that no information about the clients’ data can be leaked through the whole computing process. However, encryption brings a sharp increase in communication cost. To improve the training efficiency, we apply gradient sparsification to compress the gradient by sending only the important gradients. In order to improve the accuracy and efficiency of the model, we also make some improvements to the original sparsification algorithm. Extensive experiments show that the amount of data that needs to be transferred is reduced while the model still achieves 99.6% accuracy on the MNIST dataset.
In Memoriam: Keith Quentin Hayes II (1965–2020)

The mineral community lost a much‐too‐young member on 5 June of this year. After a valiant battle with cancer, Keith Quentin Hayes II, at the age of fifty‐five, passed at his home in Rock Springs, ...
Developing the Shaping Procedures Used with Constraint-Induced Cognitive Training

To develop techniques to train impaired cognition-based tasks by the techniques of shaping.
Detection of Children's Personality with Fingerprint Using K-Nearest Neighbor (Knn) and Decision Tree Methods

Now in many parts of the world technology has been developed that is able to identify individuals from individual biological characters known as Biometrics. Biometrics itself is a way of identifying and verifying individuals based on their physical characteristics or behavior. So fingerprints are an option to detect a child's personality. The desire of parents to print their children into superior seeds is getting bigger. Questions about how to maximize talent, potential, and children from the start often haunt the minds of today's parents. Realizing the importance of this, psychologists continue to perfect tests to analyze children's intelligence and personality. With the occurrence of these problems, this study will design a system that can read fingerprints with the results knowing the child's personality and Learning style. This system is designed by using the Gray Level Co-Occurance (GLCM) feature extraction and is classified by the K-Nearest Neighbor (KNN) Method and Decision Tree which can go through a data or a fact that moves forward to a conclusion. In this research, the two classification methods have different accuracy, KNN has an accuracy of 85% and 89% Decision Tree has more accuracy than KNN because it uses a decision tree.
Analisa Sistem Aplikasi Marketplace Facebook Dalam Pengembangan Dunia Bisnis

Trade in the modern era currently uses a system of buying and selling not directly face-to-face but only with one finger and in one right is not limited by space and time which is commonly called online trading and online transactions in one place, namely the website and there are very many types of marketplaces as long as technology continues to develop and changes in the quality of technology continue, in this study the author wants to show the use of the Facebook marketplace in the business world, showing an increase in economic growth for every group and age by utilizing technology provided by Facebook, the results of This research shows the quality of the selection of the Facebook marketplace as a site that is quite busy being used by many people today as the most accessible and friendlier marketplace.