Reviewerly/exHarmony huggingface dataset:
Regression Training using Model Parallelism in a Distributed Cloud

Machine learning requires a relevant amount of computational resources and it is usually executed in high-capacity centralized cloud infrastructures (e.g., data centers). In such infrastructures, resources are shared in a scalable manner through instantiation and orchestration of multiple virtualized services. Emerging trends in machine learning are distribution and parallelization of model training, which allows the execution of model training tasks in multiple distributed computational domains, with the aim of reducing the overall training time. A possible drawback in decentralization of machine learning is that performance latency issues may arise when the computation of training is geographically distributed to nodes with long distance from each other. One way to reduce latency is to utilize edge computing infrastructure, i.e., to distribute computation near the origin of the request. As edge resources can be scarce, it is important to orchestrate the model training in a parallelized manner. To this extent, in order to effectively ease the use of parallelization both in centralized and in distributed scenarios, we propose and implement a concept that we refer to Intelligent Agent (IA). An IA is responsible for instantiating and scheduling of the machine learning tasks (e.g., model training), and deriving inferences. In our solution, model training is distributed to multiple IAs in parallel. Each IA is packaged into a Linux container in order to take advantage of container portability across heterogenous deployments and to reuse existing container orchestration tools. We validate our proposal by deploying and instantiating multiple IAs across a distributed cloud environment, where each IA is accounting for a fixed amount of computational resources. Keywords - Intelligent agent, Model parallelism, Regression training, Intelligent cloud
Role of Public WhatsApp Groups Within the Hindutva Ecosystem of Hate and Narratives of "CoronaJihad"

This article uses the context of the widespread circulation of accounts about “CoronaJihad” in India during the COVID-19 pandemic to examine how public WhatsApp groups that participate in disseminating such accounts function within the ecosystem of hate around Hindutva majoritarianism in the country. The manner in which the WhatsApp platform operates within this ecosystem is mapped through a granular study of three public Hindutva WhatsApp groups; the messages within these groups during the first phase of the COVID-19 lock-down in India were examined during the course of this study. The pattern of messaging within the three groups that contribute to the narrative of “CoronaJihad,” which blames the minority Muslim community for the spread of the virus in India, were analyzed. The article focuses on factors including company policies and the specific sociopolitical situation in the country to understand the circumstances that make WhatsApp’s deep entanglement with the divisive politics of Hindutva majoritarianism in India possible.
Adaptive Learning Rates for Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning (MARL), the learning rates of actors and critic are mostly hand-tuned and fixed. This not only requires heavy tuning but more importantly limits the learning. With adaptive learning rates according to gradient patterns, some optimizers have been proposed for general optimizations, which however do not take into consideration the characteristics of MARL. In this paper, we propose AdaMa to bring adaptive learning rates to cooperative MARL. AdaMa evaluates the contribution of actors' updates to the improvement of Q-value and adaptively updates the learning rates of actors to the direction of maximally improving the Q-value. AdaMa could also dynamically balance the learning rates between the critic and actors according to their varying effects on the learning. Moreover, AdaMa can incorporate the second-order approximation to capture the contribution of pairwise actors' updates and thus more accurately updates the learning rates of actors. Empirically, we show that AdaMa could accelerate the learning and improve the performance in a variety of multi-agent scenarios, and the visualizations of learning rates during training clearly explain how and why AdaMa works.
Memory Stay Or Stray?: Irregular Verbs Learning Using Kahoot!

Even though gamification has been lingering for years now, its popularity has been rising ever since. In fact, across disciplines and contexts, the amplification of 21st century learning has been giving gamification a special place in education. There are numerous studies conducted on the use of gamification among school children, university students or even trainee teachers. Coincidentally, all of these studies have shown that gamification does impact their learning. Hence, as harmless as it seems, any teachers who are not equipped with such knowledge on the benefits of gamification may hinder their learners and themselves from reaching their utmost teaching and learning experience especially when it comes to grammar learning among the younger pupils. Considering this thought in mind, this research was conducted to prove that the use of Kahoot! helps the Year 3 pupils in retaining the English Irregular verbs. This quasi – experimental designed research gathered data from a sample of 35 Year 3 pupils using the pre-test and post-test results. The findings which were analysed descriptively using sample t-test showed that pupils’ performances in the post-test improved over the use of Kahoot! in classroom. In conclusion, this research provides an insight on number of ways that gamification can actually impacted the pupils’ learning; especially in terms of memory retention. It also works as a stepping stone for more researches to study on the effectiveness of using Kahoot! in tackling any part of learning especially among the younger learners.
Improving Machine Translation by Searching Skip Connections Efficiently

As a widely used neural network model in NLP (neural language processing), transformer model achieves state-of-the-art performance in several translation tasks. Transformer model has a fixed skip connection architecture among different layers. However, the influence of other possible skip connection architectures are not discussed completely in transformer model. We search different architectures of skip connection to discover better architectures in different datasets. To improve the efficiency of trying different skip connection architectures, we apply the idea of network morphism to add skip connections as a procedure of fine-tuning. Our fine-tuning method outperforms the best models trained by the same or smaller datasets in WMT'16 En-De, WMT'14 En-Fr and WMT'18 En-De with 226M back-translation sentences. We also make experiment on transferring searched skip connection architectures to new transformer models.
Learning word-referent mappings and concepts from raw inputs.

How do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input? One hypothesis is via cross-situational learning: tracking words and their possible referents across multiple situations allows learners to disambiguate correct word-referent mappings (Yu & Smith, 2007). However, previous models of cross-situational word learning operate on highly simplified representations, side-stepping two important aspects of the actual learning problem. First, how can word-referent mappings be learned from raw inputs such as images? Second, how can these learned mappings generalize to novel instances of a known word? In this paper, we present a neural network model trained from scratch via self-supervision that takes in raw images and words as inputs, and show that it can learn word-referent mappings from fully ambiguous scenes and utterances through cross-situational learning. In addition, the model generalizes to novel word instances, locates referents of words in a scene, and shows a preference for mutual exclusivity.
PENDEKATAN DISCOVERY UNTUK MENINGKATKAN AKTIVITAS BELAJAR DAN PENGUASAAN KOSAKATA PADA SISWA KELAS B2 TK MUTIARA INSANI BANDAR LAMPUNG

The purpose of this study was to describe the improvement of vocabulary learning achievement by applying the discovery approach of B2 grade students of Mutiara Insani Kindergarten, Bandar Lampung. The method used in this research is Classroom Action Research (PTK). The research subjects were students of class B2 even Semester TK Mutiara Insani Bandar Lampung, totaling 20 students. Data obtained from observer observation sheet notes on the learning process planned by the researcher, learning activity sheet, and test sheet to determine learning achievement. The data were analyzed using simple statistics, namely the percentage. The action was carried out from January to March 2019. The results of the study were 65% active cycle 1 learning activity, 75% active cycle II increased 10%, 85% active cycle III, increased 10%. While learning achievement in cycle I completed 65%, cycle II completed 75%, increased by 10%, cycle III completed 95%, increased by 20%, and records of observers who partnered with researchers showed good average criteria. The conclusion of this research is that the application of discovery learning approach can increase vocabulary learning activity and achievement in class B2 students of Mutiara Insani Kindergarten, Bandar Lampung.
Versatile Auxiliary Classification and Regression With Generative Adversarial Networks

One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution. For a successful implementation of conditional generators, the created samples are constrained to a specific class. In this work, a new framework is presented to train a deep conditional generator by placing a classifier or regression model in parallel with the discriminator and back propagate the classification or regression error through the generator network. Special cases for binary classification, multi-class classification, and regression are studied. Experimental results on several data-sets are provided and the results are compared with similar state-of-the-art techniques. The main advantage of the method is that it is versatile and applicable to any variation of Generative Adversarial Network (GAN) implementation but also it is shown to obtain superior results compared to other methods. The mathematical proofs for the proposed scheme for both classification and regression are presented.
El desarrollo de la expresión oral en el contexto multigrado

The improvement of the Cuban Education is a permanent process aimed to solving the problems in the educational system. One of the priorities is directed to the teaching - learning process in rural multigradeschools, with emphasis in the treatment to the oral expression as a basic objective of the subject Spanish Language, which has be systematized in the rest of the subjects of the curriculum. Nevertheless, the diagnosis allowed the corroborationof some insufficiencies manifested in the oral expression of the students in the multigradeschool. The objective of the investigation is the elaboration of a didactic model for the development of the oral expression in the studentsfrom the multigrade 5.-6 classroomin rural sector. This model favorsthe integral preparation of the students, in correspondence with the ideal of primary school.
La competenza pragmatica come strumento inclusivo Uno studio di caso con bambini stranieri di prima e di seconda generazione

The aim of this research is to demonstrate the benefits of home language maintenance among first and second generation migrant children by investigating whether they would perform better than their monolingual peers in their ability to narrate stories. The study was conducted in four primary schools in the Veneto region engaging 49 early elementary aged children with different cultural, educational and migration backgrounds who were asked to perform story-telling and story-retelling tasks. After a brief introduction to the study, the second paragraph tackles the notion of pragmatic competence and its relevance for bilingual education. The third paragraph presents the method of the study as well as the survey procedures, followed by a discussion of the results. The conclusion contains limitations of the study and illustrates a number of pedagogical implications. Positive outcomes of this study should raise teachers’ awareness of the linguistic benefits of bilingualism and promote the concept of pragmatic competence.
The Concept of Computer Crime

Computer crime is a phenomenon of our times, frequently reflected in the media. A study even indicates that the fear of cyber attacks far outweighs that of ordinary thefts or frauds. Criminological research on offenses committed through computer systems are still in the exploratory process. Even those achieved so far tend to change the classical way in which crimes are viewed in the current criminal justice systems. (Amza, 2003, p. 14).
Examining the Dimensions of Organizational Climate on Employee’s creativity in Jordanian Hotels

The objective of this study is to develop and test a conceptual framework which linked the creativity of employees with four organizational climatic factors: the structure, obligation, rewards, and support, as well as the connection between creativity of employees and performance of employees. 432 employees in 18 luxury hotels in Jordan obtained data. The quantitative study is focused on an empirical exploratory factor, a basic linear regression analysis and the validity of the CFA factor solution. Results and recommendations in the study showed that the relationshipbetween employee creativity and employee success affects the creativity of workers through different organizational factors. This study aims to strengthen the organizational environment and results for hotels in the area of hospitality in order to enhance their organizational climate.
영어 학습자 지도에 대한 예비교사의 신념

The current study aims at investigating preservice teachers’ beliefs about teaching English learners and any differences, if any, in the beliefs depending upon whether they take the required courses in the teacher training program. For the study 54 preservice teachers enrolling in a College of Education were asked to draw and describe the situation where a teacher teaches English language learners, and the data was analyzed using the content analysis method. The overall findings revealed that the preservice teachers’ belief in the four categories of teacher’s role, instructional content, student activities, and teaching strategies shown in the pictures and explanation of them was hardly related to whether they take the required courses even though a few minor evidence was found. The teacher education researchers and practitioners need to take into consideration how to construct preservice teachers’ belief in how to teach effectively in language classes based on what they’ve learned in the teacher education courses. (Chungnam National University)
Professional training of future teachers for creative activity

The article explores the essence, structure of the concept of “creativity ”. The activity of a social teacher has been studied. A theoretical model ofpreparing students for pedagogical creativity is presented. The essential result of the Marxist approach to creativity is not so much the receipt of a new product as the creation of a person himself. This confirms that the creative process is considered, the results of which have the character of subjective novelty. The need to develop the personal qualities of a future teacher for a successful creative professional activity has found its embodiment in a personal approach.
Hacking in the Dark

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I Haven’t Been</b> to DAC for a while, so I was happy to learn of the Hack@DAC initiative. This exercise is especially useful for security, the subject of the Hack@DAC highlighted in this issue of DesignTest. Security threats are always going to be distributed in the sense that they can come from anywhere, can attack a product no matter where it is and will be initiated by attackers with a wide variety of backgrounds and skills—wider than any security team can ever hope to have. A distributed defense from giving a wide range of hackers in the good sense the charter of trying to break a system can find flaws before the bad guys do.
Measuring the influence of servant leadership on knowledge sharing (Case Study: Parsian Insurance Company)

Today one of the most important approaches to organization success is utilized proper leadership style which is able to affect on lots of organizational outputs. The effect of leadership on the follower outcomes at work has been extensively established through various researchments on management practices. Leaders depict several different styles and provide guidance to their followers to attain on their goals. The purpose of the current research is to survey the influence of servant leadership on knowledge sharing to attend moderating role of social responsibility. This research is applicable to main view and descriptive rom data collection. The data gathering of tool was questionnaire which library and fieldwork were utilized for its designation. Statistical population includes 880 employees of Parsian Insurance Company from which 268 ones were selected by Cochran sampling formula. Sampling strategy was stratified random. To measure servant leadership like as: agapao love, empowerment, vision, servicing, humility, trust and altruism were utilized. The result of appling to structural equation model in Lisrel software environment illustrated that servant leadership and its dimension affects significantly and positively on knowledge sharing. Meanwhile social responsibility moderates the influence of servant leadership on knowledge sharing.
Workflow Based Prototype for Criminal Investigation in Bulgaria

The basic workflows in the field of Criminal Investigations in Bulgaria which are incorporated in a proprietary information system are presented in this paper. Leading role in the investigation is the prosecutor, but in other countries, this role is more or less consistent with the role of the investigator. EMS monitors the operation of the process and suggests gaps in the work of the officials.
Elementary Science Teachers Adapt Their Practice During a Pandemic

The COVID-19 global pandemic created new challenges for teachers and school systems as teachers were forced to rapidly transition to remote learning using new digital tools and resources. Teaching elementary science in “normal times” is challenging due to issues involving teacher preparation, limited access to materials and lack of administrative support due to emphasis on tested subjects, among others. Using reform-based, inquiry practices is challenging when teaching science face-to-face and even more so in an online environment. Compounded with issues of access and equity, teachers faced many problems with moving elementary science instruction online due to COVID-19. This study reports on the experiences of 10 early career teachers who were graduates of a specialized elementary science concentration. Teachers reflect on the challenges faced, how they adapted, and how they designed new learning contexts to teach science. Teachers report on resources they found beneficial, assess needs for the future, and explain how they worked to maintain a sense of community for their students during this unprecedented critical time.
The Role of Interactive Methods For Improving Students` Communicational Culture of Higher Educational Institutions

The article provides an overview of the importance of interactive methods used in teaching and learning processes in the formation of students' communication skills in higher education, as well as the upbringing of students in a new era, with a free mindset in the use of modern methods of interactive teaching methods
A Longitudinal Study of Static Analysis Warning Evolution and the Effects of PMD on Software Quality in Apache Open Source Projects.

Automated static analysis tools (ASATs) have become a major part of the software development workflow. Acting on the generated warnings, i.e., changing the code indicated in the warning, should be part of, at latest, the code review phase. Despite this being a best practice in software development, there is still a lack of empirical research regarding the usage of ASATs in the wild. In this work, we want to study ASAT warning trends in software via the example of PMD as an ASAT and its usage in open source projects. We analyzed the commit history of 54 projects (with 112,266 commits in total), taking into account 193 PMD rules and 61 PMD releases. We investigate trends of ASAT warnings over up to 17 years for the selected study subjects regarding changes of warning types, short and long term impact of ASAT use, and changes in warning severities. We found that large global changes in ASAT warnings are mostly due to coding style changes regarding braces and naming conventions. We also found that, surprisingly, the influence of the presence of PMD in the build process of the project on warning removal trends for the number of warnings per lines of code is small and not statistically significant. Regardless, if we consider defect density as a proxy for external quality, we see a positive effect if PMD is present in the build configuration of our study subjects.
Research on College Students’ Professional Quality and Cultivation of Innovative and Entrepreneurial Talents

Through the cultivation of college students' professional consciousness, professional knowledge and professional ability, we can improve their professional accomplishment of college students, make them meet the requirements of employers, and improve the employment rate of college students.Through the cultivation of college students' psychological quality and innovative ability, they can become innovative people with the potential of entrepreneurship.
Inside VR Perspective & Immersive Dogma

Virtual Reality technology was first introduced in 1994 for virtual world development without depending onusing headsets to display. Oculus Rift, HTC Vive and Samsung Gear VR are the most popular Virtual Reality (VR)hardware for videogames. Virtual Reality (VR) technology became so popular because it is most commonly used inentertainment applications such as gaming, education, experience, simulations and 3D cinema. Simulation games replicatethe real-world activities but within a controlled environment. For example, Google create a Google Earth VR toexperience every country around the world with Virtual Reality (VR). The purpose of this project is to develop a VirtualReality (VR) to promote a study environmental of University Kuala Lumpur Malaysian Institute of InformationTechnology Building. This research will focus on several features such as immersive, user interface (UI) and userexperience (UX). Also, this research will look into the effectiveness among selected respondent related to 3D Design ofConstruction and VR Application.