Reviewerly/exHarmony huggingface dataset:
Can Digital Research be an Alternative Method during the COVID-19 Pandemic in Indonesia?

Unprecedented COVID-19 global pandemic entails uncertain conditions, which lead people to seek alternative solutions to make activities running accordingly. Limited movements due to travel restriction and health protection confine people’s activities, including the research process. Postponing research activities is arguably not the best solution for scholars, mainly while conducting data collection. In social and humanities research, researchers mostly undertake data collection through field studies and face-to-face communications prior to the pandemic. Social distancing procedures, however, encourage people to avoid close contacts and cancel visit plans. Thus, researchers are necessary to find an alternative method during uncertainty situation. Digital research method (DRM) seems to be a plausible way to keep research ongoing, although the implementation of that method in the developing countries’ research activities, compared to the developed countries, remains lagging behind. This research aims to position DRM amidst the pandemic situation and show possibilities in using this method as an alternative in the developing countries by taking Indonesia as a case study. Results show that to make DRM works in Indonesia, some requirements need to be fulfilled to meet academic standards, and whether relevant or not the research topic applies to be processed utilizing digital method are also essential to be considered.
Does Summary Evaluation Survive Translation to Other Languages?

The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines.The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations.To investigate how much we can trust machine translation of summarization datasets, we translate the English Sum-mEval dataset to seven languages and compare performances across automatic evaluation measures.We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries.We also consider the effect of translation on the relative performance between measures.We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality.Our code and data can be found at https:
Preprofessional orientation of teenagers by means of computer art

This article examines the system of designing classes aimed at immersion of teenagers in modern professions related to computer art. The relevance of this topic is substantiated by modern trends in socioeconomic development of our country. The development of creative abilities is a good foundation for acquiring new scientific and technical information; it accelerates the processing and generation of novel and useful ideas. The object of this research is preprofessional orientation of teenagers. The subject is the methodology of teaching graphic editors based on the specificity of its use in modern professions. The article employs the modeling, analysis, and generalization. The project laboratory of computer graphics and design at the premises of Lyceum in the town of Balashikha under the Bauman Moscow State Technical University, became the framework for this research. The conclusion is made that project work helps teenagers to acquire strong knowledge in the area of application of modern graphic editors, valuable practical skills and abilities, such as hard work, discipline, work culture, teamwork. In the course of this research, the author examined the possibility of application of modern instrumental and software tools for creating animated videos using the stop-motion technique, as well as developed the instruction on using technical means to achieve the set goals.
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression

A fundamental issue for federated learning (FL) is how to achieve optimal model performance under highly dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge FL server via multiple communication channels (e.g., 4G, LTE and 5G). However, having an edge device send copies of local models to the FL server along multiple channels is redundant, time-consuming, and would waste resources (e.g., bandwidth, battery life and monetary cost). In this paper, motivated by the layered coding techniques in video streaming, we propose a novel FL framework called layered gradient compression (LGC). Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model, and sends the result back to the devices. We prove the convergence of LGC, and formally define the problem of resource-efficient federated learning with LGC. We then propose a learning based algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e.,the compression level of different layers and the layer to channel mapping) in each iteration. Results from extensive experiments show that using our algorithm, LGC significantly reduces the training time, improves the resource utilization, while achieving a similar accuracy, compared with well-known FL mechanisms.
A robust graph-based approach to observational equivalence

We propose a new approach to defining programming languages with effects, and proving observational equivalence. Operational machinery is given by a hypergraph-rewriting abstract machine inspired by Girard's Geometry of Interaction. It interprets a graph calculus of only three intrinsic constructs: variable binding, atom binding, and thunking. Everything else, including features which are commonly thought of as intrinsic, such as arithmetic or function abstraction and application, must be provided as extrinsic operations, with associated rewrite rules. The graph representation naturally leads to two new principles about equational reasoning, which we call \emph{locality} and \emph{robustness}. These concepts enable a novel flexible and powerful reasoning methodology about (type-free) languages with effects. The methodology is additionally capable of proving a generalised notion of observational equivalence that can be quantified over syntactically restricted contexts instead of all contexts, and also can be quantitatively constrained in terms of the number of reduction steps. We illustrate the methodology using the call-by-value lambda-calculus extended with (higher-order) state.
The Effect of the Use of Artificial Intelligence and Operational Risk Management on Banking Performance with the Implementation of Regulation as a Moderation Variable

The Industrial Revolution 4.0 motivates banks to digitalize banking. Banks must innovate using technology and need to pay attention to the business operational risks that arise due to the technology implementation. Several previous studies have described the benefits of AI in banking in qualitative research; not many have proven it empirically. This study aims to analyze the effect of Artificial Intelligence (AI) and operational risk management on banking performance by implementing regulations as a moderating variable. The data was obtained through a survey of 170 bank employee respondents who had implemented banking digitization. The data were processed using the SmartPLS 3.0 program with multiple linear analysis methods. The results show that the use of AI does not affect banking performance.Meanwhile, operational risk management has a positive effect on banking performance. Implementation of regulations has been proven to strengthen the influence of AI on banking performance. However, not for the impact of operational risk management on banking performance. This research has managerial implications for banking that banks need to analyze the importance of using artificial intelligence and active risk management in improving performance. RegTechs (Regulatory Technology) is an urgency to be implemented in anticipation of potential fraud, which can be in the form of knowing your customer (KYC), fraud detection, regulatory compliance, and risk management, done digitally by utilizing technologies such as databases, artificial intelligence to the blockchain. This research also has implications for the government, particularly the OJK and BI, in evaluating banking policies or laws, especially those related to the implementation of banking digitization in Indonesia, regulations related to customer data security and cyber resilience.
Health communication processes in social media: towards a transformative intervention on the information about chronic pain

Chronic pain is a complex condition, and patients who suffer from it have to deal regularly with added problems like disbelief, isolation, or stigmatization. In this respect, social media arose as platforms where these individuals found spaces to freely express the pain they were experiencing, and where they started to form online support communities to share their concerns with other pain patients who were suffering from the same disturbing illness. Accordingly, the purpose of this research is to critically examine the processes of pain communication in social media both from the logic of health-related institutions and from the perspective of chronic pain patients. These exchanges of information are reviewed using two exploratory methodologies on Twitter and Instagram, establishing first the state of the art of social media health communication policies of health-related organizations. After evaluating these processes independently, it is explored how social support theory could improve the interactions between physicians and pain patients in these online platforms. This thesis points out that both stakeholders are using social media platforms differently, and without interacting with each other. However, the principles of social support theory applied to the use of these online spaces are showing opening capabilities to be approached with the purpose of improving the communicative gap between patients and physicians in these networked platforms. This thesis concludes that the use of social media could enhance both the management and assessment of this invisible and poorly communicated condition.
SOME ASPECTS OF BASIC COMPUTER TRAINING OF BACHELORS IN THE CONDITIONS OF TRADITIONAL AND UNCONVENTIONAL METHODS AND FORMS OF TRAINING

The work is devoted to methodological problems of basic computer training of bachelors with a focus on the use of unconventional methods and forms of education.&#x0D; Unconventional methods and forms of basic computer training of future bachelors are formed under the influence of a number of factors, among which today the main ones are the introduction of a competence approach in higher education and the transition to a mixed (fulltime-distance) form of education.&#x0D; In the process of basic computer training, the information and computer and technological competences of bachelors of technical specialties can be formed on the basis of the use of traditional methods and forms of training. For the formation of procedural and activity competence, unconventional approaches are proposed, based on the use of quasi-professional tasks and the orientation to the formation of such general competencies as the ability to abstract thinking, analysis and synthesis in future bachelors; the ability to make informed decisions; ability to apply knowledge in practical situations, etc. The corresponding structure of the main discipline of basic computer training with the allocation of two modules, the main topics of each module and their approximate volumes is proposed.&#x0D; To ensure readiness for the educational process in emergency situations, it is advisable to use the adaptive full-distance learning scheme AFDLS. Regarding the basic computer training of higher education student, the main methodological issue is the determination of rational proportions of the combination of classroom and distance work in the context of certain types of educational work, establishing criteria and procedures for the transition from one form of education to another. To ensure the possibility of promptly changing the calendar plan and eliminating the need for independent study of complex material, it is proposed in the working programs of computer disciplines to allocate blocks of as small a volume as possible that are little connected with each other.
Optimal pebbling of complete binary trees and a meta-Fibonacci sequence

In this paper, we present a new method for determining the optimal pebbling number of a complete binary tree. This method reveals a curious connection between the optimal pebbling numbers of complete binary trees and the Conolly-Fox sequence, a type of meta-Fibonacci sequence.
Improving Eight Graders’ Reading Comprehension Using Student Team Achievement Division (STAD)at SMP Strada Santo Fransiskus

This study employed an action research design which was carried out in two cycles.Conducted from March to May 2015. It aimed at improving students’ reading comprehension skill through Students Team Achievement Division (STAD) technique.The participants were 37 eight-graders of SMP Strada Santo Fransiskus Xaverius III, North Jakarta. During the implementation, quantitative data were collected using tests,and qualitative data were collected using diary notes, questionnaire, and observation sheets. The obtained data were analyzed using the descriptive analysis technique. Theresults revealed that the implementation of STAD technique improved students reading comprehension. It was proved by the increase of the mean scores from the pre-test topost-test 1 and post-test 2, which were respectively 53.51, 64.84, and 77.57. Moreover,the qualitative data obtained through the diary notes, questionnaires, and observationsheet revealed that the participants showed great interest in learning reading through STAD. Based on the finding of the research, teachers are recommended to apply STADtechnique as an alternative to improve students’ reading comprehension.
An Analysis of Co-movement in Equity Sector Indices

This study examines the co-movement among equity sector returns of the Malaysian capital market.The relationship is investigated using Correlation-based on Ordinary Least Square (OLS) and Multivariate-GARCH Dynamic Conditional Correlation (DCC) to examines the volatilities and correlations of sectoral equity indexes.The study uses daily data that ranges from 5 February 1999 to 6 February 2019.The OLS result reveal that there is a strong comovement between sectoral equity and the stock market prices except in tin and mining sector.While time-varying correlations among sectoral indexes are estimated using MGARCH-DCC, the empirical results from this analysis show that the plantation, properties and tin and mining sectors have negative unconditional correlation with the stock market, which is a good sign of diversification advantages.The findings have important implications helping portfolio managers and investors to understand the co-movement of equity sectors and then formulate policy measures that encourage better portfolio diversification.
Batch Normalization Assisted Adversarial Pruning: Towards Lightweight, Sparse and Robust Models

Although deep neural networks (DNNs) have achieved great success in many areas, they are extremely vulnerable to imperceptible adversarial attacks. DNNs can improve robustness through adversarial training at the expense of massive computing and large network capacity, which makes the model difficult to be lightweight and deployed on edge or resource-constrained devices. To solve this problem, we take both the advantages of structured and unstructured pruning to get a lightweight and sparse model. With the fact that adversarial attacks mainly exist in the high-frequency component of images, we analyze the Batch Normalization (BN) layer to find that the magnitude of its scaling factor can identify those high-frequency components. Therefore, we propose to consider the magnitude of the scaling factor for the pruning criterion. Furthermore, unlike existing methods that prune networks only base on weights, we define “effective weight” which considers both the scaling factor and weight magnitude in unstructured pruning. A novel pruning method is proposed by considering the effective weight. We also verify the Lottery Ticket Hypothesis in adversarial pruning methods yet unknown to the adversarial community.
Analisis Aspek Usability pada Website PDAM XYZ Kota XYZ dengan Metode WEBUSE

Website usability is very important because it can determine and know the quality related to how easily the user can use and learn and learn and whether the website runs in sync according to function and does not cause errors at the time of use. Each website has a different level of usability, the increased level of usability means that the website is easier to understand users (users can easily find what users are looking for quickly and not experience problems). This research aims to find out if the website has met the needs of users and know the difficulties experienced by users. This case study researchers chose the PDAM XYZ website of XYZ city. With the implementation of usability, it is expected that PDAM XYZ will know what is needed by users to access the PDAM XYZ website. Research on the website PDAM XYZ XYZ city is using the web usability evaluation tool (WEBUSE), WEBUSE method is a convenience evaluation method in the form of a web-based ease of use evaluation questionnaire to assess the usefulness of the website being evaluated. Researchers conducted an analysis by taking a sample of 20 users of PDAM XYZ Kota XYZ website. Based on the research that has been done obtained good and excellent results on the four variables, obtained the highest variable is the variable Content, Organization and Readability with a score of 0.89, and the lowest variable is Performance & Effectiveness which gets a score of 0.68.
Hofstede’s Cultural Dimensions Theory: Can Researchers Add More Cultural Dimensions?

This research endeavors to justify why tribalism__a sense of belonging to an individual’s tribe, superiority among other tribes, blindly loyal to a tribe, and demeaning outgroups__is a different cultural value different from Hofstede’s initial first four dimensions of 1984 in 76 countries. The author used the bivariate analysis and data from Corruption Perception Index, Tribalism Index, and Hofstede’s Cultural Dimensions’ Study 2001. The results show that tribalism is different from Hofstede’s initial cultural values of uncertainty avoidance, PD, masculinity, and collectivism. The results confirm the author’s argument why tribalism should be added to Hofstede’s Cultural Dimensions Theory. This study validates the concept of tribalism as a testable dimension by testing the relationship between tribalism and corruption using linear regression analysis. Researchers can use tribalism as a new cultural value that differentiates societies from one another.
Implementasi IoT dalam Otomasi Pengontrolan Kondisi Lingkungan dan Pemberian Pakan: Efeknya Terhadap Parameter Efisiensi Peternakan

Nowday 35% of Indonesians buy poultry products every week. However, 95% of Indonesia farms are small open houses often built from bamboo using manual feeding and watering systems. The problem related to the manual feeding and watering system is the high mortality rate of poultry, which is an average of 8%. The use of IoT (Internet of Things) in agriculture and livestock is increasingly popular. This research implemented IoT on a poultry farm to automatically control environmental conditions based on sensor readings and measure feed level and water consumption. It also monitored chicken weight via a digital scale. This study also measured the farm efficiency parameters before and after IoT implementation, namely FCR, IP, and mortality rates. The results showed that the performance of IoT in poultry farms could increase efficiency. After IoT implementation, the FCR value decreased from 1.46 to 1.38, which led to an increase in the FE value to 72%. The IP score increased to 391.1. The mortality rate of chickens decreased from 3.67% to 2.35%. Also, the average weight of chickens at harvest is 1460gr. the mortality rate of chickens decreased from 3.67% to 2.35%. Also, the average weight of chickens at harvest is 1460gr.
ANALISIS PEMBELAJARAN DARING PADA MATERI SIFAT DAN PERUBAHAN WUJUD BENDA DI KELAS V SDN KEDALEMAN IV CILEGON

The covid-19 pandemic is turning the originally face-to-face education system into online learning. Online learning is one form of educational innovation that answers the challenges of the availability of varied learning resources and adaptation of learning activities to achieve academic goals. Therefore, during the pandemic, online learning is necessary for learning to continue at the elementary school level. The purpose of this study is to know the planning, implementation, and evaluation of online learning activities on materials of the nature and changes of the shape of objects in 5th grade of SDN Kedaleman IV Cilegon in the 2020/2021 school year. This study uses a qualitative approach and case study method. The subjects of this study were four students and four parents in 5th grade of SDN Kedaleman IV Cilegon participated in online learning activities. Data collection techniques include in-depth interviews, non-participant observations, and documentation. This study's data analysis techniques include data collection, data reduction, data presentation, and conclusion drawing. The results showed that online learning in 5th grade of SDN Kedaleman IV Cilegon has the following stages: Planning stages such as determining the application used, creating WhatsApp groups, preparing learning materials, tools, and supporting materials in online learning, choosing learning methods and media. The implementation stage involves preliminary, core, and closing activities by using facilities in supporting online learning activities, namely virtual face-to-face and LMS. The evaluation phase consists of an evaluation of tests (assessment of knowledge with an assignment of questions and assessment of skills with practical tasks) and evaluation of online learning implementation programs (advantages and disadvantages and constraints and solutions)
To be Closer: Learning to Link up Aspects with Opinions

Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 dataset.
Research Guides: Understanding Predatory Publishing: Evaluation Tools

This guide is designed to increase awareness of Predatory Publishing practices, guidelines on how to avoid, and how to seek assistance from information experts.