Reviewerly/exHarmony huggingface dataset:
Perbandingan Algoritma C4.5 dan Naive Bayes Dalam Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Daring

Since the spread of Covid-19, online learning is considered the main solution for universities so that the lecture process continues to run well, but in online learning there are many obstaclesfaced by students and lecturers, as for the obstacle, namely the location of the house is not covered by the internet network, student behavior is difficult to understand. monitored and absorption of material is very lacking and the obstacles faced by campus are the difficulty of knowing how big the level of student satisfaction in online learning is, on this problem, the author wants to evaluate the online learning system by first classifying the level of student satisfaction with online learning using the C4.5 algorithm. , based on the results of data processing that is deemed less convincing, data processing is also carried out using another classification method, namely the Naive Bayes method. Based on the results of the comparison of the two algorithms, the accuracy of the algorithmNaïve Bayes is better than C4.5 by a difference of 11.77%. That the accuracy rate of the algorithmC4.5 is 58.82% with the validity test using cross validation obtained the average value for the value of Class Precision Dissatisfied 0%, Class Precision Satisfied 66.67%, Class Precision Unsatisfied 0%, Precision Class Very Dissatisfied 0%. And the value of Class Recall Dissatisfied 0%, Class Recall Satisfied 90.91%, Class Recall Unsatisfied 0%, Class Recall Very Dissatisfied 0%. While the level of accuracy using the AlgorithmNaïve Bayes with the results of 70.59% with the validity test using cross validation obtained the average value for the value of Class Precision Dissatisfied 0%, Class Precision Satisfied 76.92%, Class Precision Unsatisfied 33.33%, Class Precision Very Dissatisfied 100% and Class values Recall Dissatisfied 0%, Class Recall Satisfied 90.91%, Class Recall Less Satisfied 25%, Class Recall Very Dissatisfied 100%.
Evaluation of the Solar Energy Nowcasting System (SENSE) using Multiple Ground Based Measurements

&amp;lt;p&amp;gt;Solar energy nowcasting is a &amp;amp;#160;valuable asset in managing energy loads and having real-time information on solar irradiation availability. SENSE is an operational-ready system which exploits satellite retrieved images from the EUMETSAT&amp;amp;#8217;s Meteosat Second Generation for cloud microphysics information above a certain location, modelled aerosol optical properties data from the Copernicus Atmospheric Monitoring Service (CAMS), in conjunction with Radiative Transfer Model (RTM) simulations in order to retrieve the Global Horizontal Irradiance (GHI) and the Direct Normal Irradiance (DNI) spectrally (280 - 2800 nm). For a full year period (December 2020-December 2021) a campaign was held in Athens (Greece) in the framework of the &amp;amp;#8220;Atmospheric parameters affecting Spectral solar Irradiance and solar Energy&amp;amp;#8221; (ASPIRE) project. For the needs of the campaign a number of ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, a BREWER and a PANDORA spectroradiometer and a Precision Spectral Radiometer (PSR).&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;In this study, we evaluate the integrated outputs of SENSE in W/m2, at 15-minute frequency against the ground-based recordings, for the period of the campaign. A separate assessment of the SENSE&amp;amp;#8217;s inputs (cloud and aerosol optical properties) is performed in respect to ground-based retrievals. Finally, deviations between SENSE and measurements are linked to deviations between climatological/satellite-based atmospheric parameters and those retrieved from the ground-based measurements. The detailed evaluation of SENSE inputs and outputs using detailed ground-based data advances our present-day knowledge about the uncertainties of SENSE products, thus improving our understanding of the renewable energy merit in the total energy use for Greece. This effort is important for SENSE users such as the Greek Independent Power Transmission Operator and the Public Power Corporation Renewable of Greece.&amp;lt;/p&amp;gt;
Blended Learning, The International Chinese Learning Trend In Post-Pandemic Era

In the early days of the Covid-19 pandemic, Chinese online learning faced many challenges, including (1) poor internet connection and high internet costs; (2) low computational literacy of teachers; (3) limited interaction between teachers and students; (4) inappropriate teaching materials and methods; and (5) the lack of student discipline and the difficulty of providing assessments. However, these challenges have turned into opportunities for the rapid development of online Chinese learning over time. Academics in China predict that online Chinese learning will be maintained in the post-pandemic era, the blended learning will become a learning trend that is applied globally. This research is descriptive qualitative research with data collection techniques through literature study method and Forum Group Discussion (FGD). The result of this study is a description of the solution to the challenges of Chinese online learning globally during the pandemic, blended learning, and its implementation strategy in the post-pandemic era based on the “Chinese Proficiency Grading Standards for International Chinese Language Education” issued in 2021.
Blokzincir Destekli Mobil Robotların Sağlık Hizmetinde Uygulanması: Kullanım Örnekleri, Araştırma Zorlukları ve Gelecek Trendler

Using service robots in healthcare is gaining importance in case of emergent situations like pandemics where human labour is considered risky. Multi robot systems of mobile robots have the potential to perform simple but vital tasks in healthcare. However, centralized control with a server computer of these systems carry the risks of single point of failure and ineffective operation of robots, thus decentralized control with blockchain integration offers a better solution. We mention research challenges regarding blockchain powered multi robot systems of mobile robots from use case, blockchain technology and its integration into current computing systems used in medical centers aspects. Then we propose a method for decentralized management and task distribution in a multi robot system by using Hyperledger Fabric as a permissioned blockchain platform and give common use case scenarios. In this system, tasks are assigned to robots depending on the selection of nearest available robots to the task target. Each robot runs the smart contract containing the task assignment method, so that data traffic for the task assignment process is distributed among the network, instead of stacking up on a single line as in a centralized system. Future research issues and directions for future works are also stated as a conclusion.
Separable Reversible Watermarking in Encrypted Images for Privacy Preservation

We propose a separable, reversible watermarking scheme in encrypted images for privacy preservation. The Paillier cryptosystem is used for separable detection and decryption. Users may want to use cloud services without exposing their content. To preserve privacy, the image owner encrypts the original image using a public key cryptosystem before sending it to the cloud. Cloud service providers can embed the watermark into encrypted images by using a data-hiding key without knowing and destroying the original image. Even though the cloud service providers do not know the original image content, they can use the data-hiding key to detect the watermark from the encrypted image for authentication. Besides, the image owner can use the private key to directly decrypt the watermarked encrypted image to get the original image without any distortion due to its homomorphic property. Experimental results show the feasibility of the proposed method, which can provide efficient privacy-preserving authentication without degrading security.
The Problem of Guidance the Integral Funnel to the Target Set

We consider a parameter-dependent nonlinear control system on a finite time interval. The problems of guidance of an integral funnel on a target set in a two-dimensional Euclidean space are studied in a soft statement. The integral funnels are controlled by choosing a constant parameter present in the system and a starting point from a set of possible initial states. It is proved that the choice of the initial point can be reduced to the choice of a vector constant parameter in a control system that has only one fixed initial state.
Microscale Discrete Element Model for Simulating Bridging Behavior of Fractured Glass Laminates

The bridging behavior between fragments is one vital factor in the post-fracture stage of glass laminates. A particle-based discrete element model was developed in this work to simulate the bridging behavior of fractured glass laminates. The model was based on the calibration of hyperelastic PVB material properties using soft-bond model and further validated with the experimental data as well as the observations from through-crack-tensile tests. In order to investigate the bridging performance of fractured glass laminates with multiple fragments under uniaxial tension, three factors encompassing the crack aligning, fragment number and adhesion conditions were considered to perform a parametric study. The results give an exploratory application of the developed discrete element models in the estimation of the post-fracture behaviors. It is found that the smaller fragment size can diminish the tension-stiffening effect in the non-aligned case which might generate an enhancing ratio of initial modulus up to nearly 3.6. The non-aligned cracks can also lead to a very high strength if the adhesion ability can be guaranteed. However, the delamination of fragments is expected to control the post-fracture strength of fractured glass laminates with PVB interlayer.
Soaking up the Sun: Solar Energy Optimization during Pandemic, Study Case at Micro, Small and Medium Enterprises (MSME) Rattan Crafts Center in Trangsan Village, Sukoharjo, Central Java

The National Energy General Plan has set a target of achieving the Renewable Energy mix by 23% by 2030, to achieve this the transition of energy to RE must be done immediately. Solar energy is one of the opportunities of renewable energy that is very abundant in Indonesia. Along with the issuance of the Minister of Energy and Mineral Resources Regulation number 49 of 2018 concerning the Use of Rooftop Solar Power System by Consumers of PT Perusahaan Listrik Negara (Persero) which provides opportunities for the utilization of PV Rooftop for household and commercial sector. In view of this, the Central Java Government plans to install Rooftop Power Plant in the Micro, Small and Medium Enterprises (MSME) Sector to develop the utilization of RE in the productive sector while improving the economic recovery of the Covid-19 pandemic. The use of renewable energy (EBT) is expected to save electricity costs in MSMEs. The results of planning using Helioscope software showed that in the three MSMEs with different PLTS capacities, namely 1 Kwp, 2.04 KWp and 4 KWp can produce annual energy production of 1,191 MWh, 2,433 MWh and 5,352 MWh respectively. After the installation of the PLTS, it was proven that in the first two months after installation energy consumption can decrease to minimum usage.
TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.
Personalised publication recommendation service for open-access digital archives

Increase in the number of open-access academic publications and open-access institutional academic archives led more researchers use these archives. No model offers personalised publication suggestions in academic archives. A central service architecture has been proposed towards personalised academic article recommendations for open-access digital archives. Thus, it has been possible to make personalised suggestions for open-access digital archives and enable researchers to discover new publications. A service based on the centralised micro-service architecture model was proposed in the study. Also, TF-IDF and article classification methods were used together for the personalised publication suggestion system. For the first time globally, the proposed method was used with 1464 real users in 49 open-access archives. F-measure success value was found to be higher than 0.8 for recommended publications.
Utilization of a Web-Based Geographic Information System for Land Mapping and Some Its Overview: A Case Study in Sukabumi District, Indonesia

Land used for residence and agriculture is a source of life, especially in Indonesia. This is because most of the sources of livelihood, especially those living at the sub-district level, are mostly engaged in agricultural activities. Therefore, land data collection is very important, starting at the sub-district level. Unfortunately, the information system that supports this does not yet exist, especially in the Sukabumi district. Hence, this study to overview and use a web-based Geographic Information System (GIS) for land mapping, especially in Sukabumi District, Indonesia. The method used in this study was to conduct interviews with land officials at the sub-district level and several communities in the area. Information system problems from potential users are then addressed by designing a web-based information system and integrating it with GIS applications. The developed information system provides menus and sub-menus adapted to the needs of potential users. So far, the information system has been accessible and can help land officials at the sub-district level and several communities in the area. This will provide the level of detailed information that local communities need in responding to information related to their lands to minimize social conflicts.
Facilitation of Customer Empathy: The Effect of Robot Apology on Customer Reaction Following a Service Failure

Despite the rapid increase in the employment of robots, robot service failures are inevitable. Previous studies advocate human employees to provide service recovery rather than robots themselves. Few marketing studies have demonstrated how robot service recovery affects customer reactions. This study examined the effects of service recovery provided by robots (i.e., robot apology) on customer satisfaction and the underlying psychological mechanism. Based on two experimental scenarios, we argue that robot apology significantly influences customer satisfaction through the mediation of customer empathy after a service failure. In addition, the effect of robot apology is moderated by customers’ sense of power. This research offers theoretical implications for robot service recovery and practical implications for firms employing service robots.
A BRIEF REVIEW ABOUT BIOMETRICS SYSTEMS IN MODERN CONTEXT

Authentication systems employ various access mechanisms for the data validation process. Nowadays there are many proposals focused on solving the traditional problems that exist to validate the records of installations in public or private places. With the appearance of COVID-19, the use of technology has intensified to avoid contact with physical devices and achieve successful access. This article shows a compilation of work dedicated to methods and techniques for access to facilities by reading modern biometric systems.
Development of A Hybrid Mobile App for Student Management System

In the event that the school or college has a large number of employees and students. Courses, student requirements, and time management are all issues that managers face on a daily basis. As a result, the proposed system in this study aims to create a student management system that utilizes a hybrid mobile application to manage student registration activities in schools. Flutter framework and Firebase database were used to create the hybrid app. This project can assist teachers, managers, and students in any school or institute in setting, selecting, and identifying a suitable time for their courses, as determined by the schedule. This project also provides significant benefits to managers in terms of managing student registrations and information.
THE IMPACT OF SELF-EFFICACY AND CREATIVE PROCESS ENGAGEMENT ON INNOVATIVE WORK BEHAVIOUR IN THE TELECOMMUNICATION INDUSTRY IN MALAYSIA

&lt;p&gt;This study aimed to illuminate the influence of self-efficacy and creative process engagement on employees' innovative work behaviour in the telecommunication industry in Malaysia. To determine whether these two variables are linearly related, the researchers have applied the quantitative method to test the hypothesis developed for this study. The correlation procedure was used to test the strength of association, and the linear regression procedure was used to test the relationships between the quantitative variables. The population of this study was 45,093 employees working for 214 telecommunication employers in the southern and western regions of Malaysia. A self-administered survey was utilised to gather the data for this research. A total of 424 respondents provided the data to derive the analysis and conclusion for this study. It was shown that self-efficacy directly influences employees' cooperation and efforts, indicating that self-efficacy has a motivating potential for innovative work behaviours. Innovative work behaviour requires strong self-efficacy and involvement in the creative process. Commitment is a huge concern since the causes and consequences of inventive working behaviour may differ greatly depending on whether the emphasis is on self-efficiency or creative process engagement. It seems from the outcomes of this study that self-efficacy and participation in the creative process may help increase creativity and innovation. According to this research, innovative thinking is more likely to be associated with employees who are confident in their abilities to execute challenging tasks and multitask. Employees' capacity to innovate will improve if they have a sense of self-efficacy and participation in the creative process. This will help employees solve problems, enhance their creativity, and create new ideas.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;JEL:&lt;/strong&gt; L20, L23, L96&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0942/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A CORPUS-BASED STUDY OF LEXICAL COLLOCATIONS OF KEYWORDS FOUND IN ONLINE BUSINESS NEWS ARTICLES

Business news articles are crucial for Business English students since they provide a rich source for learning vocabulary in their field of study and professional communication. This study investigates lexico-grammatical items specific to a specialised corpus of online business news reports. Its overall aim is twofold: 1) to generate a keywords list extracted from the self-built corpus; and 2) to identify lexical collocations of the first ten keywords. Seven hundred online business news articles from BBC news.com were compiled to build a corpus of business news. The data were analysed by the application of Antconc 3.5.9 developed by Anthony (2020). The results derived from the present study through a corpus-based analysis uncovered a list of keywords which were frequently used in online business news where the majority of the keywords were nouns (63.51%), verbs/nouns (8%), adjectives (7%), adjectives/nouns (4%), adverbs (2%), and verb (1%). From the selected top one hundred keywords, the lexical collocations of each keyword were identified with 3 combination types based on the set framework adapted from Benson et al.’s (2010). The obtained combination types include Adjective+Noun (40%), Noun+Noun (35%), and Noun+Verb (20%), respectively. The pedagogical implications and recommendations for future research were also discussed.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0761/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Prediction of Telecommunication Service Providers' Stock Prices in Indonesia to Support The Digital Economy

Telecommunication services are one of the most important aspects to support the sustainable development of the digital economy. The study aims to predict the stock price of the two largest companies in Indonesia in the telecommunications sector during the pandemic. The statistical method used to determine stock price prediction is based on Monte Carlo simulations. From the results, if the MAPE values are less than 10% are said to be accurate and can be used as consideration for investors to support telecommunications service providers in Indonesia.
Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine &amp; Market Topics &amp; Sentiment

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore, we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.