Reviewerly/exHarmony huggingface dataset:
Antecedents of Cloud Computing Adoption in the Malaysian context: A Systematic Literature Review

In a competitive marketplace, the competitiveness and survival of any corporation are often attributed to its ability to adopt innovative technology which bestows a competitive edge and reduced costs, improves the quality and the efficiency of its business processes. Cloud computing is a platform for the development of computational solutions for multiple fields of knowledge, as it offers cost-saving mechanisms and increased efficiency to organisations. Despite its maturity and enhancement, reviews pertaining to antecedents of cloud computing adoption in the Malaysian context are scarce. Thus, a systematic literature review usingScopus database for retrieving the related-articles was carried out to shed light on the antecedents of cloud computing adoption in the Malaysian context. The review revealed that the antecedents influencing cloud computing adoption includes technological context elements (relative advantages, technological readiness, cost saving and compatibility), organisational context elements (top management support) and environmental context elements (competitive pressure, external support/trading partner support/regulatory support, vendor reputation and trust). Understanding the antecedents of cloud computing adoption is crucial towards strengthening cloud computing adoption and, in turn, will improve the performance and competitiveness of corporate sectors.
SCHOOL BASED AND SOCIAL MEDIA SUPPORTED MICRO TEACHING APPLICATIONS FROM THE PE

Micro Teaching (MT) are applications that are carried out with peer and expert supported in order to support the professional development of Pre-service Teachers (PT) and have a unique value in teacher training.These applications offer PT unique experiences as they provide opportunities such as monitoring and evaluating their own performance, learning in collaboration with their peers and receiving continuous and instant feedback from their peers and field experts.The purpose of this research is to examine the professional experiences of PT' in this process by providing the opportunity to test the contents they designed and implemented in MT courses, in real school environment and on students.MT applications related to the course were carried out in the faculty.The contents prepared in this course were applied to first-year students in a primary school in the faculty region.Student performances during the applications were recorded with a video camera and shared with the PT through a closed group created in a social media application.PT made evaluations about themselves and their peers by watching these performances.The study group of this research, which was carried out in the case study pattern, which is one of the qualitative research techniques, is PT studying at the Faculty of Sports Sciences at MCBU, choosing the MT course (n=16).The data of the research were obtained from the daily notes obtained from the PT and the focus group interviews held at the end of the applications.The data were subjected to content analysis with the support of two experts who were not involved in the research.As a result of the analysis, under the main theme of "teacher qualifications", the experiences of PT were examined in three sub-dimensions: "professional skills", "social skills" and "emotional states".The data obtained indicate that these social media supported and school-based applications have positive results on the professional competencies and social skills of PT.In addition, it is understood from the statements of the PT that the experiences they had within the scope of the research allowed them to get to know their profession and students more realistically.
Human Gesture Recognition with a Flow-based Model for Human Robot Interaction

Human skeleton-based gesture classification plays a dominant role in social robotics. Learning the variety of human skeleton-based gestures can help the robot to continuously interact in an appropriate manner in a natural human-robot interaction (HRI). In this paper, we proposed a Flow-based model to classify human gesture actions with skeletal data. Instead of inferring new human skeleton actions from noisy data using a retrained model, our end-to-end model can expand the diversity of labels for gesture recognition from noisy data without retraining the model. At first, our model focuses on detecting five human gesture actions (i.e., come on, right up, left up, hug, and noise-random action). The accuracy of our online human gesture recognition system is as well as the offline one. Meanwhile, both attain 100% accuracy among the first four actions. Our proposed method is more efficient for inference of new human gesture action without retraining, which acquires about 90% accuracy for noise-random action. The gesture recognition system has been applied to the robot's reaction toward the human gesture, which is promising to facilitate a natural human-robot interaction.
Multi-photon-contributed enhanced visibility of spectral two-photon quantum interference between thermal-gradient states of light

Spectral Hong-Ou-Mandel effect is experimentally revealed between a thermal field and a heralded state. We demonstrate that multi-photon components in heralded state get engaged in quantum interference which is manifested in improved visibilities for certain mean photon numbers of the thermal field. A theoretical relationship is derived for the visibility, showing its compound dependency on the mean photon number per pulse period within the thermal field and the thermal part of the heralded state. Relying on pulsed excitation, we demonstrate an experimental approach for time-efficient acquisition of visibility. The results of this research are of potential application to high-speed remote secret key-sharing and combating the security gaps arising from multi-photon contamination.
Twitter's move on API will fuel criticism of platform

Headline INT: Twitter's move on API fuels criticism of platform
Meeting the Challenges of Large Online Graduate Cybersecurity Classes in the Age of COVID

Designing curriculum and teaching delivery programs that can meet the needs of specialized groups of employers and students is challenging in the best of times. When extra criteria are added, such as making a degree program fully online when also limited with the number of fully qualified faculty due to constrained resources, flexibility is a requirement. This is a case study of one such program development project that saw the design and development of a Master-level program of study in Cybersecurity that was designed at one level of expected faculty resource availability that had to rapidly evolve in a new direction due to significant resource restrictions. Built on a model of maximizing the productivity of a few fully qualified faculty by leveraging less qualified but very capable part-time staff to meet the needs of online delivery of large sections of graduate instruction.
Did AI get more negative recently?

In this paper, we classify scientific articles in the domain of natural language processing (NLP) and machine learning (ML), as core subfields of artificial intelligence (AI), into whether (i) they extend the current state-of-the-art by the introduction of novel techniques which beat existing models or whether (ii) they mainly criticize the existing state-of-the-art, i.e. that it is deficient with respect to some property (e.g. wrong evaluation, wrong datasets, misleading task specification). We refer to contributions under (i) as having a 'positive stance' and contributions under (ii) as having a 'negative stance' (to related work). We annotate over 1.5 k papers from NLP and ML to train a SciBERT-based model to automatically predict the stance of a paper based on its title and abstract. We then analyse large-scale trends on over 41 k papers from the last approximately 35 years in NLP and ML, finding that papers have become substantially more positive over time, but negative papers also got more negative and we observe considerably more negative papers in recent years. Negative papers are also more influential in terms of citations they receive.
Teaching Offensive and Defensive Cyber Security in Schools using a Raspberry Pi Cyber Range

Computer Science as a subject is now appearing in more school curricula for GCSE and A level, with a growing demand for cyber security to be embedded within this teaching. Yet, teachers face challenges with limited time and resource for preparing practical materials to effectively convey the subject matter. We hosted a series of workshops designed to understand the challenges that teachers face in delivering cyber security education. We then worked with teachers to co-create practical learning resources that could be further developed as tailored lesson plans, as required for their students. In this paper, we report on the challenges highlighted by teachers, and we present a portable and isolated infrastructure for teaching the basics of offensive and defensive cyber security, as a co-created activity based on the teacher workshops. Whilst we present an example case study for red and blue team student engagement, we also reflect on the wide scope of topics and tools that students would be exposed to through this activity, and how this platform could then be generalised for further cyber security teaching.
Analysis Of Product Price For Creative Economic Business Yogyakarta Special Region Using Data Scrapping

The government in the National Economic Recovery (PEN) launched a strategy to increase business activity, namely by maximizing the potential for product sales through E-Commerce. E-Commerce sites in Indonesia display product information with different descriptions and prices. Through the pages on E-Commerce can collect data to form a useful information. Collection of data variations requires a method or system that can automatically collect the data provided on the Web page. In addition to data collection, another thing that needs to be considered is the meaning of knowledge from the collection of information that has been obtained. For these data to be useful, it is necessary to design a place for data management. Data must be selected and arranged properly so that it is easy to use so that it gives a good meaning. This research requires methods and tools in the process. The method used is Web Scrapping while the tools used are Tableau. There are two objectives in this research. First, obtain product price data for creative economy entrepreneurs in the Special Region of Yogyakarta through E-Commerce, especially Tokopedia and Bukalapak. Second, present product price data visually to facilitate the process of price distribution analysis so that it can provide information to local governments, especially product managers from creative economy business actors to support strategic policies in determining product prices from creative economy business actors in the Special Region of Yogyakarta to support promotion and tourism.
Analysis of the Effect of the Diagnostic Failure Rate According to the Segment Setting in the Vibration Data Feature Extraction Process

With the advent of the Fourth Industrial Revolution, PHM technology is being developed to diagnosis failures of mechanical equipment and predict the remaining useful life of equipment. Acquiring quality data at a major step of PHM is an important process that has a significant impact on future diagnosis and prediction processes. In the process of acquiring and preprocessing data, the feature extraction process is a process of extracting meaningful features of the data. This study analyzed the effect on the failure diagnosis rate according to the segment setting in the feature extraction process of vibration data. Therefore, the noise of the vibration data was removed, features were extracted for each segment, and the failure diagnosis rate was calculated using a classification algorithm. Based on the finally calculated results, the performance of the classification algorithm was evaluated using F1 - Score. As a result of the final analysis, the process of setting the segment size of the vibration data is also important, but the process of applying the noise reduction methods and feature values suitable for the characteristics of the data is more important.
Recognizing Facial Expressions Across Cultures Using Gradient Features

The goal of this research is to provide a useful technique for better facial emotion recognition, especially across cultural boundaries. Although people communicate both verbally and nonverbally, face expressions are crucial in determining verbal communication. The previous human-computer interface did not take into account thus much nonverbal communication. We need a system that can recognise and comprehend the intentions and feelings expressed by social and cultural cues. In this article, we present a technique for categorising facial photos into six different categories of expressions. Three phases make up the approach; in the first, we used viola Jones to edit off all but the face from the original image and create new ones. Then a HOG histogram was used to extract gradient characteristics. Last but not least, we used SVM to classify picture characteristics and got encouraging results. Comparing the outcomes of the suggested method to other cutting-edge approaches, they are astounding. With regard to combined cross-cultural datasets, it offers accuracy of 99.97%.
Grammatical Relations and Semantic Roles of Arguments in English Verbs of Transfer “Convey”

This study entitled Grammatical Relations and Semantic Roles of Arguments in English Verbs of Transfer “Convey”. It focused on grammatical relations and semantic role of argument within a certain argument structure. The particular and only data source of this study is Corpus of Contemporary American English. This study used accidental sampling method. Therefore, every data found are constructed with transfer verb “convey” proposed by Levin. Base theories of the analysis are Grammatical Relation and Semantic Role by Kroeger, while it supported by theory of Proto-Roles by Dowty and Taylor. The data representation chosen are presented both in formal and in-formal method. There are 3 different argument structures found within the 100 samplings. Transitive Structure turn out to be the most dominant structure, while the other which are intransitive and ditransitive are reasonably represented. All the grammatical relations are found, they are Subject, Object, Oblique, and Adjunct. In the other hand, all the semantic roles are found except stimulus, beneficiary, and accompaniment. Terms proto-agent and proto-patient supports the mapping of the semantic content. Some roles such as theme and instrument have different lexical understanding. It divided into three terms; physical, perceptive, abstract entities. Specifically, the agent classified into three different indication they are non-symbolic agent, metaphorical agent, abstract agent.&#x0D; &#x0D; &#x0D; &#x0D;
Bentuk dan Penyimpangan Penggunaan Youjigo dalam Anime Mirai Karya Mamoru Hosoda

This research is entitled as Bentuk dan Penyimpangan Penggunaan Youjigo Dalam Anime Mirai Karya Mamoru Hosoda. This research was made to find out the form of use and usage deviation of youjigo in anime Mirai by Mamoru Hosoda. The method that used in this research is qualitative descriptive method. Yakuwarigo theory’s by Kinsui (2003) which helped by Okazaki (2010) about youjigo’s characteristics and Tarou (1975) about youjigo’s suffixes form is used to identify the form of use of youjigo in anime Mirai. Meanwhile, SPEAKING theory by Hymes (1972) is used to identify the usage deviation of youjigo in anime Mirai. Data source of this research is qualitative data such as dialogues and monologues from characters Kun, ibu ketika masih kecil, and ayah. The result of this research found that the form of use of youjigo in anime Mirai is omission, dropping o and ga particles, and suffixes ­-no, -yo, -no ne, -yo ne, and -mon. Afterwards, the usage deviation of anime Mirai was used by ayah when speaking with Kun. Usage deviation of youjigo was used to facilitate the delivery of information to children.
Penerapan Jaringan Saraf Tiruan / JST (Backpropagation) untuk Prakiraan Cuaca di Bandar Udara Radin Inten II Lampung

Weather prediction is needed in planning daily life, one of which is to make decisions. The success of a weather prediction will have an impact on decision making in various fields, including agriculture and aviation. In the field of aviation, weather prediction is important to determine the time, location, direction of motion, altitude and plan the movement of aircraft to take into account operational disturbances that can be caused if the weather is bad and also to consider in determining flight routes or determining in carrying additional fuel if in an emergency. In the case of the aircraft having to return due to unfavorable weather conditions. Therefore the need for a good weather prediction method so as to reduce losses and damage. In this case the author tries to focus on the maximum parameters in the development of weather forecasting information design based on Artificial Neural Networks / Backpropagation by adding input data of rainfall, temperature, humidity, sunlight, air pressure, wind direction and wind speed. This research was conducted in the area of Radin Inten II Airport, Lampung. The material used in this study is in the form of daily data on meteorological conditions in the Radin Inten II Lampung Airport area from the Radin Inten II Meteorological Station for the last 3 years, from 2017 to 2019. This data is needed as input data for the algorithm that will be used in study. Based on the research results, the best training accuracy is 100% on the artificial neural network architecture with levenberg-marquardt training function parameters (trainlm) and scaled conjugate gradient (trainscg), binary sigmoid and bipolar sigmoid activation functions, and the number of neurons 20, 40, 60, 80, and 100. Meanwhile, the best test accuracy is 74,359% on the artificial neural network architecture with the training function parameters gradient descent wit momentum and adaptive learning rate (trainingdx) and binary sigmoid activation function (logsig) and the number of neurons 20 and 80.
PENGKLASIFIKASIAN STATUS GIZI BALITA MENGGUNAKAN ANALISIS DISKRIMINAN

Nutrition is one of the risks that trigger morbidity and mortality.Malnutrition, especially at an early age, will increase infant and child mortality and have short stature as adults.The standard that can be used to measure chronic nutritional status is to classify the nutritional status of toddlers based on height according to age, one of the methods that can be used in this classification is discriminant analysis.Discriminant analysis is a statistical analysis used to classify individual status into classes or groups based on a set of variables.The classification of the nutritional status of children under five is categorized into stunting and normal groups based on the variables under five, birth length, birth weight, anemia status and mother's age during pregnancy using discriminant analysis on secondary data taken in 2015 as many as 116 samples, with birth weight 3 as an indicator of participation in model with a classification accuracy rate of 65.6% on training data and 74% on testing data.
PENERAPAN METODE ANFIS UNTUK MEMPREDIKSI NILAI TUKAR DOLAR AS (USD) TERHADAP RUPIAH

The purpose of this study is to explain the prediction of the exchange rate of the United States Dollar (USD) on the Indonesia Rupiah (IDR) using the Adaptive Neuro Fuzzy Inference System (ANFIS) method.ANFIS is a combination of functions contained in fuzzy logic and neural networks.The data used is data with daily period of the USD exchange rate against the rupiah.The input to the daily prediction is a combination of the exchange rates on the previous days for the prediction of the selling rate of the United States Dollar (USD) for the next one day.The results of this study, the best model ANFIS uses a membership function Gaussian with model 3 consisting of 4 inputs and 1 output with a combination of training data and test data 80:20 or 202 data training and 51 data testing resulting MAPE is 3.74% and from the MAPE value it can be concluded that predictions using the ANFIS can be categorized as very accurate.
The Application of Sentiment Analysis on Customers Responses in Social Media (Case Study: Indihome and First Media Internet Service Provider)

The development of technology in industry 4.0 era leads to a digitalization process which is marked by the emergence of the internet as one of the important enablers. Indihome and Firstmedia are the most popular Internet Service Providers) ISPs in Indonesia in providing internet services. These two companies certainly have competition in winning the hearts of customers. One of the important factors in this business competition is customer response in the form of reviews which are usually done through social media tools such as Twitter. For this reason, it is important to study how the customer responds in the form of a given sentiment. Sentiment analysis can be a way to study customer responses to products and services. Sentiment Analysis is a method that is part of Big Data in Data Science which is useful for assessing sentiment in the form of positive or negative sentiments. The data collected was 1000 tweets for each ISP which will be processed with the help of Rstudio. The processing results obtained 177 positive tweets, 489 neutral, and 334 negative tweets for indihome while firstmedia obtained 620 positive tweets, 222 neutral tweets, and 158 negative tweets. The result is firstmedia ISPs get a better positive response and the number of complaints is less than indihome. Internet service providers (ISPs) can improve product and service quality by creating a single database system and also implementing the service design blueprint.
Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos

Fish is one of the foods that are often consumed by humans to complete the protein in the body. Indonesia is rich in animal protein so some rogue traders to avoid losses due to rotten fish, sellers process fish to make it look fresh by fishing with formalin liquids so that buyers think the fish is still fresh, this problem is often found in the market so that it takes the right solution. The solution offered is to apply Machine Learning (Boosting Algorithm) to classify fresh fish and fish that have been planned with formalin by utilizing the extraction of GLCM features. The findings of this study indicate a variation of boosting algorithms can provide solutions to this problem. Accuracy, precision, recall, f1-score, f2-score, and Jaccard score in tilapia fish with a value of 0.95, 0.95, 0.95, 0.95, 0.95, 0.95 this result is obtained by extreme gradient boosting variations with the highest achievements compared to variations Other things are also similar to Tamban fish with a value of 0.78, 0.775, 0.78, 0.825, 0.77, 0.632. The effect of boosting algorithm on the measurement of model performance looks very increased in tilapia fish while in tamban fish do not provide maximum results, but these findings are better