Reviewerly/exHarmony huggingface dataset:
Development of E-module Teaching Simulation Tools in SIPEJAR for Teaching Practice of Prospective Teacher Students

Optimizing the practice of teaching simulations for prospective teacher-students is one of the efforts to improve the content, pedagogy, and even the use of technology in learning. This study discusses the development of teaching simulation tools products that are packaged in the form of e-modules. Product development is carried out using the ASSURE model by involving experts, practitioners, and students to test their products in Indonesian language learning courses in elementary schools. The data was collected by using questionnaires and field notes which were processed using quantitative and qualitative descriptive methods. The results of product development show that teaching simulation tools are suitable for use as a medium for learning Indonesian in elementary schools. Packaging in the form of e-module itself is feasible to use in terms of content, format, application, and display. E-module teaching simulation tools can also be applied in the SIPEJAR e-learning at the beginning of the meeting to serve as modeling and provide students' teaching experience before practicing independently.
Fusion of Computer Virtual Reality Dynamic Multi-Dimensional Image Rendering Algorithm in University Smart Practice Venues

Fusion of computer virtual reality dynamic multi-dimensional image rendering algorithm in the university smart practice venues is studied in the paper. The visual simulation rendering tool VP is the world's leading software platform for real-time visual simulation, sound simulation and virtual reality. Virtual simulation system is a distributed system for large-scale complex scenes. It can not complete the virtual reproduction of such a large scene on one server. With these 2 combinations, the computer virtual reality dynamic multi-dimensional image rendering algorithm is designed. Through RTDs/RscAD platform modeling, high-precision I/O boards are used to interact with the actual power electronic control and protection system in real time to realize the core closed-loop test of the power electronic control and protection system, and then, the system is appliedto the university smart practice venues. Through the experimental simulation, the designed model is simulated.
Machine Learning Optimization using Bat Algorithm to Classify Sentiment of Twitter Users

Social-media is a very effective communication media in today’s digital era. Twitter is one of them which widely used by Internet users. Huge number of tweets has encouraged research in the field of text mining, especially in sentiment analysis. Most of sentiment analysis researches which mined data in Bahasa used TF-IDF to assign weight on every word in corpus. This traditional method resulted low accuracy when tested using machine learning methods. In this study, instead of using TF-IDF, we implemented Bat Algorithm to weight every word in corpus. We tested this on Naïve Bayes, Decision Tree and K-NN methods. The result of this study shows that Naïve Bayes, Decision Tree and K-NN methods which classified data weighted using TF-IDF reached accuracy 33.58%, 32.82% and 33.61%, respectively. Afterwards, words in corpus were weighted using Bat Algorithm and tested using the same methods. The test result shows that Naïve Bayes, Decision Tree and K-NN methods reached 39.01%, 76.63% and 66.15% in respectively. It can be inferred that Bat Algorithm usage for weighting words in corpus improves machine learning algorithms to classify sentiment of Twitter users. Moreover, it can be identified that the biggest improvement occurred in Decision Tree algorithm which increased 43.81% accuracy. On the other hand, improvement in Naïve Bayes algorithm is still minor compared to other machine learning algorithms.
The Effect of Programmer Information Security Awareness on the Results of Vulnerability Assessments in Achieving Secure Applications

The existence of application innovation is important for the operations of an organization. Information security is currently a challenge in building secure applications because hacking often occurs in critical or transactional information applications, such as the financial sector. In addition to applying good programming techniques, application programmers must have information security awareness to produce secure applications. Application security vulnerabilities are caused by programmers' lack of information security awareness. In this study, the measurement of programmer information awareness is juxtaposed with testing application security vulnerabilities. Measuring the level of information security awareness using knowledge, attitude, and behavior modeling with Manage Security Services indicators on COBIT 5 as the focus area and testing application security vulnerabilities using OWASP-ZAP according to OWASP TOP 10 best practices. Data processing using Structural Equation Modeling-Partial Least Square can provide information about the effect of programmer information security awareness on application security vulnerabilities. The information becomes input in improving the competence of programmers in the field of information security. As a result of the research, some findings and discussions are given at the end of this article to achieve secure application programming through increasing awareness of information security.
A Comparative Analysis for Measuring Accuracy in Recognizing Hand Gestures using Feedforward Neural Network and CNN Method

Aim: The motive of the work is to accurately and precisely recognize the hand gestures and to use them for conversion to text and developing a media playback and snake game. Materials and Methods:ASL Alphabet dataset having 29 hand gestures with 26 alphabets and 3 words are used to train and test the hand gesture recognition framework. The proposed model can perceive hand movements captured through a camera in real-time into text, media playback controller and snake game. The intent is realized by databases with the help of two groups namely Convolutional Neural Networks (CNN) and Feedforward Neural Networks (FNN) to identify the more relevant Movement Hand Images(MHI) and it’s approach to develop text, a media player and snake game. The sample size is estimated as 20 using G Power.. Results and Discussion: Test results exhibited the prediction accuracy of the model with FFN (84.1%) and CNN (77.8%).There exists a statistical significant difference among the study groups with significance value (p<0.05). Conclusion: Hand gesture Recognition framework with FFN delivered better results compared to CNN in terms of performance measures, less data loss and accuracy in recognizing hand gestures, then conversion to Text, development of media playback and a snake game.
Classification of Indonesian Sign Language Greeting Words for Translators for Speech Impaired Persons with Convolutional Neural Network-2D Time Distributed

Language is a medium for communicating with fellow human beings. However, in language, some things are done verbally and non-verbally, and people with disabilities are one of them to communicate non-verbally. Motion capture technology is a technology that is being developed rapidly to be used in industrial businesses. And the latest challenge in this technology is being able to implement it into Indonesian sign language specifically for the speech impaired. This is intended so that people with speech impairments do not have difficulty communicating with the general public. With this research, it can be useful for previous researchers to be developed again and become an innovation in solving a problem.
Usability Analysis to Measure the Effectiveness of Implementing the Mapping System for COVID-19 Patients

The Mapping System for COVID-19 sufferers is a system built from research results that can detect first aid symptoms and risks of COVID-19 suffered by residents so that residents know their health status without checking the health of the COVID-19 task force in each area. The system is equipped with a feature to read the location of residents who report their health so that it is known where they are. As well as the feature of determining the health status of residents in the Negative COVID-19, ODR, ODP, PDP, or Positive COVID-19 categories. Of all these features, to evaluate the system and assess user satisfaction, it is necessary to analyze using usability to measure its effectiveness. So that the shortcomings are found and can be identified and developed even better. By using the Software Usability System (SUS) Questionnaire and involving 25 respondents from the community, it can be seen that the average satisfaction score is 68.8. So that the results can be stated to be quite satisfied in using the mapping system for COVID-19 sufferers.
Dilated Convolutional Neural Network-Based Deep Reference Picture Generation for Video Compression

Motion estimation and motion compensation are indispensable parts of inter prediction in video coding. Since the motion vector of objects is mostly in fractional pixel units, original reference pictures may not accurately provide a suitable reference for motion compensation. In this paper, we propose a deep reference picture generator which can create a picture that is more relevant to the cur-rent encoding frame, thereby further reducing temporal redundancy and improving video compression efficiency. Inspired by the recent progress of Convolutional Neural Network(CNN), this paper pro-poses to use a dilated CNN to build the generator. Moreover, we insert the generated deep picture into Versatile Video Coding(VVC) as a reference picture and perform a comprehensive set of experiments to evaluate the effectiveness of our network on the latest VVC Test Model–VTM. The experimental results demonstrate that our pro-posed method achieves on average 9.7% bit saving compared with VVC under low-delay P configuration.
Transient Detection with Unknown Statistics Via Source Coding

Quickest detection problems are fairly common in surveillance applications, as framing surveillance alerts as a change in an observation sequence’s statistics is often apt. In this work, we consider the scenario where an appropriate statistical description of our observations is not available, neither before nor after the transient we are trying to detect. In this vein, we explore the use of the database Lempel-Ziv, or LZ77, procedure, to detect this transient in the observation data. This algorithm is known to have phrase lengths that are asymptotically distributed as Gaussian random variables, which allows us to form a quickest detection problem around statistics of the coded output. This work specifies procedures to perform source-agnostic transient detection using Locally Optimal (LO) statistic to augment a Page CUSUM test. The work also shows an application to acoustic data.
Low-Energy-Consumption Operation Debugging Method of Large-Scale Gymnasium HVAC System Based on Physical Sensor Network

In order to achieve the goal of low-energy operation of HVAC in large stadiums, a building central air-conditioning energy-saving monitoring network based on physical sensor network technology has been developed, which can realize real-time monitoring and control of central air-conditioning systems in large public buildings or building groups. The network nodes form a self-organizing wireless sensor network according to the IEEE802.15.4/ZigBee protocol, using wireless temperature, humidity and other sensors to provide temperature and humidity in the building environment, water supply and return water temperature on the secondary side of the power center plate heat exchanger, etc. Perform real-time acquisition. The on-site operating parameters are transmitted to the upper computer of the monitoring center through the GPRS network, which realizes the energy-saving management and optimization control of the entire system operation by the upper computer.
The Practice of Designing and Research Physics Experiments Combined with the Discussion of Big Data

This research is based on high school physics experiment teaching practice, based on high school physics curriculum standards, and on the basis of carefully sorting out the experimental content in the current high school physics textbook published by the People's Education Edition, using a variety of research methods, including data mining, large Data analysis, genetic algorithm, machine learning and other technologies analyze and improve the design and research nature of high school physics experiments. Through various methods and methods, the integration of high school physics experiment teaching and information technology was explored, and the results showed that the effect of evaluation and research in teaching practice increased by 6.3%.
A Comparative Analysis Of Data Integrity In Healthcare

For any professional, maintaining data integrity is a demanding task. The purpose of this study is to develop a list of research efforts in the field of healthcare data integrity. Through assault statistics, the purpose of data safety issues in healthcare is highlighted in this study. Data integrity breaches are perhaps a greater serious threat to the healthcare business than data theft, as they might allow attackers to change anything in the record. By their very nature, Breach of healthcare data integrity is harder to identify, and in many situations, the true consequences of such hacks have yet to be determined. This survey examines a few of the key benefits of maintaining information integrity, and the risks that are associated with the technology industry these days, as well as the healthcare industry in particular, if it is not strictly followed.
Comparative Analysis Of Crime Hotspot Detection And Prediction Using Convolutional Neural Network Over Support Vector Machine with Engineered Spatial Features Towards Increase in Classifier Accuracy

The major aim of the study is to predict the type of crime that is going to happen based on the crime hotspot detected for the given crime data with engineered spatial features. crime dataset is filtered to have the following 2 crime categories: crime against society, crime against person. Crime hotspots are detected by using the Novel Hierarchical density based Spatial Clustering of Application with Noise (HDBSCAN) Algorithm with the number of clusters optimized using silhouette score. The sample data consists of 501 crime incidents. Future types of crime for the given location are predicted by using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms (N=5). The accuracy of crime prediction using Support Vector Machine classification algorithm is 94.01% and Convolutional Neural Network algorithm is 79.98% with the significance p-value of 0.033. The Support Vector Machine algorithm is significantly better in accuracy for prediction of type of crime than Convolutional Neural Network (CNN).
Adaptively Intelligent Meta-search Engine with Minimum Edit Distance

In current era retrieval of information has attained high demand due to spectra from websites has abundantly increased. Search Engines are basic tools to get information from the web of data and show irrelevant data causing wastage of time. Considering the fact that time is a precious commodity and is the hallmark of everything around us. To overcome the wastage of time and for its optimum utilization meta-search engines are design. Meta-Search Engine use to fetch relevant data. Existing meta-search engine shows their relevant data based on keywords as well as a semantic query. Semantic query-based results still have some irrelevancy in the results. In this paper, we analyze the semantic query based on machine learning algorithms. This paper hypothesizes improved results through the query expansion mechanism. Author also remove duplicated URLs that come from multiple search engines. Minimum Edit Distance algorithm is used to measure the similarity between titles, snippets and if measuring similarity is more than 0.6 then it must remove that title and snippet. Ranking process, generated retrieval of the relevant document at the top relevant document. Comparative analysis of proposed work is done with existing meta-search engines, overall performance of Intelligent Meta-Search Engine (IMSE) remains 74.17%.
Panel Data Fusion of Distributed Multi-Dimensional Data Index Strategy in Cloud Computing Environment

Aiming at the problem that the data index of the distributed storage system in the cloud computing environment does not support complex queries, a multi-dimensional data indexing mechanism, M-Index, is proposed, which uses pyramid technology to describe the multi-dimensional metadata of the data as a one-dimensional index. This is the first time on this basis. The concept of prefix binary tree is proposed, and one-dimensional index and the prefix of PBT effective node are extracted as the primary key of data in the storage system. This paper proposes a new multi-dimensional cloud data indexing scheme based on UB tree: Cloud UB. This scheme first uses the Z curve to reduce the dimensionality of the multi-dimensional space, and then divides the multi-dimensional space into Z regions along the Z curve.
Machine Learning-Based Methods in Source Camera Identification: A Systematic Review

Source identification is one of the most critical problems in the field of multimedia forensics. In the last decade, researchers have been studying and improving in this field. Photo Response Non-Uniformity is one of the unique noise patterns that is being used to match a media to its originating device. Utilizing the noise patterns with machine learning algorithms has been the focus of research in recent years. Therefore, a systematic review is needed to present the latest contributions in this field. This systematic review focuses on the published work from 2015 to 2021 in source identification using noise patterns in machine learning-based systems. The results of the review indicate that a benchmark should be proposed and used to fairly compare past and future methods. Moreover, a minimum number of devices used for evaluating a model should be set by the research community in order to accurately evaluate the accuracy of the model in real-life situations.
Post-Covid 19 and students` experiences with technology-based learning: A longitudinal study of students` IT preferences for future learning environments

The dire and unprecedented circumstance caused by Covid-19 has disturbed the education systems not only in the UAE, but also all over the world. Teachers were confronted with the need to adapt to online teaching in which new methods of education have been adopted to accommodate students and their learning. This paper inspects students` experiences with technology based-learning and IT preferences for future learning environments as a blueprint of their post-covid 19 experience. The study used a close-ended questionnaire in which 103 students participated. The study used SPSS to provide descriptive statistics to verify the students` experiences with technology-based learning and IT preferences for future learning environments.
How to Build on Arab Speaking Students’ Positive Experiences: Emergency Remote Learning during the Covid-19 Pandemic and into the Future of Learning

According to the United Nations, UNESCO (2020), the Covid19 pandemic has caused the greatest disruption to education in memorable history. School closures impacted 94% of the world’s student population in 190 countries. In the Middle East, campus closures started in March 2020 when the spread of the pandemic was deemed a serious health risk. By the end of April 2020 college students in the UAE found themselves attending rapidly deployed on-line video classes. However, Al Lily et al. (2020) note that countries around the world have not worked together on how to implement emergency remote learning and this has resulted in potential local differences in effectiveness. This paper centers specifically on Arab learners at a college in the U.A.E. and their response to emergency on-line learning. This study focuses on two surveys carried out as a longitudinal research project looking at changes in student confidence and happiness with on-line learning between May 2020 and October 2021. In the 2020 survey, students indicated concern about their ability to succeed, a point commonly reflected in other early student surveys. However, as time passed and students became comfortable with online learning, their confidence and happiness has improved dramatically as indicated in the October 2021 survey data. However, the 2021 survey also incidentally discovered the absence of a significant body of students as compared to the 2020 survey. These two finding are important in order to plan for what needs to happen going forward. In particular, we need to understand how remote teaching has affected students from different backgrounds differently.
A Contemporary Cloud-based Dynamic Authentication System for Mobile Applications

For a variety of reasons, this identification strategy plans several compensations over outdated techniques including ID cards (tokens) or PIN statistics (passwords) such as, i) The person who is to be recognized is mandatory to be physically present at the point of identification. ii) Identification based on biometric methods eliminates the need to remember a password or carry an encrypted token. This work attempts to understand the significance of mobile devices for society. Using user-friendly safe mobile applications, the services can be distributed ensuring data security and protection and can be hosted on cloud service both as client-side service and backend service.
A Novel Approach to Estimation Precision and Recall for Star Rating Online Customers Based on Negative Hotel Reviews using Multinomial Naive Bayes over Multischeme Classifier

To estimate precision and recall for hotel star rating using sentiment content. Majority class classifier with sample size (N=10) and Multinomial Naive Bayes with sample size (N=10) were iterated at different times for predicting accuracy percentage of hotel review. The F1 measure used in prediction to probabilities which helps to improve the prediction of accuracy percentage. The sigmoid function used in Simple majority classifier prediction to probability which helps to improve the prediction of accuracy. There was a statistical significance between Multinomial Naive Bayes and Majority class classifiers (p=0.00). Results proved that Multinomial Naive Bayes got significant results with 68% accuracy compared to Majority Class Classifier with 67% accuracy. Multinomial Naive Bayes is a simple and most effective algorithm to build fast machine learning models. Multinomial Naive bayes with f1 measure helps in predicting with more accuracy percentage of hotel review.