Reviewerly/exHarmony huggingface dataset:
Improving Visual Style Classification in Digital Games Using Intercoder Reliability Assessment

The digital gaming community appreciates visual style information in digital games as it facilitates information seeking. Nevertheless, learned scholars have discovered that the digital game visual style classification is inconsistent and easily modified, potentially limiting the information and leading to inaccurate visual terminologies during information discovery. Therefore, this cross-sectional study was performed to assess multiple visual style classification terms and their definitions among Malaysian game developers using the closed card sorting exercise. A total of seven professional game developers participated in an online survey that comprised thirty-five digital game case studies using a card sorting technique. They were asked to classify nineteen visual style classification terms, including psychedelic, text, illusionism, photorealism, televisualism, handicraft, caricature, celshaded, comic book (anime), watercolour, Lego, minimalism, pixel art, silhouette, bright, dark, maplike, colourful, and black and white. The Fleiss’ kappa intercoder reliability assessment was performed to measure the coders’ agreement on visual style classification, followed by the think-aloud protocol descriptive analysis to gather assessment insights into the visual style descriptions. The intercoder reliability test achieved a significantly moderate agreement based on the results. The professional game developers agreed on eighteen visual styles and rejected the bright visual style classification due to its overlapping description with the colourful visual style. The definition of ten visual style classifications was improved from the existing Video Game Metadata Schema (VGMS) description, contributing to the digital game’s coherence and consistency. This improvement will enhance visual style classification information for machine-learning-based recommendation systems for digital game distribution platforms and digital archiving.
Peningkatan Proses Pembelajaran Menggunakan Google Classroom SD Negeri Mangunan 2 Kabuh, Jombang

The use of technology is important to support the teaching and learning process.With the COVID-19 outbreak, the government issued the Republic of Indonesia's Minister of Health Regulation (Permenkes) Number 9 of 2020 concerning teaching and learning through interactive or online learning.All schools are required to adopt a learning process using the blended learning method.However, this has not been supported by the readiness of SD Negeri Mangunan 2 Kabuh teachers who can only rely on the use of the WhatsApp chat platform.This results in a deteriorating impact on smartphone performance and teaching effectiveness.Therefore, it is necessary to have training on learning management systems using the google classroom platform with problem-based learning methods and servicelearning assistance.There are five stages, namely: preparation, pre-test, debriefing, assignments, monitoring, post-test and evaluation.From the results of the training to improve the learning process using google classroom for teachers of SD Negeri Mangunan 2 Kabuh, it was shown that the pre-test teachers scored 91% who did not know and could not operate the google classroom.The post-test resulted in 100% of the teachers knowing and being able to operate the Google classroom.
DISCOVERY LEARNING MODEL IN READING SKILL LEARNING AT MADRASAH ALIYAH AL-UMM: ANALYSIS OF IMPLEMENTATION AND STUDENT RESPONSE

&lt;p style="text-align: justify;"&gt;&lt;em&gt;A student who is susceptible to an erroneous understanding of Arabic texts when low reading skills. Therefore, learning reading skills must be done in a good way. This study aims to describe Madrasah Aliyah Al-Umm Malang's implementation of a discovery learning model in her 10th-grade reading comprehension learning and student responses to the application of this learning model. This study used a descriptive qualitative method. The results of this study suggest that the steps for applying the discovery learning model to learning reading skills are to provide suggestions, identify problems, collect data, process data, and prove and generalize. Indicates that there are Student responses to the use of discovery learning models that demonstrate that discovery learning models can train different aspects, starting with independence, cooperation, and self-confidence. In theory, researchers hope their research can contribute to the development of science, especially in education related to teaching Arabic in general and reading comprehension in particular. The results of this research will also provide new knowledge for researchers and readers. In the meantime, the researcher hopes his work will help Arabic language teachers choose appropriate and effective learning models to apply to their literacy materials. &lt;/em&gt;&lt;/p&gt;
A Review of Study on Quality Assurance Models

A Review of Study on Quality Assurance Models
Properties of LoTs: The footprints or the bear itself?

There are two ways to understand any proposed properties of LoTs: as diagnostic or constitutive. We argue that this choice is critical. If candidate properties are diagnostic, their homeostatic clustering requires explanation via an underlying homeostatic mechanism. If constitutive, there is no clustering, only the properties themselves. Whether DNNs are alternatives to LoTs or potential implementations turns on this choice.
IMPROVING THE QUALITY OF SPAM DETECTION OF COMMENTS USING SENTIMENT ANALYSIS WITH MACHINE LEARNING

Nowadays, people spend more and more time on the Internet and visit various sites. Many of these sites have comments to help people make decisions. For example, many visitors of an online store check a product’s reviews before buying, or video hosting users check at comments before watching a video. However, not all comments are equally useful. There are a lot of spam comments that do not carry any useful information. The number of spam comments increased especially strongly during a full-scale invasion, when the enemy with the help of bots tries to sow panic and spam the Internet. Very often such comments have different emotional tone than ordinary ones, so it makes sense to use tonality analysis to detect spam comments. The aim of the study is to improve the quality of spam search by doing sentiment analysis (determining the tonality) of comments using machine learning. As a result, an LSTM neural network and a dataset were selected. Three metrics for evaluating the quality of a neural network were described. The original dataset was analyzed and split into training, validation, and test datasets. The neural network was trained on the Google Colab platform using GPUs. As a result, the neural network was able to evaluate the tonality of the comment on a scale from 1 to 5, where the higher the score, the more emotionally positive the text and vice versa. After training, the neural network achieved an accuracy of 76.3% on the test dataset, and the RMSE (root mean squared error) was 0.6478, so the error is by less than one class. With using Naive Bayes classifier without tonality analysis, the accuracy reached 88.3%, while with the text tonality parameter, the accuracy increased to 93.1%. With using Random Forest algorithm without tonality analysis, the accuracy reached 90.8%, while with the text tonality parameter, the accuracy increased to 95.7%. As a result, adding the tonality parameter increased the accuracy for both models. The value of the increase in accuracy is 4.8% for the Naive Bayes classifier and 4.9% for the Random Forest.&#x0D;
Communications: PhotonicsViews 2/2023

PhotonicsViewsVolume 20, Issue 2 p. 24-28 CommunicationsFree Access Communications: PhotonicsViews 2/2023 First published: 03 April 2023 https://doi.org/10.1002/phvs.202370205AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume20, Issue2April/May 2023Pages 24-28 RelatedInformation
Real-time Face and Object Detection with Age and Gender Prediction for Video Surveillance Applications

Abstract: The development of advanced computer vision techniques has made it possible to perform real-time face and object detection for video surveillance applications. Video surveillance is an essential tool for monitoring public areas, buildings, and other locations for security and safety purposes. However, analyzing the vast amount of data generated by video surveillance systems can be challenging. Real-time face detection and object detection systems provide an effective solution to this problem, enabling the identification and tracking of people and objects of interest. In recent years, there has been a growing interest in incorporating age prediction and gender prediction into video surveillance systems. The ability to predict the age and gender of individuals can provide valuable insights to enhance the effectiveness of video surveillance applications. For example, age and gender prediction can be used to detect and prevent potential crimes. This research paper presents a study of a real-time face and object detection system with age and gender prediction for video surveillance applications. The proposed system utilizes deep learning and convolutional neural network techniques to achieve high accuracy in face and object detection, as well as age and gender prediction. The experimental results demonstrate the effectiveness of the proposed system in accurately detecting and tracking faces and objects while predicting their age and gender in real time. The proposed system has potential applications in various fields
APPLICATION OF GAMIFICATION TOOLS IN THE PROCESS OF TRAINING AND DEVELOPMENT OF THE PERSONNEL RESERVE

The article discusses the concept of gamification in personnel management, provides examples of the use of gamification in various processes of personnel management in the organization, defines the role of gamification in the system of personnel training and development. The characteristics of the main gamification tools that can be used in the process of personnel training, in particular in the process of working with the personnel reserve, are given. As an example of using gamification, the authors describe the architecture of a mobile application for training and development of the personnel reserve, which contains most of the gamification tools considered.
Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia

Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies.
Randomness properties of sequence generated using logistic map with novel permutation and substitution techniques

In this paper, a design of a chaos-based keystream generator (KSG) using a novel permutation technique with various two-dimensional patterns and a substitution technique with &lt;em&gt;Z&lt;/em&gt;&lt;sub&gt;4&lt;/sub&gt; mapping is proposed. Initially, a chaotic function such as a logistic map is used to generate a pseudo-random number. Then these numbers are converted into binary sequences using binary mapping. In order to achieve statistical properties of the resultant binary sequences, a novel method of KSG is developed by considering parameters such as initial value “x&lt;sub&gt;0&lt;/sub&gt;”, system parameter “&lt;em&gt;r&lt;/em&gt;”, novel permutation techniques defined by 2-dimensional patterns, and substitution technique defined over Z&lt;sub&gt;4&lt;/sub&gt; transformation. The binary sequences so obtained are subjected to randomness tests by applying the National Institute of Standards and Technology (NIST) SP-800-22 (Revision 1a) test suite for investigation of its randomness properties to obtain suitable sequences which can be used as a key for cryptographic applications. From the results obtained, it is found that the binary sequences exhibit better randomness properties as per the cryptographic requirements.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care

&lt;p&gt;Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities.&lt;/p&gt;
View points on data points: A shared vocabulary for cross-domain conversations on data and metadata

Sharing data across scientific domains is often impeded by differences in the language used to describe data and metadata. We argue that disagreements over the boundary between data and metadata are a common source of confusion. Information appearing as data in one domain may be considered metadata in another domain, a process that we call “semantic transposition.” To promote greater understanding, we develop new terminology for describing how data and metadata are structured, and we show how it can be applied to a variety of widely used data formats. Our approach builds upon previous work, such as the Observations and Measurements (ISO 19156) data model. We rely on tools from the Data Documentation Initiative’s Cross Domain Integration (DDI-CDI) to illustrate how the same data can be represented in different ways, and how information considered data in one format can become metadata in another format.
Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering

&lt;span lang="EN-US"&gt;Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.&lt;/span&gt;
Digital Economy: The Evolution of the Digital Economy

We are in the early stages of a historical transition from the industrial economy of the past two centuries to a new kind of a 21st century digital economy. This transition is being driven by dramatic advances in digital technologies and the internet over the past two decades, as well as the more recent advances in big data and artificial intelligence. The paper will discuss some of the key technologies that are driving this long-term transformation, including artificial intelligence, blockchain, cloud computing, and next generation user interfaces, as well as their potential impact on the economy, society, and our personal lives.
Effect of Directed Activities Related to Texts on English Skill: Relevance and Adequacy of the Content of Grade XI Students

The study was aimed to find out the effect of directed activities related to texts (DARTs) on English writing skill: Relevance and adequacy of the contents. An experimental study was conducted through pre-test post-test control group design. A public sector degree college for boys from district Lahore was selected as sample of the study. Sixty students out of 400 enrolled in the session 2018-19 were selected and divided into two equal groups randomly. Study intervention period was of 16 weeks
Hyperparameters analysis of long short-term memory architecture for crop classification

&lt;span lang="EN-US"&gt;Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.&lt;/span&gt;
An efficient ultra-wideband digital transceiver for wireless applications on the field-programmable gate array platform

&lt;p&gt;The ultra-wideband (UWB) technology is a promising short-range communication technology for most wireless applications. The UWB works at higher frequencies and is affected by interferences with the same frequency standards. This manuscript has designed an efficient and low-cost implementation of IEEE 802.15.4a-based UWB-digital transceiver (DTR). The design module contains UWB transmitter (TX), channel, and UWB-receiver (RX) units. Convolutional encoding and modulation units like burst position modulation and binary phase-shift keying modulation are used to construct the UWB-TX. The synchronization and Viterbi decoder units are used to recover the original data bits and are affected by noise in UWB-RX. The UWB-DTR is synthesized using Xilinx ISE&lt;sup&gt;®&lt;/sup&gt; environment with Verilog hardware description language (HDL) and implemented on Artix-7 field-programmable gate array (FPGA). The UWB-DTR utilizes less than 2% (slices and look-up table/LUTs), operates at 268 MHz, and consumes 91 mW of total power on FPGA. The transceiver achieves a 6.86 Mbps data rate, which meets the IEEE 802.15.4a standard. The UWB-DTR module obtains the bit error rate (BER) of 2×10&lt;sup&gt;-4&lt;/sup&gt; by transmitting 105 data bits. The UWB-DTR module is compared with similar physical layer (PHY) transceivers with improvements in chip area (slices), power, data rate, and BER. &lt;/p&gt;
CORRELATION BETWEEN STUDENTS’ SELF-CONFIDENCE AND INTERPRETING ABILITY

Every research must have a purpose, as well as this study, which aims to determine the correlation between self-confidence and interpreting ability in seventh semester students of English Study Program at FKIP Unmas Denpasar. There were 27 students taken as a sample of class VII B with cluster sampling technique. This research was designed using correlation research. For data collection in this study, there were two instruments used, namely a questionnaire for a self-confidence test consisting of 30 items and an interpreting test. The collected data was then analyzed using Pearson's Product Moment Correlation on IBM SPSS Statistic 25. The results of the data analysis and based on statistical calculations obtained r of 0.617. From these results, it can be seen that there is a correlation between self-confidence and interpreting of the seventh semester of the English Language Study Program, FKIP Unmas Denpasar. In conclusion, the alternative hypothesis (Ha) is accepted.
Realtime human face tracking and recognition system on uncontrolled environmen

&lt;span lang="EN-US"&gt;Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer.&lt;/span&gt;