Reviewerly/exHarmony huggingface dataset:
Learning Media Innovation about Keris Cultural Heritage through Augmented Reality

Keris is a special weapon that is a cultural heritage in Indonesia which is of high value. Until now, the introduction and learning of culture about keris are still limited to 2D from books, or in exhibitions or museums. Learning about keris culture certainly needs to be supported by technological developments. The process of delivering information is currently experiencing rapid development, the latest technology used in delivering information is Augmented Reality (AR) technology. Users can visualize historical objects or objects in three-dimensional (3D) form via Android smartphones. The method used in this research is the ADDIE model, starting from Analysis, Design, Development, Implementation, and Evaluation. The results reveal that distance, marker angle, and device specifications all have a sizeable impact on the camera’s marker readout. The shortest distance between the marker and the camera where 3D objects can be displayed is 20 cm, while the farthest distance at which 3D items cannot be displayed is 100 cm. And the marker reading angle is only about 0° until 45°. The application of Augmented Reality technology is interactive and real-time, so Augmented Reality can be implemented in various fields and become a learning medium to introduce historical objects that are cultural heritage.
How Do Affect the Infusion of Smart Technology and Mindfulness of Tourism SMEs on Competitiveness?

Abstract The purpose of this research is to analyse the effect of the infusion of smart technologies and the mindfulness of tourism small and medium-sized organisations (SMEs) in the use of smart technologies on several aspects related to the companies and their employees. Specifically, its effect on service advantage, value co-creation, employee job satisfaction, employees’ perception of service cannibalisation and competitiveness of tourism companies is analyzed. The total sample obtained was 877 employees/managers of tourism SMEs located in Europe and the United States. The model proposed in the study was estimated with PLS-SEM, a variance-based structural equation modeling statistical technique. The results of the study offer important implications and recommendations to researchers in the tourism field, tourism organisations and destinations on the importance of using smart technologies.
Detection and Classification of Indian Classical Bharathanatyam Mudras Using Enhanced Deep Learning Technique

Indian Classical dance forms like bharathanatyam are composed of advanced hand gestures, facial expressions moreover as body moments. Because of its complexity, identifying each mudra in bharathanatyam is extremely difficult. This paper demonstrates a massive Convolutional Neural Network (CNN) that was trained on Google Colaboratory using a single step model, You Only Look Once version 3 (YOLOv3), to analyze images in the dataset and detect and classify the mudras. Open datasets of mudras are not presently available. So Bharatanatyam mudra dataset of single hand gesture images of 28 classes was created. This proposed system is, as far as we know, the first attempt in this subject. YOLOv3 was never used to detect mudras. YOLOv3 divides the image into sectors, predicts bounding boxes, and calculates probability for each. These bounding boxes are then weighted according to the projected probability, and the model is then able to detect the object based on the final weights. The neural network was able to correctly generate test data after being trained, with a mean average precision (mAP) of 73%.
Bert Transformer Model for Indonesian Mood Music Dataset

Music is an inseparable part of human life. Music can affect a person's mood. One's soul can unite in music. Music influences a person to feel sad, happy and relaxed. In terms of music, in general Indonesia has two types of music. Regional and national music. National music consists of several genres. Some of them are Dangdut, Pop, Ballad, and Rock. The many genres and developments in the world of music open the opportunities to the field of computer science to be explored further regarding to the amount of data contained in music processing (songs). In this study, the authors used self-processed data sets. The songs were downloaded from Youtube channel then were converted and only required chorus. In practice, the author used the Bidirectional Encoder Representations from Transformers (BERT) model. In learning, this model machine is used to improve accuracy. The resulting accuracy of this method is 81% with a chorus of more than 1000 songs.
2D Vector Representation of Binomial Hierarchical Tree Items

Today Artificial Intelligence (AI) algorithms need to represent different kinds of input items in numeric or vector format. Some input data can easily be transformed to numeric or vector format but the structure of some special data prevents direct and easy transformation. For instance, we can represent air condition using humidity, pressure, and temperature values with a vector that has three features and we can understand the similarity of two different air measurements using cosine-similarity of two vectors. But if we are dealing with a general ontology tree, which has elements "entity" as the root element, its two children "living things" and "non-living things" as first- level elements repeatedly children of "living things" that are "Animals", "Plants" as second level elements, it is harder to represent this kind of data with numeric values. The ontology tree starts from the general items and goes to specific items. If we want to represent an element of this tree with a vector; how can it be possible? And if we want the measured similarity using some methods like cosine-similarity, which one similarity is higher, ("Animal" and "non-living thing") or ("Animal" and "Living thing")? How should we select the values of this vector for each item of the hierarchical tree? In this paper, we propose an original and basic idea to represent the hierarchical tree items with 2D vectors and in the proposed method the cosine-similarity metric works for measuring the semantic similarity of represented items at the same level as parent items. There are two important results related to our representation: (1) The "y" values of the items give the hierarchical level of the item. (2) For the same level items, the cosine similarities between the parent item and child items are higher if the child belongs to this parent compared to other childrens'. In other words, the cosine similarity between the parent item and child items is highest if the child belongs to this parent.
Misuse of Electronic Medical Records in Blockchain Technology Intelligence Security System

Technological advances Globalization Era are very rapidly developing. The advancement of information technology in the health sector is Electronic Medical Records. Electronic Medical Record is a technology-based health information system that functions to store complete health and patient care data. most of the Electronic Medical Records owned are simple and do not have a data security system. Data Security, Authenticity of management, and admin of data registration from health facilities can be a threat to the misuse of Electronic medical records. The purpose of maintaining the confidentiality of data using Blockchain Technology is used for the management of overall patient data protection. Simple Blockchain technology is recommended as an Electronic Medical Record Security system and Blockchain technology as a Cryptographic-based data security system where each Blockchain block can impact patient data in detail supported by the Internet network. The method used is in the form of empowering simple Blockchain technology in supporting the Security system protection of personal data in Electronic Medical Records which can increase the effectiveness of data storage so that it can avoid data misuse and manipulation of patient care data. Blockchain is a technology that is not easily accessed by others and helps protect patient data from forms of threats. In an effort to overcome the threat of misuse of Electronic Medical Record data, the role of intelligence in the form of securing data security systems is a solution in protecting data through data security
Optimal Solution for Data Storage Security in Cloud Computing (OSDSS-CC)

In the recent decade, Cybersecurity and Cloud Computing have become important research and development areas due to the high necessity of applications in this paradigm for different sectors such as education, economy, and public services. The use of encryption algorithms becomes more critical in trying to secure the data during the transmission and storage of the data. There is leakage in the optimal security solution for cloud computing that covers all factors such as performance and security. This research work aims to analyze the Cybersecurity weakness in the Cloud Computing applications and optimize a modern solution that concentrates on performance and security at the same time. As a first step in analyzing the Cloud Computing environment security, three modern security mechanisms for Cloud applications are simulated and evaluated in terms of security and performance. Analyzed results provide an essential indicator for using this security mechanism and show the need to design an optimal security mechanism suitable to cover the main security parameters for the cloud environment. This motivates to propose an optimal method for using advantage of the studied security algorithms to enhance cloud computing security with optimal cost.
A Systematic look up into various Object Detection Techniques in Autonomous Vehicles: A Survey

Working with a robotic system, understanding the surrounding environment is necessary before any process. As a result, Object detection has become the most significant prerequisite in Autonomous Vehicles (AV), since it represents a vital step in various tasks like Navigation of robots, Behavior Interpretation, Video Surveillance, and Traffic Analysis. However, the detection task becomes complicated due to various issues like Occlusions, Dynamic Background, illumination, Abrupt Motion, and Appearance Change of an object. This paper provides a complete analysis of existing state-of-art object detectors on both Traditional-based and Deep-Learning (DL) based methods. Traditional methods rely mainly on the hand-crafted features and are further subdivided into Model-based, Model-free, Feature-based and Appearance-based methods. The DL method has end-to-end network architectures with single-stage and two-stage object detectors, and each category of object detectors is discussed in detail. Subsequently, some prominent datasets for the detection process are analyzed and summarized along with their performance comparison on some datasets. Based on the observations of different detectors, Faster RCNN and YOLOR has performed better for PASCAL VOC and MS-COCO respectively with 86% and 89% of Average Precision (AP). Finally, several perspectives of challenges and future research are provided in Object Detection.
Information Security Awareness Evaluation of Telemedicine Application Users using Human Aspect Information System Questionnaire

During the Covid-19 pandemic, people began to pay attention to their health and choose to use teleconsultation services to reduce physical contact and the possibility of contracting the virus. Users easily submit their data to facilitate the service process. The health industry must be prepared to secure data due to the prevalence of data leaks and sales. In addition to the company's security system, user awareness is the first protection that must be built. This research was conducted on a new object regarding users of telemedicine applications that are rarely applied in Indonesia but are frequently used nowadays. Data was collected by distributing questionnaires to 400 Alodokter users, one of the teleconsultation applications in Indonesia. The questionnaire was developed using the Human Aspect of Information System Questionnaire (HAIS-Q), with seven focus areas in each knowledge, attitude, and behavior dimension. The data is analyzed using Analytical Hierarchy Process (AHP). The result revealed that the information security awareness level is "Good". Based on the dimension, knowledge is the weakest dimension among others, especially related to the focus area of incident reporting and mobile devices. Based on focus areas, information handling is the lowest score, especially regarding setting a screensaver with a short time to minimize other people from accessing personal devices. Based on all items, the weakest item is regarding changing passwords periodically. This means that Alodokter must provide more education to users or improve Alodokter application by requiring users to change their passwords automatically for a period of time.
Design Interactive Game by Using Visualization: The Movie Data Set

Data visualization is a strategy for seeing previously unknown data that will aid humanity in discovering essential information. There are various ways to represent datasets, but data visualization is the most effective because of its ability in seeing unseen information, which is a valuable tool for assisting in understanding and keeping interactive games enjoyable. The representation of the skeleton by the game is necessary when the skeleton is used to analyze the visualization patterns, and therefore the visualization of the dataset gives effective results. In this paper, we will present a new method that will effectively analyze the visualization patterns and present an interactive movie with interactive movements compared to the movements of the original visualization patterns in the dataset, and visualize movie datasets, by quantities of movie skeleton, which can be used in-game. This new method is quantity skeleton has a good level of speed, efficiency, and accuracy. In the conclusion of this study, we will present all of the outcomes of the comparison among our proposed method and other well-known ways, proving the effectiveness and efficiency of the proposed through the use of well-known comparison measures and the presentation of the results.
Reversible Data Hiding in Encrypted Images using Deep Neural Network and GAN Model

Recently, some private images of a Hollywood stars leaked from icloud, for instance. There are two popular methods, secret writing and knowledge activity, for protecting picture contents against leakage. Despite secret writing’s ability to solve the privacy problem, its use is vulnerable to hackers due to the complexity and unique appearance of cypher languages. Data activity technology not only safeguards the data in a hidden file but also hides the communication method from an attacker by embedding it within a cover such as a picture, piece of music, or video. Reversible and irreversible unit knowledge activities exist. Reversible knowledge activity in images is a way for recovering the initial cover when embedded messages are extracted from encrypted images, such as image data, labels, or authentication data, without accessing the original contents. CNN and GAN (Generative Network) models will be utilized for training and evaluation. The initial image must be recovered in its entirety, and the disguised message must be extracted correctly at the receiving end. Numerous applications, including law enforcement, medical applications, such as keeping patient information discreet, and military applications, have a high demand for the physical property of hidden concealed knowledge. The original image must be recoverable without any quality loss, and the application must also allow for editing.
GPU-Based Multiple Face Recognition Using YOLOv5x

The growth of deep learning has led to impressive achievements and challenging issues in artificial intelligence and computer vision fields. Face recognition based on convolutional neural networks (CNNs) is one of the challenging and interesting hot topics in these fields. Recently, YOLO (You Only Look Once) became one of the more robust and attractive frameworks adopted for object detection in real-time. Despite that it is originally designed for object detection, the work of this paper is succeeded to apply it for face recognition with multiple faces recognized simultaneously in real time. The last version of this platform, known as YOLOv5x, is adopted to satisfy the desired task based on embedded GPU (Graphics Processing Unit) edge device. The proposed system is trained using custom-created dataset of 7378 augmented images, tested, and validated using Google Colab Notebook and Jetson Nano developer board. This board is supported by a parallel computing platform called CUDA (Compute Unified Device Architecture) enhanced with PyTorch framework for real-time acceleration without using DeepStream real-time server or Docker container. The developed system can recognize up to 20 faces simultaneously with high inference time of 0.817 s and precision of 99.8%.
Analysis of Micro Expressions using XAI

Micro Expressions refer to the minuscule flinches that occur on an individual’s face as an innate response to a stimulus. Unlike Macro Expressions that last for anywhere between 0.5 to 2 second, Micro Expressions last for less than half a second, their amplitude is also extremely minute. Their brevity coupled with subtleness, makes it hard to capture them with a naked eye, let alone identify and classify them. Recent advancements in deep learning models have allowed researchers to isolate frames from micro expression videos and feed them to these models, which in turn classify them with high precision and accuracy. However, deep learning algorithms are black-boxes as users are unable to completely understand their underlying functioning and calculations involved. This poses a challenge to the reliability and accountability of the model. eXplainable AI or XAI models have been revolutionary in solving this problem. In this paper we have implemented the LIME model, combined with a VGG16 deep learning model. This compiled architecture has not only been able to detect micro-expressions accurately but has also provided suitable explanations for the results achieved. To corroborate our findings further, we have employed an OpenCV algorithm. The problem with micro expression detection is the short life span of these expressions as well as the inability to produce or hide them on command. Arising as reactions to stimuli, it is only possible to study them empirically with heavy dependence on a high speed cameras and intensive manual studies. Incorporating XAI in this domain will help increase the throughput capacity of micro expression analysis.
Usability on Children: a Meta-analysis Review

Utilizing digital platforms is popular in various fields. One of them is a platform for children. In the context of children, it requires different treatment. So far, it found many articles discussing this issue. Usability is an indicator to measure a friendly platform. This article aims to capture the problems regarding usability and children by providing a literature review (meta-analysis). Some articles are observed on IEEE and ACM digital libraries published in the period 2011 to 2020. 80 articles are collected. Various aspects are found, such as approaches, methods, tools, design principles, and indicators. Interesting findings indicated that the majority of papers do not involve children as partners but as objects. Some techniques are not relevant to children. Instead of preferring to use techniques that are more appropriate for adults. In a conclusion, usability aspects require the user’s involvement as a partner. The result of this article act as a refinement for future studies to select an appropriate approach for children.
Proposed Factors that Affecting The Use of Mobile Payment QR Code on Micro Small Medium Enterprises as a Business Intermediaries

Mobile payment play a major role in most economies, especially in developing countries. Digitization like the Mobile Payment (MP) QR Code of MSMEs is still very low. This research will refer to the framework and models that have been confirmed previously and will reveal the factors that influence the use of MP QR technology in MSMEs. The seven proposed factors are obtained from the basic theory slice with the interview results.
Gesture Interpreting of Alphabet Arabic Sign Language Based on Machine Learning algorithms

Many methods have been applied to distinguish and interpret sign language (SL) since it is the only way that deaf people use to communicate with society. Interpret SL faces challenges, for example, variation of image illumination, background differences, video quality, hand geometry, skin colors, etc. This paper presents a system for distinguishing and interpreting the alphabet in Arabic Sign Language (ArSL). This proposal does not depend on using visible gloves or markers to carry out the classification procedure. Even images of bare hands obtained from videos are covered. It uses machine learning (ML) algorithms to detect hand gestures in videos and generate equivalent Arabic characters. Classification is achieved on a dataset of 32 SL alphabet characters, where features are extracted from the images and matched to the corresponding character in the gesture. The Linear Discriminant Analysis (LDA) algorithm is used for automatic feature extraction. Stochastic Gradient Descent (SGD), Decision Tree (DT), Naive Bayes (NB), k-Nearest Neighbors (k-NN) as well as Random Forest (RF) are five classification algorithms that are assessed. The k-NN model, which has an accuracy of 86%, was utilized to classify SL gestures from video frames.
Detection of URL-based Phishing Attacks Using Neural Networks

Doing business in a network environment, despite its high efficiency, due to the fact that it is a "remote" activity, is very inspiring for various types of dishonest actions and fraud. Phishing is a form of fraud in which an attacker tries to find out sensitive information such as user login information or account information. The phishing attacks that are happening today are sophisticated and increasingly difficult to spot. To find out which URL is legitimate and which is not, we used a neural network as a binary classifier of machine learning. To measure the performance of the model, we used binary classification accuracy.
Students’ Perspectives on ‘PeVe App’ to Learn English Phrasal Verbs

Learning English Phrasal Verbs (PVs) is one of the biggest challenges for the English as a Foreign Language (EFL) learners in higher education in Indonesia that demands a learning method coping with the development of technology and teaching devices. PeVe app is a new platform designed to support EFL learners to learn English PVs for developing their writing skills. The research aimed to discover the EFL students’ perspectives on the development of first-level content of PeVe apps in learning English PVs and to get their feedback on its development. Extreme Programming was used for developing PeVe Apps and the last phase of testing was carried out by distributing an online questionnaire to 70 students in two classes at one university with 62 responses. A descriptive quantitative was used to analyze the data. The result shows that more than 90% agree that the apps for learning PVs are supportive and more than 80% agree that learning English using the apps is fun and most agree this app supports them in learning English PVs. From the feedback, several things need to be added to the landing page, material, exercises, and the quiz. Since this app is a pilot project, only one at the Beginner level is available. Therefore, it is needed to develop to the further levels with various pictures, summaries of tasks, and listening materials. Thus, it is suggested for further research to improve PeVe Apps.Keywords— Convolutional Neural Network (CNN), RUU Omnibuslaw, Sentiment Analysis, Twitter.