Reviewerly/exHarmony huggingface dataset:
How Information Technology Literacy Moderated Factors Affecting Quality of Computer-Based Audit

The times require auditors to change, auditors no longer carry out the audit process using a manual system, but must switch to computer-based auditing. Especially in the era of the Covid-19 pandemic, which forced auditors to conduct remote audits, which of course had to be computerbased. Our research focuses on what factors can influence computer-based audit quality and what factors can moderate these factors. Our research uses independent variables, namely remote audit implementation, auditor performance and professionalism and in addition to using information technology literacy as a moderating variable. Our research is a quantitative study, using primary data distributed through questionnaires to auditors working in public accounting firms. For statistical data processing, we use the ordinary least square. The results of our study indicate that remote audit implementation, auditor performance and professionalism have a significant effect on computer-based audit quality and information technology literacy is able to moderate and strengthen the influence of these three variables.
Web-based Application for Searching the Event Organizers with Provided Audiences by Using Recommender System Method

An obstacle that is often faced by event owners (such as TV show) is finding a suitable event organizer that can provide many audiences at once. Some examples of events that require large audiences are talk shows, reality shows, game shows, fashion shows, variety show, exhibitions, and bazaars. These audiences are generally paid-audiences recruited by the event organizer. Large audiences are needed to show the high enthusiasm of the community for the event being held. The purpose of this research is to develop a web-based application that allows event owners to find an event organizer who can also provide large audiences through the recommender system method. The application development method used is Waterfall, and the data collection methods used are questionnaire, observation, and interview. The conclusion of this research is that this application makes it easy and helps event owners to find an event organizer that can also provide large audiences with certain criteria.
The Influence of Consumer Interest on the Use of UI and UX in the E-Commerce Application

UI and UX are one of the important elements in a digital application. Implementing a good UI and UX can help and simplify the process of interaction between applications and users which then leads to a positive user experience. However, if an application is not implemented using a good UI and UX, the opposite will happen, namely the inhibition of the information interaction process between the application and the user, which in turn creates frustration on the user's side. Currently, UI and UX are applied in every type of application development, one of which is an e-commerce type application. Good UI and UX implementation in e-commerce applications is expected to add value to the user experience that can compete with other similar e-commerce applications to get consumer attention or engagement. This study aims to see how big the role or impact of UI and UX in influencing consumer interest in e-commerce applications. In this case, the research object of the e-commerce application used is Shopee. This type of research is quantitative research methods conducted through a questionnaire as a source of research data. The questionnaire was randomly assigned to 30 people who met the criteria, among others, aged between 15 to 25 years, using existing e-commerce applications for more than 3 months, and using e-commerce applications for at least the past month. The result is to redesign a high-fidelity prototype from Shopee that is in accordance with the UI and UX rules and principles from the results of research data sources. The results of the Shopee redesign will then be carried out a second data collection as a form of validation. The result is that as many as 63 percent of respondents said that the display of the redesign is better than the current version of the Shopee application. It can be concluded that applying good UI and UX principles in e-commerce applications can increase consumer interest in using these applications.
Optimization of Student Database Confidentiality Using Elgamal Algorithm and Fermat Method

Every Islamic boarding school institution must have many databases, one of which is the student database. Securing the student database is needed to protect student data and information from database theft. To create an optimal system in securing the student database with the aim of avoiding accessing and processing data and information by unauthorized persons, the student database processed in this study used the ElGamal algorithm and the Fermat method with key formation using prime numbers and solving the problem requires discrete logarithm calculations. The keys used by this algorithm are the public key and the private key. The result of testing this method is that the student database is encrypted. The algorithm used to generate these prime numbers is to use Fermat. The ElGamal algorithm is very helpful in securing the student database at the Darul Hikmah Islamic Boarding School TPI Medan. In testing the encryption of the student database file with the file name “santri.mdf” it was successfully converted into a file with the new name “santri_mdf.encrypt”, and in the decryption process it could return to plaintext with the name ″santri.mdf”.
Applying Minimum Message Length to the Clustering of Mutual Funds

Clustering has been widely studied to group data into clusters. Several methods have been used including Maximum Likelihood (ML), Information Criterion by Akaike (AIC), and Bayesian Information Criterion (BIC) by Schwarz. In this paper, Minimum Message Length (MML) is applied to the clustering of mutual funds data. In this application, data are assumed to come from multivariate correlated Gaussian distribution. For this, MML principle needs to be numerically approximated. The modeling results are contrasted with those obtained using alternative methods, in terms of probability-bit costings and clustering structures. The experiment's findings demonstrate that, in terms of the fitted probability bit costings, MML clustering provided a more trustworthy model than AIC and BIC with significantly less bits required in conveying the data given the model. MML clustering also handled overlapping clusters better compared to modeling using the combination of ML with AIC and BIC. Furthermore, mutual funds trading have shown changes of movements during the pandemic Covid-19 with performances of mutual funds tend to be decreasing across funds, especially during the first 15 months of the period. Only several funds were grouped differently compared to most funds analyzed. The latter have shown effect of pandemic Covid-19 the most with lower returns compared to the returns of most funds.
Inference Calibration of Neural Network Models

The development of both the theory and the practice of neural network models has been significant in the last decade. Novel solutions including Dropout and Batch Normalization (BN) have been proposed and pervasively adopted to overcome issues like over-fitting and to improve the convergence of the models. Despite of their remarkable success, in this paper we show that Dropout and BN can make the model biased and suboptimum in inference time because of the shift of their behaviour from training to inference. We propose a simple method, called Inference Calibration, to reduce this bias and improve the performance for neural network models. Our experiments show that Inference Calibration algorithm can effectively reduce prediction error and improve model's accuracy, while reducing the calibration error for both regression and classification models.
Fault Prediction in Photovoltaic Systems with Extreme Learning Machine Modeling

Modern electric power systems are highly complex and dynamic interconnected systems. The integration of new and renewable energy plants (EBT) such as solar power plants (PLTS) will affect operations. In photovoltaic (PV) systems, the PV output characteristics are nonlinear. This happens due to certain conditions, such as different weather conditions. Many errors may go undetected due to the nonlinear nature of the PV output characteristics. Many researchers and engineers have been working to find the best solution for detecting various early faults in PV with high accuracy and low computational load. In this study, the PV errors discussed are prediction errors in photovoltaic arrays, inverter faults, network anomalies, feedback sensor errors, and MPPT controller errors with varying severity. This study aims to predict errors in the photovoltaic system automatically without having to identify and perform measurements manually. The method used in this research is Extreme Learning Machine (ELM). In this study, 13 types of parameters were used with a total of 1070068 data. This research succeeded in predicting photovoltaic errors automatically using K-Fold Cross Validation with an average accuracy of 77.83%. The number of neurons in the hidden layer is three neurons and the data used is data for the last two years.
Classifying Time-Series of IoT Flow Activity using Deep Learning and Intransitive Features

The continuous rise of traffic encryption in IoT devices has led network operators to revisit the way they gain visibility into the behavior of their network and connected assets. Moreover, flow-level analysis is perceived as a more cost-effective approach in network monitoring, particularly at scale, given the high computing cost of deep packet inspection engines. This paper uses time-series signals captured from the flow activity of IoT devices and classifies network traffic with deep learning-based classifiers based on Neural Networks (NN) and Decision Trees (DT). We analyze the efficiency and efficacy of deep learning models using one-dimensional convolutional neural networks (1D-CNN), Long Short Term Memory (LSTM), and Deep Forest (DF). We train our models on the real network traffic of 10 IoT devices collected from our lab during two months. To the best of our knowledge, this study is the first to investigate the performance of DF classifiers on IoT network traffic data and compare them to deep neural network models. We quantify the performance of our models by varying the window size (one minute to three minutes) in a time-series format. We show that the DF models present similar performance to 1D-CNN and LSTM and outperform the (shallow) Random Forest (RF) model but significantly higher inference time. DFs are attractive models since they have a dynamic architecture adjusted during training. Therefore, there is no need to manually search for the model architecture required for deep neural networks.
K-Means Algorithm to Identify The Optimal Production Period of A Photovoltaic Array Installed at Any Point in a Very Large Area

This work presents a technique to identify the optimal production periods of a PV generator installed at any geographical point located in a very large area. This method is based on artificial intelligence techniques of time series clustering. Irradiation, temperature and precipitation were determined as influencing parameters of the PV power. As a study area, the entire Beninese territory was chosen, where 418 geographical points were strategically selected. influencing parameters were downloaded from the NASA database from January 1st 2012 to December 31st 2021. Using the k-means clustering algorithm, irradiation, temperature and precipitation clusters were formed. We arrived at 19 irradiation clusters, 41 temperature clusters and 41 precipitation clusters. Using these clusters, 78 sub-zones were then formed within the study area by gathering points with the same irradiation, temperature and precipitation clusters. The clustering models were developed in Python 3 using the tslearn library. The Silhouette score was used to evaluate the quality of the clusters and a minimum of 0.99 was obtained within the clusters as Silhouette score. The optimal production periods depend not only on the potential of the location where it is installed, but also on the evolution over time of the parameters impacting its power output.
VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as “good” therefore its usability is acceptable.
Development a 3D Catalog Application as a Presentation Means of Glovic Cafe and Bakery Jember Design by Using Augmented Reality

This research uses AR technology by inserting it into the Interior 3D Catalog so that this catalog can become more real with the presence of 3D objects in it. This study aims to produce an application that can display 3D interactive object models so that it can help attract Glovic Cafe and Bakery Jember customers to visit and experience the interactive objects at Glovic Cafe and Bakery directly and will make it easier for the Glovic Cafe and Bakery Team Cafe and Bakery marketing does this. Promotion to consumers. Unity software is used to create 3D objects using Sketchup Software and create programming applications (Coding). This 3D Catalog application is made through several stages: making 3D objects, making markers, and designing applications. The results consist of two forms, namely in physical form (print media in the form of a catalog), which contains markers on several pages, and an Android-based Augmented Reality application in the form of. apk which is then installed on a Smartphone, where both complement each other. The results of the questionnaire to a total of 30 respondents show that this application is interesting to use. In addition, 17 respondents out of 30 respondents said that this application is easy to use. To develop this application in the future, it is necessary to make it more interactive and have supporting audio.
Generation of High-Quality Relevant Judgments through Document Similarity and Document Pooling for the Evaluation of Information Retrieval Systems

The Information Retrieval System Evaluation have carried out through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of document corpus, topics, and a set of relevance judgements. The relevant judgements are the documents which retrieved from the test collections based on the topics. The precision of the evaluation process is based on the number of relevant documents in the relevant judgement list called qrels. This paper presents a study on how methodologies like pooling and document similarity helps to generate more relevant documents into the relevance judgments set in order to increase the accuracy of the evaluation process. The initial results have shown that combination of pooling with document similarity performs better compared to base clustering or classification.
Sentiment Analysis towards Face-to-Face School Activities During the COVID-19 pandemic in Indonesia

As the COVID-19 pandemic took place, many face-to-face activities have been stopped to suppress the spread. However, in the last few months, many of those activities including learning activities have started to switch from online back to face-to-face. One of the major activities is face-to-face learning activities which involve millions of students all over Indonesia. Consequently, this study focuses on analyzing public sentiment through Twitter tweets which were obtained through scrapping by using Tweepy. The tweets were labeled using a semi-automatic process, using TextBlob and manual annotation. Next, we trained an IndoBERT model to conduct sentiment analysis and found that public sentiment was dominated by a mix of both negative and positive sentiment, followed by neutral sentiment. Our model obtained an accuracy of 40.79% on unseen data.
An Architecture and a Methodology Enabling Interoperability within and across Universities

We propose a general methodology and an infrastructure which allows to achieve interoperability within the same university and across universities. The former goal is achieved by incrementally defining and building a knowledge graph (KG) using data coming from multiple heterogeneous databases. Interoperability across universities is achieved by having a reference KG schema that each university can adapt to the local needs, but keeping track of the changes, and by natively supporting multilinguality. We achieve this latter requirement by exploiting a multilingual lexical resource containing more than one thousand languages and by seamlessly translating across the schemas and also (to some extent) across the data written in the local languages. The effectiveness of the proposed approach is proven by the services developed in the context of two different projects conducted in two universities in Italy and Mongolia.
Comparison of Classification Methods Using Feature Selection for Smartphone Sentiment Analysis

The determination of features is a major issue in the process of sentiment analysis classification. The right features can be chosen to reduce the dimensions of the dataset, making the classification stage more efficient and increasing the accuracy value. The study employed two methods for sentiment vector formation: first, N-Grams features yielded 6 and 18 features, respectively, and second, TF-IDF and Query Expansion Ranking (QER) yielded vectors of 10,20,30,40, and 50 results from word warfare. The f-measure value generated by Naive Bayes on 6 features is 0.73, while the f-measure value generated by SVM is 0.76. On 18 features, the f-measure value of Naive Bayes is 0.71 and 0.74 for SVM. The f-measure value in the combination of Naive Bayes and QER is highest in the 20k at 0.88, as well as on SVM and QER f-measure values of 0.91 for all k. In the n-grams feature, SVM accuracy on 6 and 18 features is higher than Naive Bayes, while QER has the highest accuracy value. In this study, the SVM algorithm outperformed Naive Bayes in the analysis of smartphone problem sentiment. When QER feature selection is used, accuracy improves.
Development of Techniques for Speech Emotion Recognition (SER) In the Context of Deep Learning

Understanding emotions from voice signals is a crucial yet difficult aspect of human-computer interaction (HCI). Many methods, including many well-known speech analysis and classification methods, have been used to extract emotions from signals in the literature on speech emotion recognition (SER). Deep learning methodologies have recently been presented as a replacement to traditional SER procedures. This paper presents an overview of deep learning algorithms for speech-based emotion recognition and evaluates some recent work that makes use of these methods. The review discusses the databases used, the emotions retrieved, the advancements made in voice emotion recognition, and its limits.
Web Development of Direct-to-consumer Genetics Testing

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia.
A Framework of Ensemble CNN Models for Real-Time Sign Language Translation

American sign language (ASL) is a natural language to minimize communication barrier for the people suffering from hearing and speech impairment. However, sign languages are not a common form of communication in society, and it is lacking a sign language translator to ease the communication. Hence, this research proposes an improved visual-based communication framework to translate ASL alphabets in real-time. The proposed framework consists of different steps i.e., image pre-processing, hand segmentation with U-Net, data-augmentation techniques, and classification of American sign language. A n ensemble classification model i s proposed where V GG19, R esNet-50 and MobileNet are used as base models, and 2 fully connected layers are used as meta model. The proposed approach achieved 99.86% accuracy and performed better when compared with existing literature.
Unsupervised DeepView: Global Explainability of Uncertainties for High Dimensional Data

In recent years, more and more visualization methods for explanations of artificial intelligence have been proposed that focus on untangling black box models for single instances of the data set. While the focus often lies on supervised learning algorithms, the study of uncertainty estimations in the unsupervised domain for high-dimensional data sets in the explainability domain has been neglected so far. As a result, existing visualization methods struggle to visualize global uncertainty patterns over whole datasets. We propose Unsupervised DeepView, the first global uncertainty visualization method for high dimensional data based on a novel unsupervised proxy for local uncertainties. In this paper, we exploit the mathematical notion of local intrinsic dimensionality as a measure of local data complexity. As a label-agnostic measure of model uncertainty in unsupervised machine learning, it shows two highly desirable features: It can be used for global structure visualization as well as for the detection of local adversarials. In our empirical evaluation, we demonstrate its ability both in visualizations and quantitative analysis for unsupervised models on multiple datasets.