Reviewerly/exHarmony huggingface dataset:
Using Image Processing for Architecture Extraction from Non-Standard Sources

Many organizations, programs and teams have yet to adopt or implement modern model-based systems engineering tools, and instead continue to represent their systems archi-tectures using virtual “paper-based” formats such as PDFs or images. A system's architecture is a crucial portion of the technical baseline that engineers and decision-makers alike will continuously develop, maintain, and reason over during the lifetime of any system. This paper presents a new tool, ArchEx, that lowers the barrier-to-entry for those looking to transition beyond these static architecture images. Using a combination of Python-based computer vision techniques, ArchEx allows for the intelligent, automated import of virtual paper-based architecture artifacts into commercial tools to allow for digital model representation and manipulation. It parses the architecture image to derive a graph structure which is then loaded into a model-based systems engineering tool. ArchEx provides an automated assist to the systems engineer, decreasing the time and effort typically required to transcribe image-based documentation into a modeling tool. It is being actively developed within the MITRE Corporation, with initial test applications and experimentation showing promising results. This paper will provide an overview of the technical approach surrounding ArchEx, a visual demonstration of the capability in its current state, and a brief summary of planned next steps, and future applications.
Effects of data preparation, training and testing choices on classifier performance: Case study in ship classification

Modern deep learning algorithms can be used to build very powerful object classifiers. However, these algorithms are also extremely sensitive to modeling assumptions adopted during their development. In this paper, we use ship classification as a case study to investigate and illuminate the effects of intentional and unintentional choices made during model development, focusing directly on the impact of performance of the resulting classifier. We study and analyze the effects of architectural choices, fine-tuning and refinement strategies, train-test data processing assumptions and variations introduced by the inherent randomness of the training process. In addition to looking into the modeling assumptions, we also study performance impact of various dataset preparation practices. Specifically, we investigate the dependence of model performance on annotation accuracy by injecting varying amounts of label noise into the training data. Finally, in order to gain a better understanding of the effects of image preprocessing, we conduct our studies and report findings on ship classification datasets that are curated to include simple, straightforward cases (uniform size and ship alignment) as well as more challenging versions (variable sizing and unaligned) to give a broader understanding of performance. Understanding the different variables as well as the effects that they have on object classification can inform design and development choices, ensuring higher levels of reliability, efficacy and confidence in the performance of classifiers used for naval applications.
Anonymized Blockchain-Based Infection Tracking for Disease Control

Worldwide Coronavirus Disease 2019 (COVID-19) has infected more than 220 million people and caused more than 4.5 million deaths and it is increasing daily. Precisely, the United States has become the country with the largest number of known infections with more than 40 million cases and caused more than 0.65 million deaths and even increasing. Contact tracing is an effective disease control strategy for highly infectious diseases such as H1N1 and more recently the novel COVID-19. It involves collecting information about infectious patients and their contacts for restricting the further transmission of disease. This data is also extremely valuable for the city/state administrator as it can be used to localize active infection areas and issue public warnings. Despite the effectiveness of this mechanism, there are serious concerns regarding user data privacy and tracking system efficacy. To address these shortcomings, we are aiming towards building a blockchain-based decentralized contract tracing solution to strengthen infectious disease mitigation approaches. Blockchain functions as a distributed ledger using advanced cryptographic techniques and ensures that the data remains immutable and auditable. The main objective of our current work is to build a prototype tracking system by collecting, matching, and processing personal location-aware information about infected persons and their contacts. We are also evaluating our solution with varied population size and time to demonstrate efficacy and efficiency. Our proposed tracking system visualizes simulated infection spreads, based on varied infection and recovery rates. The proposed solution will help healthcare administrators to proactively manage mild to highly catastrophic pandemic situations. Overall, this study will help create a strategic road map in developing a secured digital contact tracing solution to mitigate highly communicable diseases.
Attention Focused Generative Network for Reducing Self-Occlusions in Aerial LiDAR

This work explores the problem of re-ducing self-occlusion in aerial lidar. We introduce an entirely new dataset, DALES Viewpoints which uses a combination of synthetic and real-world data to represent aerial lidar scenes with different levels of occlusions. Our overall goal is to transform these occluded point clouds into a more visually complete representation of the same scene. We also propose a method called Channel Learned Downsampling (CLD). This downsampling method can be used as a drop-in replacement for any sampling method and uses a channel attention mechanism to select points based on their features instead of relying entirely on their spatial information. We show that this sampling method outperforms other sampling methods when used with a state-of-the-art point cloud completion network.
Analysis of Streaming Video on VPN Networks Between OpenVPN and L2TP/IPSec

Advances in multimedia technology currently have a positive impact on its users. Users get information in the form of text, audio, images, and even video delivered through the streaming method, also known as video streaming. The problems encountered in video streaming are security, confidentiality, and authenticity when it comes to users and will be dangerous if misused by outside parties. VPN is needed as a form of protection against this. The simulation is carried out by sending video files with 360p, 480p, and 720p resolutions on L2TP networks with IPSec and OpenVPN. Data packets on the VPN network will be captured by Wireshark. The capture results will be calculated on the Quality of Service parameters, namely delay, jitter, throughput, packet loss, so that good quality of service can be obtained. After calculating the data on the two VPNs, Based on the parameters of delay, throughput, and packet loss in video streaming experiments, Open VPN is better than L2TP/IPSec. As for jitter parameters, L2TP/IPSec is better than Open VPN. Based on the QoS index, the index on L2TP/IPSec scored 2.5 in the unsatisfactory category. While the index on Open VPN scored 3.42 in the satisfactory category. By far, the advantage of Open VPN is that it allows secure user authentication for streaming video.
Literature Review: Evolution Strategies Application in Solving Multi-Objective Optimization

Evolution Strategies (ES) is a heuristic-nature-inspired algorithm that uses real-coded gene representation. It is in the same family of evolutionary algorithms as the Genetic Algorithm (GA). ES is less popular and rarely used in solving problems than GA, but some research has proved that ES can result in a feasible solution, sometimes better than GA. This literature review tries to review ES&#x0027;s performance in computational time and its ability. Multi-objective optimization (MO) cases. While the basic forms of ES such as (&#x00B5;,&#x03BB;)-ES, (&#x00B5;/p, &#x03BB;)-ES, (&#x00B5;+ &#x03BB;)-ES, and (&#x00B5;/p+ &#x03BB;)-ES is usually capable of solving complex problems, sometimes ES needs to be modified. Another modification has been made by making a hybrid algorithm between ES and Fuzzy Inference System (FIS). The 12 literature that has been reviewed proves that ES is indeed capable of solving multi-objective problems ranging from the ordinary to the very complex.
Implementation Analysis of KEBI Application on Non-Standard Words Detection in Student Essay Tasks on Language Variety

Non-standard word errors in writing sometimes escape the attention of writers or editors due to several factors, including unintentional or limited time in editing. Non-standard word is one of the Indonesian spelling error. Therefore, the application for detecting non-standard words in writing, KEBI, was created to minimize the height of non-standard words in writing. In this study, KEBI was analyzed in terms of application implementation to five various language essay documents as student assignments for Indonesian language courses. There are three things that are analyzed, namely the interface page, processing time, and the level of accuracy of detection and correction using the precision and recall method (confusion matrix). The overall research method includes literature study, data collection, testing, measurement of display, time, and accuracy. Based on the results of the analysis, the interface page display shows good quality. The processing time required by KEBI to detect non-standard words is in the range of 0.02-0.06 seconds. Meanwhile, KEBI&#x0027;s non-standard word detection accuracy rate is 86&#x0025;, while KEBI&#x0027;s non-standard word correction accuracy rate is 90&#x0025;.
Auto-Image Colorization Of Grayscale Images Using Machine Learning Techniques

There are lots of old pictures in black-and-white and grayscale. When a grayscale image is painted, it can show the clarity of the past image. Many methods were proposed to perform a color transformation on greyscale images. Some include using manual methods and chemicals to more modern ones using graphic design software. However, all of these methods require special skills and a long time. We need a system that can automatically perform color transformations on gray images. The system developed in this study uses the Self-attention and Spectral Normalization GAN (SSN-GAN) algorithm, which automatically transforms grayscale images. This method was chosen because it can solve various image-to-image translation problems. This method produces a better initial score and a more stable training process. Self-attention is used to increase the initial score of the image generated by the GAN. The addition of spectral normalization makes the training process on the GAN run more stable. At the same time, by increasing the learning rate on the generator and discriminator, the model can produce a better image. In this study, researchers used two loss functions, namely, using SSIM Loss and without SSIM Loss. The highest accuracy value in the test with an epoch value of 50 and using SSIM loss, the accuracy value obtained is 59.9
ModelLStat: A Tool for Static Analysis in Model Layer on Web MVC Application

Model-View-Controller (MVC) is a design pattern that is commonly used for web applications. MVC divides applications into three main layers, the Model, the View Layer, and the Controller Layer. The model layer is related to the database. All data access to and from the database occurs at this layer. Usually, to monitor database activity, we use tools from database tools in the server. That tools allow us to see real-time database load performance. The disadvantage of real-time monitoring is that we can perform analysis when the application is running. Lack in the implementation process will be seen as a whole when the application is running. If errors are known when the program is running, the costs will be higher. Therefore, in this study, we create a tool to analyze the model layer. The analysis we use is static analysis. We try to analyze information from the code in the model layer. From the code in the model layer, we will analyze information on classes, functions, queries, and the frequency of tables are accessed in the database. With this static analysis, we can anticipate the access data within the application before the application goes live.
Value at Risk with Bootstrap Historical Simulation Approach for Prediction of Cryptocurrency Investment Risk

In the era of the digital economy, cryptocurrency has emerged as a new transaction tool and investment asset that is widely used by people all over the world. Cryptocurrencies are preferred because their security and privacy are guaranteed, and can provide greater profit potential than conventional financial investment assets (stocks, bonds, etc.). Although it can provide the possibility of higher returns, investing in cryptocurrencies does not mean that it is risk-free. Therefore, we need to know the estimated risk of loss that will occur to prepare a strategy so that the investment does not go bankrupt. This article aims to calculate predicted losses in the Top 5 Cryptocurrencies (as of June 2021) using VaR with the Bootstrap Historical Simulation approach. Using historical cryptocurrencies data from 01/07/2019-16/07/2021, at a 95&#x0025; confidence level and a holding period of 1 day, the cryptocurrency with the highest risk of loss is Cardano (ADA), with a VaR value of 8.62&#x0025;.
Classification of Online Learning Materials Using Artificial Intelligence

In a new normal era, all teachers use online learning to teach their students. Each of the teachers makes the material in online learning, so it makes problems in online learning and students, there are many materials on the same topic and the same subjects that make useless and overlap in the material of teaching. The purpose of this research solves this problem. This research makes a system that can classify based on the topics and the subjects in online learning, so teachers do not make another material and became overload in database online learning. The system uses Artificial intelligence to classify the material based on topic and subject. The processing in classification use model of document classification. The processing has 2 processing that are modeling the training of document and classification documents based on the features of document. The system was tested in existing online learning and use 2 of testing the application to get accuracy and validation. The first testing is that the accuracy in this testing is check a functional of naive Bayes algorithm. This aim in this is to get accuracy, precision and recall in the algorithm. The second of test is that application is validated by validator to get valid or no valid about the result. All of testing given a significant average both testing in algorithm and testing in validation. The average of rate all testing is 83, 5&#x0025; that this research success to classify the material on online learning based on specific subject
Classification of Proper or Improper Masks during a Pandemic Using a Custom Convolutional Neural Network with Data Augmentation

Classification of the use of masks today is very necessary regarding the pandemic period that is not over yet. This is mainly to break the chain of transmission of the COVID-19 virus from one person to another. From the literature that has been studied, the Convolutional neural network method can be used to distinguish the types of masks used in society. The advantage of the Convolutional Neural Network is that it can recognize objects with a fairly high level of accuracy, but it has a weakness, namely that the training process time is still quite high. This is the author&#x0027;s concern by doing a custom layer on the convolutional neural network. In addition, the addition of data augmentation is done to increase the number of data variations. The result used 18&#x2013;34 custom layers in an average of around 97.93&#x0025;, with an average computation time for the training process of about 1 minute 83 seconds. The resulting classification errors using Mean Absolute Error is 0,0163
Identification of Acute Respiratory Infections in Toddlers Based on the Chi-Square And Naive Bayes Methods

Acute Respiratory Infections usually attack the respiratory tract of toddlers, both the upper and lower respiratory tracts, because the body&#x0027;s defense system against viruses that cause infection has not yet been formed. And usually, parents will know if the baby&#x0027;s condition is very chronic so that the baby experiences complications. This causes the need for a system that can assist in the early detection of respiratory tract infections. This study proposes the Chi-Square and Naive Bayes (NB) method. The Chi-Square method is a feature selection method to reduce features that have no effect. At the same time, the NB method is a prediction method that performs a simple probability-based identification process based on the application of the Bayes theorem with the assumption of strong independence. The contribution of this study is to determine respiratory tract disease in infants using the chi-square feature selection and the NB method, which can assist parents in detecting respiratory tract infections. From the tests that have been carried out using 120 datasets with 90 as training data and 30 as test data, the accuracy is 75.833&#x0025;. This proves that the Chi-Square and NB methods are able to identify respiratory tract infections.
Educational Application Development as an Effortless Solution for Basic Level Learning Media

Basic level learning media is an educational application intended for elementary school students as a distance learning media. With the pandemic, it has caused learning activities that were previously carried out directly to be transferred to distance learning. however, some learning management systems are still very complex and difficult to use. In this study, we developed an educational media application that was focused for learning basic level. The purpose of this research is that students can carry out learning activities using learning media independently. Steps being taken in the development of this application including problem identification, data collection and analysis, design, implementation and testing. To obtain complete data, data collection techniques by observing and distributing questionnaires were applied. Desktop-based application development using the Java language. Then perform the test by testing the applied black box and the results are obtained that the system being developed can run optimally on all features.
Sentiment Analysis in Reviews About Beaches in Bali on Tripadvisor Using Recurrent Neural Network (RNN)

The tourism sector utilizes information and communication technology in promoting Indonesian tourism so tourists can obtain information about Indonesian tourism through websites, one of which is Tripadvisor. As one of the marine tourism spots in Indonesia, Bali uses the review from Tripadvisor web in developing, evaluating, and promoting its tourist attractions. However, there is a discrepancy between the rating and the review given by the user, and the review contains a paradoxical figure of speech in one review. This study is about a sentiment analysis on reviews of the five most favorite beaches in Bali: Double Six, Seminyak, Nusa Dua, Kelingking, and Canggu, using the Recurrent Neural Network (RNN) in the Tripadvisor website. By approaching in 4 steps: preprocessing data (word stemming, lemmatization, data labeling, tokenization), TF-IDF weighting, classification using RNN, and performance evaluation. The result of sentiment analysis is that positive labels dominate. This study also calculated the value of precision, recall, F1-Score, and ROC curve for each beach. The accuracy gets more than 80&#x0025;. ROC curve is classified as &#x201C;Fair Classification&#x201D; and &#x201C;Good classification&#x201D; with AUC values between 0.71-0.87. This proves that RNN has good performance and accuracy in performing sentiment analysis on reviews of the five most favorite beaches in Bali.
The Arrangement of Convolutional Neural Network Layers for Digit Speech Recognition

Digit Speech Recognition (DSR) is speech recognition based on the utterance of digit: one through nine. Itincluded isolated recognition (one word). The system begins with the digit as input data audio. Furthermore, the audio is converted into a digital signal and became a spectrogram whichis a 2-dimensional shape of the signal. The spectrogram is processed to obtain features using the Convolutional Neural Network (CNN). These features determine the results of recognition. The architecture that built for the system consists of a 24 layers: an input layer, five convolutional layers, five batch normalization, five Rectified Linear Units (ReLU), four max-pooling layers, dropouts, fully connected layers, softmax, and classification layers. Testing uses minibatch-size 64, learning rate 3e-4, max-epoch 25. Optimization uses Root Means SquarePropagation (RMSProp). The percentage data experiment consists of 60:20:20 for training, validation, and testing. The results showed precision, recall, and accuracy were 95.91&#x0025;, 96.19&#x0025;, 95.88&#x0025; respectively.
Kohonen-SOM LOF Approach for Anomaly Detection

Water loss in distribution is a serious problem for water companies, as it can result in a loss of profit. Machine learning has a method for dealing with this, for example, anomaly detection. Anomalies in the study were detected using data from March 2017 to February 2018. The data used is water consumption, and the variables such as mean water use, maximum water use, and standard deviation of water usage were obtained. Kohonen Self-Organizing Map (SOM) and Local Outlier Factor (LOF) are used because they can create a good cluster, and LOF can detect which one of them is filled with anomalies. The Kohonen SOM algorithm produced 45 groups that were considered anomalous because their silhouette width criteria were less than the group&#x0027;s average silhouette width. There are 45 different groups of unexpected anomalies. The LOF generated 1229 unusual consumption events, with each incident involving 579 households or customers. The frequency calculation yields 41 customers who are suspected of being anomalous. While the baseline method from the company only captures 16 anomalies out of 41 customers. This is because the company method fails to capture unusual consumption behaviors, such as monthly consumption. The anomaly detected the customer&#x0027;s characteristic of having an average of more than average usage of classes and sub-zones.
The Material Nature of Software

Algorithms are sets of instructions (a type of declarative and operational statements in a language) to process sets of bits (minimal units of information) through the manipulation of sets of binary states of electronic devices. The secret of algorithms is that they are a mix of linguistical and electronic entities. Indeed, algorithm is another name for program or software in informatics technology. So, to answer the question “What are the algorithms made of?” we need to have a view of the very nature of computational content of a software statement. We will see that computational instructions in informatics technology are really instructions for manipulating physical signals loaded with information content. And we will see that this is just the same case we find in the philosophy of mathematics in general: the nature of algorithms is the same as the nature of mathematical entities.