Reviewerly/exHarmony huggingface dataset:
Real Time Object Detection for Aiding Visually Impaired using Deep Learning

This research aims to create an assistive device for the people who are suffering from vision loss or impairment. The device is designed for blind people to overcome the daily challenges they face which may be perceived to be trivial to normal people. The device is created by using advance computer science technologies such as deep learning, computer vision and internet of things. The device created would be able to detect and classify daily objects and give a voice feedback to the user who is handicapped with blindness.
Instrumen Evaluasi Teks Berita Dalam Buku Teks BSE Bahasa Indonesia

This article aims to describe the suitability of the newsprint evaluation instrument contained in the Indonesian Language BSE textbook with the 2013 Curriculum competency standards. This type of research is a qualitative research. The research method used is content analysis, namely by combining the appropriateness of the contents of the evaluation instrument with the basic competencies in the 2013 Curriculum. The data sources of this research are a number of evaluation instruments about news text teaching materials contained in the BSE Indonesian language textbook for SMP class VIII. Based on the research that has been done, data obtained, that the level of conformity of the evaluation instrument with the basic competencies of the 2013 Curriculum related to news text teaching materials is 66%, while the discrepancy is 33%. The results of this study can be used as information to determine the quality of the evaluation instruments contained in Indonesian textbooks.
Flipped Classroom Model: Empowering Digital Literacy for Mathematics Learning in Society 5.0

In the era of society 5.0, technology and education are likely two side of coin. Teachers can use technology in the classroom. Also, teachers play a role in maximizing the quality of learning. This study employed descriptive qualitative research. This study aimed produced an overview of flipped classroom used in learning activities, particularly in learning mathematics. The research data in this study were observations, semi-structure interviews, and documentation of flipped classroom learning activities. The research subjects were 37 seventh-grade students of one of junior high school in Purwokerto, Indonesia. The data analysis technique in this study consisted of four stages:1) data collection, 2) data reduction, 3) data presentation and 4) drawing conclusions. The results of this study are: 1) flipped classroom improves teachers and students digital literacy; 2) flipped classroom as one alternative learning model; 3) flipped classroom expressed students’ positive activities with technology; and 4) flipped classroom provide opportunities for parents interact with students in home activities.
Monocular Depth Estimation using Transfer learning-An Overview

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.
Indication for copper mineralization associated with metabasalt in gaudurarea, hutti-maski schist belt, Raichur District, Karnataka

This report presents preliminary results of indications for sulfide mineralization near Gaudur in Raichur district, Karnataka. Indications for copper mineralization are seen in the sheared and altered schistose metabasalt of Hutti Schist Belt. The mineralization is confined in N-S to NNW-SSE direction with dip ranging from vertical to sub-vertical. Petrographic study reveals that metabasalt have undergone green schist facies metamorphism. The metabasalt is characterized by presence ofchlorite, epidote, albite, actinolite, calcite and minor quartz. Alteration in the form of chloritization and epidotization of ferromagnesian minerals is also observed. Numerous anastomosing quartz-calcite veins are seen parallel to the foliation and shear planes. At places, the outcrops ofschistose metabasalt show profuse staining by secondary copper minerals like malachite and azurite. Freshly broken rock shows presence of sulfides as disseminations and also along quartz-calcite veins. Bed rock samples of mineralized metabasalt show high values with some grab samples showing copper values up to 8.0%. The metabasalt is associated with granites in the study area which are predominantly dioritic and pink porphyritic granite which show intrusive relationship with the metabasalt. The contact zone of metabasalt and granitoids are generally sheared and could be the possible loci for sulfide mineralization. Petrographic and EPMA studies of ore samples suggest that there are two stages of mineralizing event. The first one is of syngenetic type which occurs as fine disseminations within the schistose metabasalt and the second one is epigenetic, represented by remobilized sulfides along veins. Although the Hutti Schist Belt is well known for gold mineralization, presence of indications for copper mineralization within the metabasalt near Gaudur show that it may have a potential for copper mineralization as well.
Data Mining Using K-Means Clustering Algorithm for Grouping Countries of Origin of Foreign Tourist

Indonesia has enormous potential to develop the tourism sector. The role of the tourism sector in Indonesias economic development is increasingly important. The contribution has been made by the tourism sector through foreign exchange earnings, regional income, regional development, investment, and employment increment as well as business development across various areas in Indonesia. One of the governments targets in the tourism sector is to increase foreign tourist visits. Grouping or clustering the countries of origin of the tourists need to be done to help the government in determining strategies. This study uses the K-means clustering algorithm to classify the data on the country of origin of tourists and evaluate the clusters using silhouette score for determining the appropriate number of clusters. The result of the silhouette score shows that K = 2 has a value of 0.8, which is the best cluster that can be used to classify data on the country of origin of tourists. Based on the test results of the clusters, both of the clusters were then identified as cluster 1 for the category of low visitors with 206 members and cluster 2 for the category of high visitors with 6 members, namely Malaysia, Singapore, China, Other Asia, Timor Leste, and Australia. The results of the clustering process are expected to be input data for further performance, namely mapping the right marketing strategy for the countries visiting Indonesia so as to increase foreign tourist visits to Indonesia.
A New Generalized t Distribution Based on a Distribution Construction Method

In this paper, a new generalized t (new Gt) distribution based on a distribution construction approach is proposed and proved to be suitable for fitting both the data with high kurtosis and heavy tail. The main innovation of this article consists of four parts. First of all, the main characteristics and properties of this new distribution are outined. Secondly, we derive the explicit expression for the moments of order statistics as well as its corresponding variance–covariance matrix. Thirdly, we focus on the parameter estimation of this new Gt distribution and introduce several estimation methods, such as a modified method of moments (MMOM), a maximum likelihood estimation (MLE) using the EM algorithm, a novel iterative algorithm to acquire MLE, and improved probability weighted moments (IPWM). Through simulation studies, it can be concluded that the IPWM estimation performs better than the MLE using the EM algorithm and the MMOM in general. The newly-proposed iterative algorithm has better performance than the EM algorithm when the sample kurtosis is greater than 2.7. For four parameters of the new Gt distribution, a profile maximum likelihood approach using the EM algorithm is developed to deal with the estimation problem and obtain acceptable.
A Survey on the Effectiveness of Virtual Reality-based Therapy and Pain Management

Virtual reality refers to the technology used to create multi-sensory three-dimensional environments that can be navigated, manipulated, and interacted by a user.This paper's objective is to categorize the most common areas that use virtual reality (VR) for managing pain (psychological and physical).To our knowledge, this is the first survey that summarizes all of these areas in one place.This paper reviews the conducted studies that used VR for psychological treatment, especially with phobias.Also, this paper summarizes the current literature on using virtual reality interventions for managing acute, chronic, and cancer pain.Based on the review, virtual reality shows great potential for controlling acute pain -such as pain associated with burn wound care.However, limited studies only investigated the impact of using virtual reality on patients with chronic pain.The findings indicated that VR distraction has a great impact on pain and distress related to cancer and its treatments.This paper also discusses the challenges and limitations of the current research.Notably, the identified studies recommend VR distraction as a promising adjunct for pain reduction and psychological treatment.However, further research needs to be conducted to determine under what conditions VR distraction will provide more analgesic effects.
Streamed Video Reconstruction for Firefox Browser Forensics

In criminal investigations, the digital evidence extracted from social media may provide exceptional support. Reviewing the history or cache of the web browser may provide a valuable insight into the activity of the suspect. The growing popularity of Internet video streaming creates a risk of this technology misuse. There are a few published research on video reconstruction forensics on the Chrome browser. There is a difference in the methods applied to reconstruct cached video on Chrome from the methods applied to Firefox or any browser. Our primary focus in this research is to examine the forensic procedures required to reconstruct cached video stream data using Twitter and YouTube on the Firefox browser. Some work has been done to reconstruct a cached video on the Chrome browser, but we need more work on the rest of the browsers, most notably the Firefox browser used in this research. Both examination strategies and contemplations displayed are approved and suitable for the forensic study of various streaming platforms as well as the web browser caches.
Identification of Network Traffic over IOT Platforms

The Internet of Things (IoT) is a rising infrastructure of 21st century. The classification of traffic over IoT networks is attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the normal traffic from the malicious traffic and to assign the normal traffic to the proper destination to suffice the QoS requirements of the IoT users. Detection of malicious traffic can be done by continuously monitoring traffic for suspicious links, files, connection created and received, unrecognised protocol/port numbers, and suspicious Destination/Source IP combinations. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this work, adaboost and Xgboost machine learning algorithms and Deep Neural Networks approach are proposed to separate the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. The result of experiment indicates a deep learning algorithm achieves higher accuracy compared to machine learning algorithms.
Transducer Function In Vending Machine Simulation Design

A vending machine is a substitute for a cashier or admin in the form of a machine so that buyers simply enter coins and the machine will issue them according to the request. Moore machine and mealy machines as a process for processing buyer orders which are translated into machine orders. Based on the test results of coins entered 1000 and 500, the drink can be chosen according to the price of the drink.
Wavelet Analysis of Red Noise and Its Application in Climate Diagnosis

Signals are often destroyed by various kinds of noises. A common way to statistically assess the significance of a broad spectral peak in signals and the synchronization between signals is to compare with simple noise processes. At present, wavelet analysis of red noise is studied limitedly and there is no general formula on the distribution of the wavelet power spectrum of red noise. Moreover, the distribution of the wavelet phase of red noise is also unknown. In this paper, for any given real/analytic wavelet, we will use a rigorous statistical framework to obtain the distribution of the wavelet power spectrum and wavelet phase of red noise and apply these formulas in climate diagnosis.
Pengaruh Motivasi Berprestasi dan Persepsi Siswa Tentang Cara Guru Mengajar Terhadap Hasil Belajar Matematika Siswa

This study aims to: (1) find out how achievement motivation affects the mathematics learning outcomes; (2) knowing how the influence by student perceptions of how teachers teach on mathematics learning outcomes; and (3) to find out how the influence of achievement motivation and student perceptions about how teachers teach on the mathematics learning outcomes at VIII grade students of SMPN 16 Mataram in academic year 2019/2020. This type of research is ex post facto research. The population of this study were all students at VIII grade as many as 110 students consisting of classes A, B, C and D. The sample used was saturated sampling where the entire population was the sample in this study. The data collection technique used was a questionnaire and tests. This research uses prerequisite test analysis techniques and inferential analysis techniques. The results obtained show: (a) There is a significant effect of achievement motivation on the mathematics learning outcomes with a contribution of 10.1%; (b) there is a significant influence on student perceptions of how teachers teach on mathematics learning outcomes with a contribution of 17.8%; and (c) there is a significant influence on achievement motivation and student perceptions of how teachers teach on mathematics learning outcomes with a contribution of 20.1%.
Pemanfaatan Teknologi Informasi Untuk Meningkatkan Kemampuan Perencanaan dan Kematangan Karir Siswa

This study aims to analyze the use of information technology in planning and career maturity of students. This study uses a systematic literature review approach. The journals used in this systematic literature review were obtained through a database of national and international journal providers through Google Scholar which were then analyzed systematically. The results of this study indicate that the use of information technology can improve planning and career maturity in students. Both the media such as blogs, multimedia and the use of the internet can be adequate resources for student career advancement.
Practical Aspects of Vulnerability Detection During Certification Tests of Information Security Software

Purpose: analysis of various techniques and techniques for identifying defects and vulnerabilities during certification tests. Research method: comparative analysis. Result: the conclusion is made about the relevance and priority of the study of open-source web applications. The study is given and the shortcomings of directive methods for identifying vulnerabilities and undeclared capabilities in software products are shown. The author’s statistics of the identified vulnerabilities are given with detailing by classes of computer attacks, manufacturers of information security tools, programming environments and methods for identifying vulnerabilities. A comparative analysis of author’s methods with known directive testing methods is given. The relevance of the implementation of the concept of developing secure software is shown. Recommendations on improving the security of software tools for information protection are given.
Summarization and Sentiment Analysis for Financial News

Abstract: Data is growing at an unimaginable speed around us, but what part of it is really useful information? Business leaders, financial analysts, stock market enthusiasts, researchers etc. often need to go through a plethora of news articles and data every day, and this time spent may not even result in any fruitful insights. Considering such a huge volume of data, there is difficulty in gaining precise, relevant information and interpreting the overall sentiment portrayed by the article. The proposed method helps in conceptualizing a tool that takes financial news from selected and trusted online sources as an input and gives a summary of the same along with a basic positive, negative or neutral sentiment. Here it is assumed that the tool user is familiar with the company’s profile. Based on the input (company name/symbol) given by the user, the corresponding news articles will be fetched using web scraping. All these articles will then be summarized to gain succinct and to the point information. An overall sentiment about the company will be portrayed based on the different important features in the article about the company. Keywords: Financial News; Summarization; Sentiment Analysis.
Peter the Great’s Lost Letter to Georg Wilhelm de Henning: On the History of Expert Thought and the Antiques Market

The elements of expert analysis established in Russian academic circles by the late nineteenth century, when auxiliary historical disciplines became an indispensable part of the academic base of historical knowledge, are described in this article with reference to a lost letter by Peter I to Georg Wilhelm de Henning sent on 24 December 1724. These elements include the study of the letter, the stages and methods of introducing it into scholarly circulation, and the assessment of its significance and value in monetary terms. It is established that the original of the letter ended up in a private collection in the early twentieth century and became inaccessible to historians. However, handwritten copies, a draft, and descriptions have survived. The article analyses the work with Peter I’s letter performed by the members of the commission for the publication of Peter the Great’s letters and papers, as well as by the first owner of the letter, N. K. Bogushevsky, the Parisian antiquarian Noël Charavay, into whose hands the letter fell a decade after its former owner’s death, and N. P. Likhachev, a prominent specialist in a number of auxiliary historical disciplines to whom the antiquary turned for an expert opinion. While researching the copies and descriptions of Peter I’s letter, the authors used traditional methods of expert analysis of the missing original found in the collections of St Petersburg Institute of History (Russian Academy of Sciences), the Russian State Archive of Ancient Acts, and the National Archives of France. Referring to handwritten materials from the Russian State Archive of Literature and Arts, the Russian State Library, and sales catalogues, the authors carry out a brief analysis of the Western European and Russian antiques market in the late nineteenth – early twentieth centuries in relation to Russian documents from the eighteenth century (prices and demand). The attribution of authenticity to historical documents (including the first Russian emperor’s autographs) was not only an issue of the reliability of historical knowledge or academic interest, but also one of reputation of connoisseurs, collectors, antiquarians, and the experts they turned to. At the same time, reputation helped ensure the quality of expertise and was a tool for raising this through horizontal connections within the professional community.
Electronic Voting Implementation Through Bitcoin Blockchain Technology

Even with all the advances we have seen in secure digital technology, the most secure way to currently cast a vote on election day consist of a hand-marked paper ballot.When extenuating circumstances arise, offering a voting environment that is accessible and safe for everyone, but also secure can be a difficult task under the current voting system.This paper discusses one proposed electronic voting system which uses blockchain technology.Based on a review of literature on blockchain technology and specific implementations of voting systems, a summary of relevant background information as well as implementation protocol are provided.Even though experts believe that societies are not currently ready to implement systems like the one described in this paper, the technology to create a secure and efficient system does exist, and could one day become available.
Sentiment Analysis of Ten Tourism Destinations in Indonesia

Sentiment analysis is frequently used to examine textual data to determine the sentiment contained in a sentence. This method can be used to determine the polarity of emotions associated with specific themes, events, products, and certain phenomena in the world. This study utilized Twitter data to analyze textual data in Bahasa Indonesia gathered via Twitter using R. This study will focus on ten tourism destinations. These destinations were targeted for development by the Indonesian government in 2016 through the Ministry of Tourism. The purpose of this study was to determine the sentiment polarity of Indonesian Twitter users towards ten tourism destinations. The sentiment polarity score was calculated using an Indonesian sentiment lexicon. The results of this study indicate that the Indonesian government's designation of a tourism destination as a development priority destination appears to have no effect on the tourism destination's sentiment polarity, particularly among Twitter users. The popularity status of a tourist destination does not translate to a high score on positive sentiment. Due to the limitation of the sentiment lexicon in Bahasa Indonesia, it can be inferred from this study that getting an accurate sentiment score for each tweet is still not achievable. However, the sentiment lexicon in Bahasa Indonesia should be updated to increase the accuracy of the sentiment score calculation. Keywords: Indonesian sentiment analysis, Indonesian sentiment lexicon, Tourism destinations.