Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Text Document Using Machine Learning Models for Information Retrieval

Text mining is thought to have a high commercial potential due to the significant amounts of unstructured text data produced on the Internet. The practice of obtaining previously undiscovered, comprehensible, potentially useful patterns or knowledge from a corpus of text data is known as text mining. In this study, we attempt to extract the structured information from the text and then use various machine-learning models to categorize the data. We then look for the model that provides the highest level of classification accuracy.
Comparative Study of Deep learning Techniques for Cryptocurrencies

Cryptocurrencies are digital currency form that utilizes an online distributed ledger technology called a blockchain. Cryptocurrency exhibits excellent features such as immutability, security, and decentralization. However, the cryptocurrency price varies in the market due to several reasons. Recently, cryptocurrency price prediction has been a concern for several researchers. The price prediction of cryptocurrency is a global research subject matter. Several deep learning and machine learning algorithms were utilized in current research to predict cryptocurrency prices. This present study intends to resolve this issue by reviewing research includes prediction of the cryptocurrency closing price in the market at a particular time. Several existing studies revealed that it is optional to predict the precise value of the future cryptocurrency price to acquire gains in the financial sector. This predicament can be handled with a knowledge of deep learning algorithms and analysis of distinguishing features of different algorithms.
Consignment of Epidermoid Carcinoma using BPN, RBFN and Chebyshev Neural Network

Oral cancer is the most dangerous and fast-spreading cancer in the world with the least survival rate. Oral cancer appears in and around the mouth. Of the oral epithelial carcinoma, cancer that occurs in the lip has the lowest mortality rate and tongue cancer has the highest mortality rate. Early-stage identification and treatment of Oral Squamous Cell Carcinoma is the most important aspect to avoid or reduce the rate of mortality. For early detection, other than physical examinations, various imaging techniques are available such as histopathological imaging, biomedical imaging, Ultrasound, MRI, CT, and Confocal scanning techniques. Multiclass classification is a major role in a real-world application. Tumor, Node and Metastasis are the most important aspect in the staging of oral cancer. Artificial Neural Networks and Machine Learning Techniques work much more effectively to provide better accuracy in medical image classification. For the classification of Oral Squamous Cell Carcinoma images and non-cancerous images, it is tested with three different networks viz Backpropagation network (BPN), Radial Basis Function network (RBF) and Chebyshev neural network. Dimensionality reduction has been done using Principal Component Analysis and explained using a variance plot. The r-square coefficient, which gauges how well the data fit the regression line, shows that, when compared to raw data, the PCA components boost the data's dependability. Of these BPN works well in the Epithelial carcinoma histopathological image classification when compared with RBF and Chebyshev network.
Interviewing Young Adults about Cryptocurrency Literacy and Investment Intention

As the world becomes more digitalized, decentralized financial transactions using digital currency known as cryptocurrency may become more accepted and used by the general public. 16 young adults were interviewed in a qualitative research about their basic cryptocurrency literacy and investment intention. The interviewees were asked open-ended questions about their knowledge on how to make cryptocurrency transactions, willingness to invest in cryptocurrency, barriers to cryptocurrency investment intention, and their future vision of cryptocurrency. The results showed a lack of basic cryptocurrency literacy amongst young adults, high unwillingness or reluctance to invest in cryptocurrency due to its poor reputation, and uncertainty about the future of cryptocurrency. Numerous barriers that must be overcome to make cryptocurrency more acceptable to general public use include addressing their issues of untrustworthiness, riskiness, and volatility, as well as improving certain conditions of the cryptocurrency market with increased stability, security, and more regulation to discourage criminal activities. If the issues with using cryptocurrency are addressed, digital marketing and banking will be revolutionized.
Addressing Data Scarcity in Thai Car Recognition Using Few-Shot Learning

This paper aims to address the issues of data scarcity and fine-grained classification in car recognition, specifically in the Thai context, using a few-shot learning model. To achieve this, we conduct experiments to compare several models and establish DN4 as a baseline model. We collect and analyze performance data from various training variables (N-way K-shot) and backbone architectures. Furthermore, we propose the hard task sampler module for few-shot training, which generates more difficult tasks based on the similarity matrix derived from the model's discriminative performance. We test our proposed model on the Comprehensive Cars (CompCars) dataset, which is split into CompCars (non-TH) and CompCars (TH) based on car model popularity in Thailand. By using the best settings from our experiments, our model significantly improves the accuracy of the DN4 baseline. Notably, the accuracy is increased from 85.7% to 92.70% on the CompCars (non-TH) dataset and from 82.44% to 90.28% on the CompCars (TH) dataset. Overall, our approach shows promising results in addressing the challenges of data scarcity and fine-grained classification in car recognition in the Thai context.
Private Permission Blockchain for Optimized Invoice Management System

Blockchain technology has become popular in the recent years due to Bitcoin and Ethereum which are potential in terms of security, transparency and sharing in the same network by Peer-To-Peer network. Though this technology has high costing, it is worth to use in the businesses to reduce fraudulences from inside and outside of organization. Invoice system is a technology that all companies invest to protect the data of invoice management because it is important for analysis by the trends of business each year. In this paper the use of blockchain technology to protect the data of invoice by developing the invoice management system with smart contract for invoice rules to approve, reject, and to protect the data is proposed to find out changes of invoice in blockchain network system by a web application in intranet to manage the limitation of small business. The conceptual framework and the output obtained by the developed system shows the feasibility to develop a private blockchain system for invoice management.
Sentiment Analysis Towards Russia - Ukrainian Conflict: Analysis of Comments on Reddit

The conflict between Ukraine and Russia has gained widespread attention across the entire globe and become a prominent topic on social media platforms. The primary sources for knowledge regarding people’s attitudes and opinions about various such subjects and issues are social networks. Reddit is one such platform where users can publish longer posts with more content that offer more perspectives on the topic unlike Twitter and Facebook, where most postings are short texts. In this work, we have provided the sentiment analysis for the ongoing Russia-Ukrainian crisis based on a list of relevant subreddits. The main objective of this research is to ascertain how the people felt generally about the conflict and how their opinions have evolved over time. Our approach involves comprehending the text, summarizing it, and assigning weight to its positivity, negativity, or neutrality. In order to conduct sentiment analysis on Reddit comments, we utilize the VADER sentiment analysis tool and the Multinomial Naïve Bayes model which has been found to be the most effective one when compared with several other models.
Digital Transformation in Higher Education for Developing Countries: Systematic Literature Approach

This study aims to conduct a systematic analysis of the recent investigations on the process of higher education's digital transformation, particularly in developing nations. Publish or Perish v8 Software was used to conduct a systematic literature review. Initially, there were 40 papers selected, but after several search strategies as follows: Determination of search phrases based on research questions; Finding suitable search terms in titles, abstracts, and keywords; Identification of search term synonyms, other spellings, and antonyms, the number of articles to be examined has been decreased to 30. Through descriptive analysis, we try to respond to these two research questions: 1) the process of digital transformation in developing countries' higher education, and 2) what challenges do these developments present? Based on the analysis, the process of digital transformation lies in 4 processes which are planning, organizing, controlling, and evaluating. The process involved was the internal business process and the educational program. While the challenges come from the lack of expertise, lack of facilities, and uncertain economic condition. Several strategies are needed to support the digital transformation in developing countries' higher education.
DEVELOPMENT OF COLUMN-BASE JOINT USING LAG-SCREW BOLTS WITH MECHANISM TO CONTROL STRENGTH AND TOUGHNESS

Pull-out fracture of lag-screw bolt is brittle and has a large dispersion in strength. therefore, the bucking of the pipe preceding the pull-out of the lag-screw bolt controls the strength and toughness of the joint. The pipe is sandwiched between the lag-screw bolts fixed to the base plate. The method of bucking the pipe has little dispersion, and the strength and toughness can be controlled by adjusting the thickness and length of the plate. The effect was confirmed by experiments. and it was also confirmed that once the maximum load is reached, it can be restored by exchanging the pipe.
Ingest and Visualize CSV Files using AWS Platform For Transition from Unstructured to Structured Data

In addition to computing, storage, databases, analytics, networking, cellular, developer equipment, management equipment, IoT, security, and agency packages, AWS provides a comprehensive range of pay-as-you-go, on-demand, global cloud-based services. Accessible AWS services and solutions number over 200 and include information warehousing, directories, and content distribution. New offers can be immediately provisioned without an upfront capital cost. This makes it possible for businesses, start-ups, small and medium-sized organizations, and clients in the public sector to obtain access to the fundamentals needed to quickly adapt to changing business requirements. The technology at the core of any cloud operation is virtualization. It asserts that virtualization breaks up a physical server’s hardware resources into smaller bits. Data processing and visualization on Amazon Web Services is the process of gathering, analyzing, and visualizing large amounts of data using a variety of AWS services (AWS). Data storage, integration, analysis, and visualization are just a few of the many services that Amazon offers that may be used to manage and display data. With the help of AWS services like Amazon S3, Amazon Kinesis, and Amazon Redshift, huge amounts of data can be acquired, analyzed, and stored. Amazon S3 is a simple storage service that may be used to store and retrieve data, Amazon Kinesis is a real-time data streaming service, and Amazon Redshift is a data warehousing service. Using AWS Glue, Redshift, and QuickSight, we demonstrate a method for processing and displaying CSV files in this paper In author’s suggested approach, we extract, convert, and load data from CSV files to Redshift, a cloud-based data warehouse, using AWS Glue. The data saved in Redshift is then visualized using QuickSight, enabling quick and simple data exploration. We also go over how using AWS Glue, Redshift, and QuickSight can scale effectively and fast, automate data processing activities, and cut expenses compared to using conventional data processing and storage techniques.
User Experience Analysis on the My Indihome For Technician Application Using the Heuristic Evaluation Method

MyIndiHome for Technician application is a mobile application owned by PT. Telkom Access which functions to be used by technicians at work. However, the problem faced is how to find out the results of the user experience evaluation on the MyIndiHome for Technician application using the heuristic evaluation method. The heuristic indicators that will be tested in this study are visibility of system status, the match between the system and the real world, user control and freedom, consis,tency and standards, error prevention, recognition rather than recall, flexibility and efficiency of use, aesthetic and minimalist design, help users recognize, diagnose, recover from errors, and help and documentation. From the results of the research that has been carried out, it is found that the average MyIndiHome for Technician application user has a very good rating level category on each heuristic indicator tested. In addition, researchers will make improvements to the application design on the indicators that have the lowest scores in the questionnaire to be used as suggestions for improvement for developers for the convenience of MyIndiHome for Technician application users.
A Transfer Learning Approach for Tyre Wear Classification

Automobiles have become an indispensable mode of transportation and travel as the economy has grown. Tires are vital components of automobiles, and their wear contributes to a huge number of road accidents. As a result, anticipating tyre life has become one of the most important criteria to avoid accidents. The proposed work classifies the tire wear condition such as No wear, Light Wear, Moderate Wear, High Wear, and Extreme Wear using a pre-trained deep learning model. The first tyre dataset was created with 1403 total images in 5 classes. Then the tyre dataset was used to classify the tire wear condition by the pre-trained model ResNet50. When compared to other pre-trained models such as SqueezNet, ResNet18, ResNet101, and InceptionResNet, the ResNet50 provided better recognition accuracy of 95.1%. The performance of the proposed method was compared with state of art methods.
Towards Good Promotion Preparation with SIDODO (Catur Dharma Lecturer e-Documentation Application)

BINUS University is a leading university in Indonesia and a private university that has obtained a distance learning permit (PJJ) known as BINUS Online Learning. According to Law No. 12 of 2012 Article 1 paragraph 9 concerning Higher Education contains “Tridharma of higher education, hereinafter referred to as Tridharma, is the obligation of Higher Education to organize Education, Research and Community Service”. Binus University, which is one of the universities in Indonesia, needs to carry out the mandate of Law No. 12 of 2012 which is called Catur Dharma Faculty member. The obligation to carry out Catur Dharma Faculty members at Binus Online Learning determines the calculation of KPI FM and the realization of KPI departments. The unavailability of an information system that supports the activities of Catur Dharma Faculty members at Binus Online Learning makes every activity that has been carried out not properly documented and monitored, to answer the need for the required information system, it is necessary to develop an E-Documentation information system for chess dharma faculty members in online learning based on web applications. The development of this information system uses the system development life cycle with the Agile Scrum methodology. At the testing stage, UAT will be used with a Black Box Test combined with a Likert scale to assess user satisfaction with features that meet the needs of the user using the SERVQUAL method.
Twitter Sentiment Analysis on Online Learning During COVID-19 Pandemic

Globally, the COVID-19 epidemic has produced significant changes in the higher education industry. Online education has grown in popularity because it facilitates access to knowledge and courses at any time and location. Institutions of higher education must understand how individuals feel about online learning and what the trends are. This is because online learning facilitates access to education, hence altering how individuals learn. Several recent studies have employed a variety of methodologies to examine the efficacy of online learning. Students and parents are quite concerned about the efficacy of online education, particularly in terms of how well it functions for students and professors and how well it functions technologically in various social circumstances. Before online learning can be utilized on such a big scale, challenges of this nature must be examined from every viewpoint. As a precaution, this study aims to determine how well the concept of online learning works by examining how individuals feel about tweets on Twitter, the most prominent social networking site. Various machine learning classifiers were then used to analyze the sentiment. These classifiers employed a range of attributes and extraction techniques to determine how individuals felt about a particular topic.
Learning Extended Term Frequency-Inverse Document Frequency (TF-IDF++) for Depression Screening From Sentences in Thai Blog Post

This paper proposed the method of depression screening from a sentence in Thai blog posts. Three classifiers based on a decision tree, linear SVC, and logistic regression were used to create classification models; each learned from extended term frequency-inverse document frequency (TF-IDF++) which is a feature vector created from a term frequency-inverse document frequency (TF-IDF), part-of-speech, and statistics of sentences such as word counts of selected terms. Our experiments showed that the model based on logistic regression achieves the top average score with a precision of 78.32%, a recall of 78.26%, and an f1-score of 78.27%. The proposed method outperforms the Thai BERT model by 0.75%, 0.77%, and 0.76%, respectively. Our investigation also showed that excessive confidence in the Thai BERT model tends to classify a sample with high probability. This also happens in case of an incorrect prediction; the error in such a case becomes noticeably higher than that of the wrong prediction in our proposed logistic regression-based model.
Toward Developing a Password Breach Detection Technique for Serving the Purpose of Honeywords

In the era of computer systems, user authentication, both online and offline, is an unavoidable step for securing users’ privacy. Password-based authentication is popularly adopted for its simplicity in this context. In password-based authentication, a set of credentials (mostly username and password) is required to identify the unique user. But this method of authentication is vulnerable to inversion attack paradigm. In inversion attack, the adversary obtains the plaintext password by cracking the hashed value of the password. Honeyword-based authentication has been introduced to combat such attacks. In this strategy, certain dummy passwords or honeywords are saved along with the user’s original password. When an adversary tries to enter one of the honeywords to log into the system, an alarm message is sent to the authority via an auxiliary server called honeychecker. Although this technique is useful to address this type of security threat, the requirement of additional space to store the honeywords is still an overhead. Driven by these drawbacks, this work is aimed to propose a strategy which can serve the purpose of honeywords but in a more cost-effective way. In this technique, the concept of tokenization is utilized. Theoretical and experimental analyses have been done to assess the viability of the proposed scheme. A comparative study between the proposed scheme and honeyword-based authentication has been carried out based on required storage cost and resiliency against MSV attack. From our rigorous analysis, it is found that our scheme shows promising results in terms of other usability and security features as well.
Consideration of Self-teaching System for Basketball Shot Form by LSTM

We develop a system that supports self-training in the shoot form of basketball to reduce teaching costs of club activities based on pose estimation. Advice for improving students' shoot form is generated based on time-series machine learning using LSTM.
Virtual Reality Based Training Simulator: A Bibliometric Analysis

Innovation in immersive technologies is growing rapidly throughout the world with the development in industrial applications. In VR technology, a user is completely immersed in the virtual environment and totally detached from real world but feel the virtual world as real. This helps user to experience some scenario which is dangerous and costly in real time. Hence many VR Based Training Simulators (VRBTS) are developed. This paper presents Bibliometrix analysis on VRBTS domain, considering data from Scopus and Dimension databases. A scientific computer-assisted review approach called bibliometrix analysis can identify important researchers or authors as well as their connections across all the publications relevant to a specific field or subject.
Implementation of Hybrid Cryptography in Steganography for Augmented Security

Now-a-days, many billions and trillions of data exchanged over the internet per second. Ensuring the security of data is a major priority in the realm of information technology.To provide data security, many technologies have been evolved like cryptography, steganography, visual cryptography, watermarking, etc,. This article combines both cryptography and steganography for providing security. Using modified hill cipher algorithms, it first turns the original message into a secret message. Traditionally, only the alphabets are encrypted with hill cipher. But, the proposed modified hill cipher algorithm can encrypt any type of message using Radix64 encoding/decoding conversion. And the keys which are used in hill cipher algorithm encrypted using modified RSA algorithm. The 2-bit LSB technique is then used to embed the encrypted keys and secret message into the cover image to create the stego-image. Then, the stego-image is transferred to the recipient side through secure channel. The recipient must follow the entire process backwards after receiving the stego-image in order to receive the original message. Finally, the parameters of several performance measures such as Entropy, MSE, PSNR, and histogram are measured. A substantial improvement in efficiency and security can be observed with the proposed system.