Reviewerly/exHarmony huggingface dataset:
COMPARATIVE STUDY ON SENTIMENTAL ANALYSIS AND OPINION MINING THROUGH ONLINE CUSTOMER REVIEWS

In daily life, consumer opinions are quite important. When we need to make a decision, we consider the opinions of other individuals. On blogs, review websites, and social networking sites nowadays, many internet users express their opinions on a wide range of items. The number of people using the internet to purchase goods is rising, and there are more and more results being stored online as a result. As a consequence, more users are writing reviews or comments every day. Organisations in the business and corporate world are always curious to hear what customers or other people have to say about their products, services, and support.Today, if someone has to buy something online, they may first read reviews left by previous customers on the product website and then make the best choice possible. Making judgments may be challenging due to the abundance of online comments regarding a certain product. As a result, opinion mining is employed to categorise the evaluations based on their polarity. Opinion mining is the process of obtaining opinions from reviews (OM). Any user making a decision about a service or company should take customer feedback into account. Opinion mining is also known as sentiment analysis. Our major objective is to create a method for studying views, which necessitates evaluating different consumer goods.
Project Monitoring with Decision Support System for the University Infrastructure Development Office

Project Monitoring is the process of keeping track of all project-related metrics that includes task duration and team performance, identifying potential problems and taking corrective actions necessary to ensure that each project is within the scope, budget, and specified timeframe. The University Infrastructure Development Office still has the traditional way of manually keeping the records of each project such as printed copy. The risk of having a permanent file document that is being produced as hard copy can be vulnerable to fire, flood, insects and so much more where we cannot retrieve those important files in case of those unexpected situations.&#x0D; This research project employed the Agile Software Development Methodology to develop an Online Project Monitoring System that can be used by the university in the infrastructure development section to give them a much more organized project tracking, reviewing and regulating the progress in order to meet the performance objectives of each project across the university. The system aims to help the institution to digitize the printed records of projects in the university in an organized manner and stored securely in a database system. The researcher adapted the ISO/IEC 25010 criteria as an evaluation tool to assess the system’s functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability.&#x0D; The assessment and evaluation results of the system indicates that the PMDSS passed and conformed with the standard software quality requirements and met all the end-users expectation and objectives that will greatly help them in monitoring of projects and making them more productive. This proves that the system can improve the management of running projects across the university and decision making of the Infrastructure Development Office.
Question Generation System Using NLP

Manually creating interesting and relevant questions for subjective and objective tests is a timeconsuming and tough task for teachers.This technique is useful in Natural Language Processing, but it is a demanding process.This application generates questions that are both subjective and multiple-choice.In summary, the BERT approach is used, and then subsequent phrases are mapped to generate MCQs.The question paper is created by randomly picking questions from the Bloom's Taxonomy.Parts of speech tagging (POS Tagging) is used to generate questions.T5 _base is an NLP-based system that creates questions for objective and multiplechoice questions.This tool assists teachers in question generation, saving them time and effort.The development of question papers helps to address the manpower shortfall.
The New Revolution in Language Learning: The Power of Artificial Intelligence and Education 4.0

This article explores the connection between artificial intelligence (AI) and language learning in the context of Education 4.0, highlighting how the former revolutionizes the latter with the introduction of emerging technologies and innovations in education. The article discusses how AI improves the processes of language learning through personalized learning experiences, interactive practice, and automated assessment. AI can be used to create diverse learning materials and immersive experiences that align with the principles of Education 4.0. When used correctly, AI can bring numerous benefits to language learning, such as increased efficiency, greater student engagement in the teaching-learning process, and the accessibility of content from anywhere and on any device. Additionally, it emphasizes the need to adopt Education 4.0 accompanied by the development of content that equips students with the necessary skills in the digital age. The article also highlights the importance of integrating AI and Education 4.0 in language learning to promote critical thinking, problem-solving skills, and digital literacy
SLAM based vision self-navigation robot with RTAB-MAP algorithm

The classic 2D SLAM are not good enough in nowadays environment. This report uses virtual machine with Ubuntu based Slam_bot package, based on the RTAB-MAP algorithm and Vision SLAM mapping to simulate the four-wheeled robot to autonomously navigate to the target point in various environments. Also, this report introduces a RGBD-SLAM based algorithm which combines the visual and depth data to process the data collect from the sensors. This robot has many sensors like, lidar sensors, RGB vision camera and odometry sensors. To see how the RTAB-MAP algorithm with RGB-D sensor replace for the 2D SALM. As results, the robot with RGB-D and RTAB-MAP algorithms have very good performance. The results show that the navigation system can complete the navigation and localization in lots of complex situations. However, some problems still exist, the speed of the robot is not fast. This may limit the application of the self-navigation robot to a certain extent, like some emergency occasion.
A survey of face recognition based on machine learning

Face recognition technology based on machine learning methods is the most popular nowadays. This research, based on existing literature and data, was mainly focused on the face recognition system. The paper is written on the basis of existing face detection methods. Then, according to the difference in feature information, five methods were used to extract face features to apply them to face recognition. Then, this paper focused on two kinds of neural networks: support vector machines (SVM) and integrated learning methods, especially the Adaboost algorithm. Finally, the hidden danger of privacy disclosure was specially discussed. The results showed that the main direction of future research should be to expand the functional dimension and promote more applications to the public on the basis of ensuring the real-time and accuracy of the system.
Using the NARDL Approach for Measuring the Asymmetric Relationship of Cryptocurrency on Exchange Rate: Evidence from Thailand

This study examines the asymmetric relationship between three major cryptocurrencies (Bitcoin, Ethereum, and Tether) and three exchange rates in Thailand (THB/USD, THB/CNY, and THB/JPY), before and during the Covid-19 outbreak through a NARDL framework. Interestingly, this study noticed that increases and decreases in Tether showed more considerable influence on the movement of THB/USD, THB/CNY, and THB/JPY than either Bitcoin or Ethereum in both sub-periods. Given the results from the NARDL approach, increases and decreases in all three cryptocurrencies (BTC, ETH, USDT) confirm the long-run asymmetric association on THB/USD, THB/CNY and THB/JPY during the Covid-19 outbreak. Compared to the period before the pandemic, only two exchange rates-THB/USD and THB/CNY, were affected by the movement of all cryptocurrencies under study. Additionally, the results from an asymmetric short-run relationship suggest that a decrease in Bitcoin and Ethereum resulted in volatility of THB/USD, THB/CNY, and THB/JPY during the Covid-19 pandemic. Before the pandemic, both increases and decreases in Tether are correlated with the changing of the THB/USD, THB/CNY, and THB/JPY. The findings of this study could be essential for development of financial policies and currency market regulation rules in Thailand.
DESAIN BACKGROUND DAN FONT SLIMS PERPUSTAKAAN STTM KEBUMEN DENGAN METODE USER CENTERED DESIGN (UCD)

The Library of Muhammadiyah Technology High School of Kebumen (Sekolah Tinggi Teknologi Muhammadiyah - STTM) Kebumen has a various books collection, such as journals, scientific books, social, language, engineering, magazines, and Al-Qur'an. However, the data processing in the STTM's library is currently using the conventional method of recording member data, book data, borrower data, return data, and payment penalty for late returning the book to a document. SLiMS is an open-source web-based library management system application licensed under the GPL (General Public License). This research is on how to design the Background and fonts of the SLiMS for STTM's library using the User Centered Design (UCD) method. The stages of this research begin with the identificate of the user needs and a pleasant and interactive design. The display design was created by using background images of the STTM Kebumen campus, with Arial fonts, cover book figures, search columns, and social media buttons. The validation of SLiMS display to design of STTM library used Usability Test. The result of the validation test with the Usability Scale System is good, which a value is 77.3%. It means that the display of SLiMS design was a successfull built like a user-friendly system with height usability.
Image Data Extraction and Driving Behavior Analysis Based on Geographic Information and Driving Data

Driving behavior analysis has become crucial for traffic safety. In addition, more abundant driving data are needed to analyze driving behavior more comprehensively and thus improve traffic safety. This paper proposes an approach to image data extraction and driving behavior analysis that uses geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was performed to derive the relationship between aggressive driving behavior and road facilities. The results indicated that lane ratios, no lane markings, and straight lane markings are important features that affect aggressive driving behaviors. Several traffic improvements were proposed for specific intersections and roads to make drivers and pedestrians safer.
Sentiment Analysis of the COVID-19 Booster Vaccine with the Naïve Bayes Algorithm

In early 2020, a new deadly virus was discovered that can spread quickly, called SARS-CoV-2 or Coronavirus Disease 2019 (COVID-19). Indonesia is a country with a relatively high number of survivors of COVID-19. The success of the Indonesian government in providing massive COVID-19 vaccinations can minimize the risk of death. The World Health Organization (WHO) stated it would revoke the Public Health Emergency of International Concern (PHEIC) status for COVID-19 in May 2023. However, many positive cases of Covid-19 were still found in Indonesia. Most of them are survivors who have not carried out complete vaccinations until they get a booster vaccine. This study analyzes public sentiment about booster vaccines in Indonesia using the Naïve Bayes classification algorithm. The results showed that the classification modeling accuracy had an excellent value of 97.51%. In contrast, based on the analysis results, the number of words that frequently appeared in the twelve highest word cloud visualization results found tokens that had positive sentiment values.
Some Aspects of Information Security and Cybersecurity Problem Area

Design and development of Information Security System (ISS) is complex process.The developer of ISS must have all needed information about the requirements, which is defined in Problem Area.One approach is to perform an analysis from different perspectives of the interested parties, which give a complex picture of the problem area.Through a risk analysis, the system developers can select the necessary operations and activities so that the tasks in front of the ISS are fulfilled.An analysis of the problem area is done, with focus to two viewpoints -Information and Cybersecurity.It is important to make some clarifications and find common and different aspects between them.This is done with the help of some basic concepts and their relations, of the both cybersecurity and information security domain -vulnerability, threats, threat actors, threat vectors and attacks.An analysis to both attacks and Approaches to protection (AP), which are protection measures in the form of actions, processes or procedures taken to protect an information system from attacks on the Confidentiality, Integrity and Availability of the ISS is done.
Smart Fit On Using Machine Learning and Deep Learning Techniques

Our society needs to know what people are doing for different reasons such as keeping people safe and tracking lifestyle and behavior.It's hard to watch everyone all the time, but sensors that respond to movement can help recognize what someone is doing.Many people have smartphones with sensors, making this kind of recognition more useful.To classify human activities this research looks at using different techniques using data from smartphone sensors.They compare the accuracy and efficiency of different methods.
Review on FPGA-based accelerators in convolutional neural network

In current research, an important means to realize artificial intelligence is artificial neural network. Many tedious problemscan be solved by artificial neural network, such as image classification, speech recognition, natural language processing. Among neural networks, although convolutional neural network has significantly better performance in the field of image recognition, it requires many parameters and a large amount of computation, and the number of network layers is gradually increasing with the progress of the algorithm, which leads to model sizes are getting larger and more difficult to handle, which has more stringent requirements on various aspects of hardware capabilities, such as computing power, data storage and memory bandwidth. Therefore, the research of acceleration is particularly important, especially in the field of hardware acceleration. And as an emerging hardware platform, field programmable gate array has the characteristics of rapid customization and programmability, which can greatly improve the efficiency of accelerators design, implementation, and verification. As a result, it can be widely used in the hardware-accelerated design of neural networks. Although field programmable gate array itself has some defects, many research results have proved its huge advantages and development potential. This paper mainly summarizes the acceleration of convolutional neural network based on field programmable gate array platform, and discusses its characteristics and application space by comparing with other platforms, showing its high applicability to convolutional neural network, and finally looking forward to the progress of related research work.
Sentiment prediction based on neural network

Recent years have seen a surge in interest in deep learning, and numerous new neural network models have appeared. In this study, we use CNN and LSTM, two popular deep learning models, to predict sentiment. We use the bidirectional model for the LSTM model. We make predictions by integrating the feature vectors of both sides after receiving sentences inputted in the reverse order from both ends. The recurrent neural network layer is replaced with the CNN model, which defines numerous one-dimensional convolution kernels to conduct convolution operations on the input. The two models are assessed and compared based on how well they anticipate and analyze sentence emotion tones. By comparison, we discover that the BiLSTM model's prediction accuracy on the test set is 10% greater than CNN's, despite requiring more training time.
Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC

In the recent years, the boom in technology industries has been greatly accelerated by the development of artificial intelligence (AI). AI, which is based on machine learning (ML), can only be developed rapidly because of the continuously increasing computational capacity of AI processors. Compared to general-purpose processors (GPPs), AI processors have specially designed architectures to accelerate the operations of AI applications, such as convolution, matrix, and massive parallel computing. The objectives of this paper are: (1) to illustrate the differences between general-purpose processors and AI processors; (2) to summarise the characteristic three mainstream AI processors: GPU, FPGA and ASIC, and draw a comparison among them. It shows that GPUs provide very competitive performance with high power consumption; FPGAs can offer high efficiency at low cost; and AISCs provide the highest performance with the lowest power consumption, but cost the most.
To describe the content of image: The view from image captioning

The aim of developing the technology of "image captioning," which integrates natural language and computer processing, is to automatically give descriptions for photographs by the machine itself. The work can be separated into two parts, which depends on correctly comprehending both language and images from a semantic and syntactic perspective. In light of the growing body of information on the subject, it is getting harder to stay abreast of the most recent advancements in the area of image captioning. Nevertheless, the review papers that are now available don't go into enough detail about those findings. The approaches, benchmarks, datasets, and assessment metrics currently in use for picture captioning are reviewed in this work. The majority of the field's ongoing study is concentrated on robust learning-based techniques, where deep reinforcement, adversarial learning, and attention processes all seem to be at the heart of this research area. Image captioning entails a brand-new field in research on computer vision. Generating a comprehensive natural language description for the source images is the fundamental issue of image captioning. This essay explores and evaluates earlier work on image captioning. Image captioning's application and task situations are introduced. The merits and disadvantages of each approach are explored after the analysis of the image captioning algorithms based on encoder-decoder and template structure. The assessment and baseline dataset for picture captioning are therefore shown. Ultimately, prospects for image captioning's progress are presented.
Machine learning algorithm and training in GoTake three influential program as example

In 2017, AlphaGo, an artificial intelligence in Go, beat KeJie----the No.1 Go player in 3-0, which have surprised the world, and artificial intelligence came to the attention of the public again. In this article, we take three influential artificial intelligence Go----AlphaGo, AlphaGo Zero and KataGo, as example to discuss how artificial intelligence Go work. We discuss them about their structures and training methods one by one in chronological order, which can also show the process of their development. In addition, some of the structures and training methods are enlightening to us, and we expect them can work in other fields.
Sensor-based gesture recognition with convolutional neural networks

Sensor-based gesture recognition is an active research field of great significance with a wide range of applications in control systems for virtual reality, medical monitoring, and abnormal behavior determination. This problem has drawn the attention from both the academia and industry and many methods are proposed in the literature. Recently, deep learning has been widely applied in sensor-based gesture recognition and achieved good effects. In this paper, we proposed a classifier model based on Convolutional Neural Network (CNN) and applied it to EMG-based and smartphone-based datasets, respectively. For these two datasets, our model both achieved better classification accuracies than traditional machine learning models, with the results of approximately 97% and 72% accuracies, respectively. We also analyzed the effects of different parameters on the results of the proposed CNN model.
SPEAKING ACTIVITIES IMPLEMENTED IN AN ENGLISH CLUB: STUDENTS’ VOICES

AnEnglish club has many important goals to support the fluency of students in mastering English skills, especially in speaking skills. Sometimes, it brings positive and negative drawbacks to the students with some activities provided. The study aimed to find out how students felt about the different types of speaking activities that were hosted by the English club. Qualitative research approaches were utilized in this case study approach to research. The participants were three students who were members of the English club at one of senior high schools in Palembang. The information was acquired by conducting an interview that was semi-structured and using open-ended questions. The data were evaluated by employing a methodology known as theme analysis. The outcomes of the study revealed that (a) debate activity did not improve speakers' pronunciation, comprehension, or grammar, (b) speech activity assisted the students' speaking components, which included pronunciation, vocabulary, comprehension, fluency, and grammar in the process of speaking, (c) students were able to practice English speaking skills such as pronunciation, vocabulary, comprehension, fluency, and grammar through story-based activities, (d) activities related to newscasting did not aid the student in instructing and learning vocabulary.
Building SaaS solutions using microsoft azure for achieving safe and secure tax related software

Several services offered by Microsoft Azure Cloud Platform are used for building, running and managing applications. These Services include Azure Active Directory (AD), Azure Virtual Machines (VMs), Azure App Services, Azure DevOps etc have been used to develop this system which can manage all real estate tax servicing needs. It significantly reduces servicing costs with the Tax Outsourcing platform. This system is a SaaS product that many customers request to maintain current tax status on loan portfolios to ensure taxes are paid on time. The contracted loans include tax identification numbers and parcel numbers, current year taxes, back taxes that are past due, information about tax redemption, etc.