Reviewerly/exHarmony huggingface dataset:
A Comparison of Oversampling and Undersampling Methods in Sentiment Analysis Regarding Indonesia Fuel Price Increase Using Support Vector Machine

On September 03, 2022, the Indonesian Government has decided to issue a policy to increase fuel prices. Fuel subsidies are converted into direct cash assistance (BLT) to protect the poor and vulnerable. However, the increase affects people's daily activities and eventually raises a debate in the community which is widely discussed in social media such as Twitter. There are various opinions expressed by the Indonesian people, such as approvals, criticisms, and insults. In this research, sentiment analysis was conducted on public opinion from Twitter to determine public sentiment regarding fuel prices increase policy. Sentiment analysis in this study uses a machine learning approach with Support Vector Machine (SVM) algorithm to classify each opinion into positive or negative sentiment. The split between training and testing data is carried out in a ratio of 70:30. A comparison was made between four imbalance handling methods, such as Synthetic Minority Over-Sampling Technique (SMOTE), Random Undersampling (RUS), Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbors (SMOTE-ENN), and Synthetic Minority Over-Sampling Technique-Tomek Links (SMOTE-Tomek). The evaluation process uses a confusion matrix that will show accuracy, recall, precision, and F-1 score. The SMOTE method produces the highest F1-score of 57,42%, 0.02% superior to SMOTE-Tomek method and 7.03% compared to the RUS method. The best model based on this study is the SVM model using SMOTE for imbalance handling because it produces an accuracy score of 87,94%, precision score of 48,22% recall score of 70.95%, and F1-score of 57,42%.
Calibration and Distortion Compensation Algorithms of Digital Cameras Used in UAV

The paper presents algorithms for calibrating and correcting the distortions of a digital photo camera on board the UAV in order to reduce the distortions in the obtained images, which provides a more accurate analysis of details. A model of the main internal parameters of the camera is presented, supplemented with the distortion coefficients, and a procedure for external calibration and self-calibration of the camera to determine the optimal values of these parameters. A genetic algorithm is proposed for minimizing the objective function, image correction, and calibrating and correcting camera distortions. The results show that using the proposed algorithm, the reduction of pixel bias is more than 100 times, and the obtained estimates of the internal parameters are close to the real ones and can be used to correct the distortions
Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models

Breast cancer is the most common type of cancer worldwide. According to the World Health Organization (WHO), there were 7.8 million women alive in 2020 who had been diagnosed with breast cancer, and it has claimed more women's lives than any other kind of cancer. With the recent rise of artificial intelligence, breast cancer detection using deep learning techniques is getting more popular. However, creating a deep learning model for a specific task from scratch costs a lot of time and money. Transfer learning is a well-known method that can make deep learning developments more efficient by leveraging pre-trained models. Using the BreakHis dataset, this paper will compare three cutting-edge pre-trained computer vision models: DenseNet, RegNet, and BiT, in predicting malignant or benign tumor tissue from breast histopathology images to determine which model is better for that specific task. Although the DenseNet model achieves the highest score with 93.7% Area Under the ROC Curve (AUC) and 97.4% Average Precision Score (APS), the BiT model is more suitable for deployment in a real-world setting since it can predict more malignant cases correctly than the other two models with a sensitivity score of 90.79%.
A Survey of Food Delivery Innovation Evolution in Developing Countries: Insights from Indonesia

The food service industry is one of the most promising businesses today, and the availability of food delivery is one of the digital transformations that has occurred in this industry. The continuous development of innovation in this business promises to increase success and revenue. This research examines how food delivery is one of the innovations that plays a crucial role in the sustainability of restaurants/food merchants. The exploratory scope in this survey study is based on research questions, starting with the process evolution in food delivery over time. This includes the influence of three technological eras: cellular telephones, websites, and mobile operating systems, which have an evolutionary impact on the business concept point of view, from restaurant-based delivery to the emergence of e-commerce. The research also explores the benefits and challenges that arise during adoption, with 5 benefits identified, including improved customer experience, increased order efficiency, increased order flexibility, improved food control, and crisis management. The challenges that arise during adoption include inconsistent delivery and quality, technology dependency, partnership dissatisfaction, and health and environmental issues. Based on the findings of this study, there is an opportunity to design a technology adoption model for online food delivery in Indonesia in the future.
Real Time Air-Writing and Recognition of Tamil Alphabets Using Deep Learning

Writing has always been a prominent way of communication. The way in which the letters are written has been varying with time. From the conventional pen and paper to touch pad and stylus, the way of writing has evolved. Air- Writing is another development in which the characters are written in free space without being limited to a specific tool. This method of writing makes the hand movement easier compared to the conventional methods. Therefore, the air writing and recognition model will be of great help for children who start learning a language. The trajectory of the air written characters is obtained by mapping the focal point using Optical flow in OpenCV. The obtained trajectory is then preprocessed and given to Dense Net 121 which is a type of CNN model widely used for pattern matching along with the dataset from HP labs which contains 3000 images for 11 Tamil vowels. The model which is trained obtained a maximum training and validation accuracy of 98.2% and 91.83% respectively with minimum training and validation loss of 6.35% and 21.04% respectively.
Computer Vision and Image Segmentation: LBW Automation Technique

Cricket is globally a famous game in which different technologies are being used to help the umpire in correct decision management. The bowl delivery is whether a fair one or LBW which is generally a matter of concern. Sometimes the decision of third umpire also goes wrong. This kind of error in making the decision can change the dimension of the game so, it is essential to make the decision precisely. This paper proposes computer vision and image subtraction technique. At first, it must figure out that whether a delivery is no-ball or the fair one, for that image processing method is used on popping crease by detecting the pixel size. If the delivery is a fair one then, it will track the movement of the ball by using the colour segmentation method, video processing on the pitch. Then by the help of ultra-edge detection technique it can identify whether the ball has touched the edge of the bat or not.
Data Security: A Systematic Literature Review and Critical Analysis

This paper systematically reviews the existing literature on data security. This study aims to identify current research directions, identify the main challenges facing data security, and suggest future research directions in this field. The research method is a structured approach to searching and selecting relevant papers based on predefined inclusion and exclusion criteria. The selected articles were then analyzed to identify common themes, methodologies, and main findings. The study reveals vital research directions, such as cloud data security, IoT data security, data encryption, data security attack analysis, and data security policy. Challenges identified include scalability in data security, privacy, and regulatory compliance, increasingly complex attack threats, and a lack of awareness of the importance of data security. Based on these findings, the study also recommends future research, including developing innovative security technologies, data security awareness and training, and research into data security's ethical and legal aspects.
Business Process Redesign of Sales Monitoring Business Process Management to be a Smart Logistic Shipment Company

The sales process in every company is important. Therefore, every sales record must be stored in an integrated database so that tracking sales records is easy. However, there are still many sales recording practices that are still done manually, making the process of recording sales complicated and time-consuming. Therefore, it needs a sales record automation process that can track all sales recording processes that provide standardization in the form of KPIs and progress at each stage of sales records. With the growth of smart logistics, it can drastically change the business process of dashboard monitoring in a logistics company. With the smart logistic concepts and the use of business process redesign methodology, it resulted in a more simplified yet integrated automated sales monitoring system.
Convolutions of String Data Structures: A Case Study of Finding Number of Palindrome Substrings in a String

In computer science, substring search or string Matching is a vulnerable problem when text resources are very large. Productivity of diverse scraping applications depend on the effectiveness of searching algorithms. Palindrome string finding is one of these problems which has its use in many applications. The problem is about finding the number of palindrome substrings within a string. This paper discusses a naive and dynamic programming-based algorithm for finding several palindrome strings. The time complexity of algorithm has also been analyzed and an effort has been made to improve it.
Sports Activity Recognition - Shot Put, Discus, Hammer and Javelin Throw

This paper describes the classification of sports activities using still images. Sports activities specifically include discus throw, shot put and javelin throw. This system is useful for athletes preparing for these sports and it will help them to understand and practice the right posture for these sports. Along with them, this model is also helpful to the coaches to train their players and increase their chances of winning. Dataset is created by collecting the different images of the following sports from different sources. Various machine learning algorithms such as random forest, KNN, SVM (Polynomial), voting classifier has been applied to the model out of which SVM (poly) performed better than the rest with an accuracy of 98.7%. Voting classifier and Random Forest has also performed well. BRISK feature descriptor method is found useful to get point of interest in the image dataset. Techniques like image augmentation used for creating artificial dataset to which is useful for training the machine learning model and Principal Component Analysis (PCA) used for dimensionality reduction is found helpful to increase the accuracy of the model.
User Acceptance of Contact Tracing Apps: A Study During the Covid-19 Pandemic

The unpredicted coronavirus outbreak, termed COVID-19, has placed numerous governments worldwide in a difficult position. The scarcity of resources to tackle the outbreak, combined with the fear of overburdening the healthcare system, has forced most countries into a state of lockdown. Many governments have shown great interest in digital contact tracing applications that can help automate the demanding task of tracking newly infected individuals’ recent contacts. However, these apps have created a great deal of discussion, especially regarding their technology, architecture, and the adoption rate needed. This study aims to contribute to an increased understanding of the acceptance of this technology in the Norwegian population. Based on the unified theory of acceptance and use of technology (UTAUT) model, our research model incorporates the following five constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and privacy consideration. A survey was distributed amongst the Norwegian population, and the results were obtained from a sample of 258 respondents. The results from this study indicate that performance expectancy has the most significant impact on the intention to use a contact tracing application. Privacy considerations are also important, followed by effort expectancy and social influence. Facilitating conditions were found to be much less important.
Tumor Kidney Segmentation from CT Images Using Residual U-Net Architecture

Kidney cancer is a highly heterogeneous disease, with renal cell carcinoma being the most prevalent and severe form. Diagnosis typically involves analyzing computerized tomography (CT) scans, and Deep Learning technology has shown great potential for automating medical imaging classification and segmentation. This technology can greatly assist physicians in early tumor detection and localization, as well as determining its size and type, allowing for more timely treatment planning. In this study, we present a segmentation-trained model that was developed using a dataset of 300 patients. The dataset underwent preprocessing, normalization, and data augmentation techniques to increase variance. The proposed method utilized a convolutional neural network with a residual U-Net model, incorporating 3D convolutional blocks architecture for four-class segmentation. We achieved an average Dice coefficient of 68% through cross-validation of the trained model, we achieved significant efficiency using less epochs by getting better results than other models, that translate in reduction of computational cost of approximately 55% compared to similar model. Our findings suggest that the proposed method may improve the accuracy and efficiency of kidney cancer diagnosis, providing physicians with a valuable tool for early detection and treatment planning. This study highlights the potential of Deep Learning technology in medical imaging applications and contributes to the growing body of research in this field.
headweAR: Development of Augmented Reality Based on Hat Tester to Support Product Testing

Augmented reality technology application in variety of sectors is becoming more common in line with progress of technological advances towards the digital future. However, the experience of online shopping that lacks in virtual dressing room deter consumers to make the purchase decisions. Existing application for virtual dressing room is yet to include headwear products in software applications. Therefore, this project is proposed to develop an augmented reality mobile application that helps user to visualize virtual hat onto user’s head. The targeted users for this project are adults aged 20 to 30 years old who are the customers of The Cap City. Multimedia Mobile Content Development (MMCD) model is implemented to develop the headweAR application. Based on user testing results, more than 80% of the respondents left positive feedback on the proposed application. Development with additional 3D models and multiple face tracking are some of the future works of this markerless AR-based application.
Performance Analysis of You Only Look Once, RetinaNet, and Single Shot Detector Applied to Vehicle Detection and Counting

Artificial intelligence has experienced significant growth in recent decades. As a result, several architectures have been developed for object detection, classification, and recognition. Currently, there are several alternatives that fulfill these purposes; however, there is no rigid framework defining how these architectures are formed. This work presents an updated performance analysis of real-time vehicle detection and counting using You Only Look Once (YOLOv8) version 8, RetinaNet (RN), and Single Shot Detector (SSD). For such analysis, the Google Colaboratory was used as the main retraining environment. The Research-Action methodology was employed to develop the practical case, and a systematic literature review was also conducted to determine the state of the art in this problem domain. For feature extraction, RESNET-50 and MobileNet were used in RN and SSD, respectively. The results indicated that YOLOv8 (which has undergone the most adjustments since its inception) exhibits the best performance in terms of detection time, precision considering the frames that need to be analyzed to enable real-time usage, and ease of implementation.
Hyperparameter Tuning in a Dual Channel U-Net for Medical Image Segmentation

Deep learning has been receiving a lot of attention lately, specially in the computer vision research community. In particular, the image segmentation task has revolutionized the treatment of medical imagery for diagnose and illness prediction. U-Net is one of the most extensively employed convolutional neural network (CNN) architectures for medical image segmentation. Moreover, U-Net is recognized to excel in segmenting multimodal medical images in general and complicated circumstances. However, the traditional U-Net architecture still has significant limitations with medical images segmentation. To tackle this limitations, a novel CNN architecture called dual channel-UNet (DC-UNet) has emerged as a possible replacement for the U-Net model. However, the performance and accuracy of the model are not the greatest when inferring on endoscopy images. This paper contributes to the research field by proposing a hyperparameter tuning of DC-UNet on the CVC-ClinicDB endoscopy dataset. Among several factors, we determine the optimum hyperparameter setup for this dataset, considering different values of: (i) batch sizes, (ii) learning rates, (iii) gradient-based optimizers, and (iv) dropout layers. We have also included a contrast limited adaptive histogram equalization, as a preprocessing technique to analyze the effect of the different experiments on the model’s performance.
Multidimensional Sentiment Analysis of Tourism Object in DKI Jakarta, Banten, East Java, Central Java and West Java using Support Vector Machine Algorithm

Information technology provides several conveniences, one of which is facilitating tourists in searching for information about tourist attractions. One of the services that can be used is Google My Business. The comments or reviews from tourists are numerous, thus requiring a significant amount of time to review them one by one. Therefore, a method is needed to address this issue, which is sentiment analysis. In this study, a multidimensional sentiment analysis was conducted on tourist attractions in the provinces of DKI Jakarta, Banten, East Java, Central Java, and West Java. Data was collected between January and March 2023 using the Data Miner tool. Two labeling techniques, namely Transformer and Textblob, were compared for labeling the data. The confusion matrix was employed as the evaluation tool, and the Support Vector Machine (SVM) technique was used to implement sentiment analysis. Labeling using transformers obtains an accuracy of 0.8169 or 81.6%, then the average precision, recall and f1-score are 68%, 54%, abd 58%. While labeling using textblob, obtained an accuracy of 0.9257 or 92.5%, the average precision is 88%, recall and f1-score results are 87%. The accuracy, precision, recall, and f1-score results indicate that labeling using Textblob outperforms the labeling using Transformers.
Search and Visualization of Researcher Networks: Co-authorship in Ecuador

The quality of scientific research is based on collaboration among experts. In Ecuador, an increase in scientific production has been observed, which has generated the need for an efficient computer tool to form networks, search for collaborators, and analyze research topics. In response to this, a web application has been developed that utilizes data mining models for searching and visualizing networks of researchers affiliated with Ecuadorian institutions and their academic areas. The project was implemented following the Scrum and CRoss Industry Standard Process for Data Mining (CRISP-DM) methodologies. The two methodologies showed an easy integration by having to not only develop a web application, but also carry out the pre-processing and modeling of data that later feed the functionalities in the platform. The data was extracted from Scopus through the set of APIs offered by Elsevier. The generated retrival models, which were the result of the DM process, were based on TF-IDF (term frequency - inverse document frequency). Finally, these are employed to obtain the best results in the different kinds of searches performed within the application. The architecture of the application consists of three layers: i) the Neo4j graph-oriented database as the data layer, ii) Flask as the backend that supports the business logic, and iii) Angular as the frontend, which utilizes libraries such as Bootstrap for design and D3.js for creating dynamic and interactive graphs. The software prototype offers three types of search: author search, relevant authors search by topic, and relevant articles search by topic.
Digital Watermark Techniques and Its Embedded and Extraction Process

The development of the digital communication system is increasing rapidly and all the manual data management process is massively converted to the digital processing system to make faster processing and reduce the valuable time of human users and increase productivity. Now the input and output system of the device is converted to a higher-level interface medium so that the device can communicate with the user and environment through audiovisual components and communicate like a human. Also in the present scenario devices are implemented by human society to deal with very secure and sensitive information like banking, the military, the research environment, the medical sector, etc. Hence there is a chance of misutilization of information through the device. Many Security using mathematical techniques like cryptography, digital signature, etc. are used to make secure information from the unauthentic person, but to protect the copywriting of the digital information watermark technology is used. Through the watermark, we can add the identification of the owner hence the other person can not claim or tamper with the information for its misutilization. In this proposed work we reviewed different techniques to create a watermark with the algorithm associated with it which helps the researcher extend the watermark technique in a better way.
Automatic Code Clone Detection Technique Using SDG

The complexity of software is increasing day-by-day. For faster and convenient implementation the software developer used to write similar codes in several places of software and these code segments are known as code clone. During the maintenance, if we know the places of code clones then it will be very effective. In this paper, we have proposed an unique technique of code clone detection using System Dependence Graph (SDG). The SDG for a program is the pictorial representation of the control and data flow in the program. The SDGs are generated automatically by the ASM API. We have created the SDGs for two programs and by analysing the SDGs we have detected the code clones. With case studies we have explained and demonstrated the working of our proposed technique. As an advantage of this the maintenance and reliability of the software will enhance.