Reviewerly/exHarmony huggingface dataset:
Computer Vision for Autonomous Vehicles-Semantic Segmentation using Jetson Nano

Over the past 100 years, Automobile Industry has seen steady innovation with the help of Technology on par with other fields. Self-driven cars have taken the world by storm and its efficiency has been proved to be better than calculated. Despite its popularity, its yet to be tested and green-lit for Indian roads, and that is the motivation behind this project. This demonstration is a scaled down approach to the world of Computer vision that deals in Vehicle Automation. The central processing unit of this project is NVIDIA Jetson Nano [1]; it is a compact AI compute Module which can be used to learn and create intuitive Deep Learning based applications. The main 3 objectives of the project are to Apply Convolutional neural networks to perform object detection, to train the application and increase scalability using transferred learning technique to make it work in native environment and to experiment with fully-convolutional semantic segmentation networks on a live camera stream.
Outdoor Social Distancing Violation System Detection Using YOLO Algorithm

The government issued orders to implement social distancing or physical distancing. Social distancing is a method of maintaining a distance of at least one meter from other people. This is useful for reducing/preventing disease transmission (virus) and reducing the chain of the spread of covid-19. So the hospital can provide optimal service. For this reason, this research is structured to create a system that can detect violations of social distancing in an open place. This system uses the You Only Look Once (YOLO) algorithm. The developed system uses a pre-trained Yolov4 model to detect 80 object classes. Testing of this system is carried out based on several scenarios. The system is programmed using Python, with tools for coding Microsoft Visual Studio Code and Anaconda. The best result from creating the detection mode is obtained from a dataset ratio of 90% train data and 10% test data, with the mean average precision results obtained being 54.11%.
Optimal Trajectory Planning Generation for Autonomous Vehicle Using Frenet Reference Path

Intelligent vehicle technology has developed rapidly by automotive companies around the world. The main objective of smart vehicle technology is the realization of an autonomous vehicles system. In this research, a trajectory planning system is designed using the frenet reference path and optimization for autonomous vehicles. The resulting vehicle trajectory is the optimal path from several candidate paths after the optimization process. The used cost function considers dynamic safety and comfort cost, also allowing the vehicle to avoid moving obstacles. There are two results from the path planning system, namely the position value of cartesian coordinates (x,y) and vehicle orientation (θ). From the results of the optimal trajectory, it can be determined the value of curvature and lateral offset concerning time. The largest curvature values are 0.005 m and - 0.0053 m.
Variance-Based Geometric Feature Selection for Face Recognition System

In a feature-based facial recognition system, geometric features are usually combined with other features to produce reliable facial recognition system. There are thousands of geometric features on a face. The difficult part in using geometric feature is determining how many features and choosing which features to use. The best feature selection method is to tests all feature combinations in the system, which is called Holistic Testing, but it is impossible to run this method because of the huge number of feature combination. Several papers use geometric features without performing feature selection process, resulting a less optimal facial recognition system. This paper proposes a feature selection method using variance value approach. The proposed method is applied to 14-point facial landmark dataset based on the FRGC 3D facial database. The variance value is used to select candidate features that make the holistic testing feasible to performed. Based on our experiment, the highest recognition accuracy is obtained in the use of 27 features, they give 92% of recognition accuracy. The whole features selection requires around 8768 second to complete. This paper also compares the selected features used in some previous papers with our selected features. By applying the features in our recognition system, our features give better performance with the same dataset. At the end, this paper concludes that, in order to obtain a good accuracy result, it is important to perform a feature selection process before using them in facial recognition system.
Age Estimation from Face Image using Discrete Cosine Transform Feature and Artificial Neural Network

Advances in technology and information allow the application of technology to be part of the decision-making system in shopping centers. The rapid development of shopping centers requires decision-makers to take advantage of opportunities appropriately. The decision-making process can run according to needs with the right analysis and prediction. One aspect that can help the analysis process is identifying visitors by their facial image. The application of age estimation automation technology through face identification can be one solution for identifying visitors. Where visitor age data is available, it can be used in the analysis and decision-making process. So that decision-makers can manage the right market according to their needs. The face identification process is carried out with specific data, using facial image data based on ages 1 - 116 years. Age estimation methods can be used in conjunction with various feature extraction methods. Discrete Cosine Transform (DCT) is a feature extraction process that can change an image that initially had many features with a low correlation level into fewer features that has a higher meaning or level of significance. So, this study uses DCT features and Artificial Neural Network (ANN) architecture as methods for age estimation. The proposed model can recognize and predict the age range from 1 to 11 with an MAE value of 9,40 on the testing data.
Twitter-based Sentiment Analysis for Indonesian Drug Products using Supervised Feature Engineering

Social media communications offer valuable feedback to firms about their products. Twitter users share their opinions about e-commerce products on social networking sites. This paper reports a study in sentiment classification for Indonesian drug products for automatically classifying Twitter data as expressing positive or negative sentiments or opinions. The study investigates the effectiveness of using a Decision Tree, Support Vector Machine, Random Forest, Na rve Bayes, and Logistic Regression, along with the term frequency-inverse document frequency used to classify 531 tweets into recommended (positive sentiment) and not recommended (negative sentiment). Among all traditional machine learning models, the highest average accuracy value is 88.6 in the RF model and the lowest average accuracy value is 80.2 when using the LR model. Judging from the average accuracy obtained, the DT and RF model used has a considerable performance
NLP Text Classification for COVID-19 Automatic Detection from Radiology Report in Indonesian Language

Radiology is used as an important assessment for patients with pulmonary disease. The radiology images are usually accompanied by a written report from a radiologist to be passed to the other referring physicians. These radiology reports are written in a natural language where they can have different systematic structures based on the language used. In our study, the radiology reports were collected from an Indonesian hospital and written in Bahasa Indonesia. We performed an automatic text classification to differentiate the information written in the radiology reports into two classes, COVID-19 and non COVID-19. To find the best model, we evaluated several embedding techniques available for Bahasa and five Machine Learning (ML) models, namely (1) XGBoost, (2) fastText, (3) LSTM, (4) Bi-LSTM and (5) IndoBERT. The result shows that IndoBERT outperformed the others with an accuracy of 98%. In terms of training speed, the shallow neural network architecture implemented with the fastText library can train the model in under one second and still result in a reasonably good accuracy of 86%.
Using of Machine Learning Techniques to detect Credit Card Frauds

Today is the era of digital money and the government is also promoting it to reduce corruption. People tend to use ATM cards and various credit cards for completing their transactions, but they are unaware of the fraudulent risks associated with them. A fraudulent transaction is conducted by an attacker using other's information and it causes Billions of dollars loss every year. Efficient fraud detection algorithms can be used to reduce the losses. These algorithms depend on advanced machine learning techniques, which can be helpful for fraud investigators. This paper is in two folds. First is to explore the various techniques proposed by various researchers to detect credit card frauds and second is to implement machine learning models to find frauds in credit card transactions. The aim is to identify all fraudulent transactions while reducing the number of incorrect fraud classifications. The types of credit card frauds and issues with credit card fraud detection techniques are also discussed in this paper.
Development of Learning Media for The Deaf Using a Webcam

The lack of interactive sign language learning media makes it difficult for the deaf to find support for interactive sign language learning. Therefore, SIBI sign language interactive learning media is needed that can support interactive learning with computers as official sign language used in Indonesia. KNN method is used on this system because its suitable for pose recognition. The application can recognize all the alphabets except ‘J’ and ‘Z because ‘J’ and ‘Z’ require gesture to be presented.
Evaluation with NIST Statistical Test on Pseudorandom Number Generators based on DMP-80 and DMP-128

The random numbers possess an essential role in cryptography as one can use them as keys, or to generate challenges, or even as IV values. One can generate random numbers from a pseudorandom number generator (PRNG). There are 2 categories of PRNG algorithms known to us, including the specially built algorithms and the algorithms based on existing cryptographic construction. One of the algorithms commonly used to generate output from a PRNG is a block cipher-based hash function. In this research, we evaluated a PRNG based on block cipher-based hash function by utilizing the NIST test. A certain data set was used to fit the required sample as a hash function produces outputs with fixed length. We used Davies-Meyer scheme based on PRESENT-80 and PRESENT-128 block cipher algorithms. From the 48 samples tested, there were 17 samples that failed the NIST test. Out of the 17 samples that failed, 12 samples failed in the non-overlapping template test.
Space and Applications of Artificial Intelligence

The probabilities to have a look at and have interaction with any given spacecraft are intrinsically restricted as compared to ground-based technology because of more than a few of factors. Crew availability, communication lag times, and power budgets are just a few of these. They also take into account the reachability and bandwidth of their ground connection. Every spacecraft must have some amount of autonomy, but research and previous missions have shown that by incorporating more sophisticated autonomous processes, many missions can be much more effective based on consistency, the production of knowledge, and the amount of work required to operate is a method that is becoming more and more popular for obtaining on-board autonomy. However, the variety of artificial intelligence methods and versions that are now written about in the literature is equally as wide-ranging as their prospective fields of application. This paper provides a thorough analysis of the state-of-the-art methods and algorithms for Fault Detection Isolation and Recovery (FDIR) and anomaly detection, and it provides examples of current ground- and space-based applications.
A Comparative Study of Supervised Machine Learning Algorithms for Fake Review Detection

The behavior of shopping has shifted into online shopping. Especially after Coronavirus Disease of 2019 (COVID-19), people choose online shopping rather than going to the market for economic and hygienic reasons. Reviews help the seller to make customers trust their products, but since some sellers are not honest, they use fake reviews to help boost their products. Fake reviews are commonly generated randomly by a computer bot or someone not using the product. Some researchers are already working on fake review detection to help this problem using many methods. In this paper, we compared three supervised machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). By preprocessing the data and using the Term Frequency-Inverse Document Frequency (TF-IDF) feature, we begin the experiment process without tuning. We apply the tuning parameters to each algorithm for the other experiments using 5-fold cross-validation. The result showed that SVM algorithms outperform the best algorithms of the three before and after tuning, with 88.89% and 89.77%, respectively.
Opinion-based sentiment analysis related to 2024 Indonesian Presidential Election on YouTube

Digital is always bringing a distinctive way to a communication and interaction between user through multiple platforms. The most popular social media platform for getting information and entertainment in Indonesia is YouTube. In Indonesia, news information topics related to government activity were also actively circulated on the YouTube, and it drew interest from each viewer. This research aims to analyze the dataset from YouTube and extract insightful information related to viewer perception of the topic and how the interaction between it. Data collected by crawling data from YouTube Application Interface and using the most upcoming topic keyword which is 2024 Indonesian presidential election candidate. Preprocessing data were handled by using multiple processes like tokenization, stop word and emoji removal, lemmatization, and pos tagging. Relevant data from the dataset like comments and replies were ingested by various machine learning algorithms to classify the user's perception and the interaction between users were visualized in the circular graph model. The findings revealed that there are more negative opinions related to the candidate and in the network also found that many users are connected when doing a counter-measure comments. Future research can adopt the present findings with another election topic on different social media especially that have more deep nested interactions to generate an even more comprehensive analysis.
Aspect-based Extraction of Implicit Opinions using Opinion Co-occurrence Algorithm

Facts are descriptive user reviews on a specific entity. A number of facts, which contain indirect opinions are called implicit opinions. This study aims to find the word opinion that is implied in the opinion sentence using the help of aspect extraction. Aspect extraction is connected to the opinion word list, thus reducing the search area to find the opinion word. This study had two stages, namely the learning stage and the validation stage. The learning stage focused on co-occurrence matrix opinion extraction using an opinion co-occurrence algorithm and extraction of category of aspects and opinions. The validation phase is to find opinion words that match the implicit opinion sentences based on aspect extraction. The data set is data from hotel user reviews in Yogyakarta from the TripAdvisor website from 2019. The validation results of implicit opinion extraction based on aspect extraction show an accuracy result in aspect extraction of 83.87%, while the accuracy result in opinion extraction is 56.13%.
Face Recognition and Face Spoofing Detector for Attendance System

Improving technology in the field of education will greatly help teachers and students in various ways, one of which is attendance. An attendance system generally leaks security and verification which may lead to fraud activity. In this paper, the authors design an attendance system that utilizes multiple verifications using card tapping and face recognition which is also accompanied by an anti-video spoofing system. The designed system is implemented as a web application with various algorithms such as Convolutional Neural Network (CNN), and Deep Metric Learning (DML) along with the application of the PN532 sensor and the use of ESP8266 for tapping the card. Our experiments show that the proposed system performs well, achieving up to 87.50% system accuracy.
Machine Learning Models for Classifying Imbalanced Class Datasets Using Ensemble Learning

In previous studies, many researchers ignored the imbalanced class in a dataset. Imbalance data that is processed by classification with one of the labels in the data has a value that is much different from the other labels. Imbalance data has a rare class (data with a few labels) and an abundant class (data with many labels). This condition can affect the results when predicting the model or the results of the predictions made by the classification model because the model cannot distinguish between rare and abundant classes, so it will assume that all data labels are abundant classes. Several previous studies have stated that the presence of Imbalanced Handling can handle unbalanced classes in a dataset. Imbalance Handling is a way to handle imbalanced data so that rare classes and abundant classes are balanced and that the Predict Model results can be maximized. This study aims to show the Imbalanced Data's effect when carrying out a classification so that the classification results are not optimal and to find the proper Resampling method for Imbalanced Handling. The resampling methods used in this study are Random Over-Sampling (ROS), Random Under-Sampling (RUS), and SMOTE. The datasets that were tested in this study were car_insurance, employees_attrition, and telco_customers and then classified using several classifications, namely, Artificial Neural Network (ANN), Decision Tree, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes and Stacking Ensemble Learning. In the car_insurance Dataset, the best Resampling results are Random Over-Sampling with KNN classification with an accuracy of 84%. For the employees_attrition Dataset, the best Resampling results are Random Over-Sampling with Decision Tree, KNN, and Stacking classifications, each of which has accurate results of 99%. For the telco_customers Dataset, the best resampling results are Random Over-Sampling with KNN classification, which has an accuracy of 84%. Of the three datasets tested, the best resampling method is Random Over-Sampling.
Abstractive Summarization of Indian Legal Judgments

This paper reports the effectiveness of a method using T5 to generate an abstractive summarisation of Indian legal judgments. When a legally qualified person manually performs summarisation, the created summary always depends on the person's expertise in performing the task. So an automatic legal summarization is used to perform this task more accurately. The system generates an abstractive summary of 350 judgments taken from the Honorable Supreme Court of India. The dataset has been created manually with a lawyer's help, and the result evaluation is performed using the ROUGE score, which gave a precision of 0.54955 for Rouge-L.
A Reinforced Active Learning Sampling for Cybersecurity NER Data Annotation

A vast majority of cybersecurity data comes in the form of unstructured textual data and needs to be annotated proficiently to train supervised machine learning models. The critical question is how much and which subset of data should be annotated for better model performance under budget constraints. Though most of the Machine Learning (ML) research focuses on learning better models using annotated datasets, this paper focuses on data annotation, specifically on suitable subset selection with an emphasis on Named Entity Recognition (NER) for cybersecurity. The proposed method provides an active learning based sampling strategy to select minimal yet most informative samples from a large set. Further, reinforcement learning is combined with the active learning approach to automate the process of sampling. The results on the auto-labelled cyber-NER dataset indicate that the cyber-NER model with Reinforced Active Learning (RAL) based sampling increases F1-Score by +2-7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and reduces compute time by 90% compared to random sampling based subset selection. Further, the proposed RAL approach achieved an 80% reduction in sample size and, consequently, annotation cost with comparable accuracy to that of complete selection.
A Comparative Study Of Abstractiveness In Summarization Evaluation

Text summarization is one of the challenging NLP tasks which involves both NLU and NLG capabilities to be available for a model to generate summary. Specifically, abstractive summarization involves generating summaries which are more concise and at the same time more complex than the extractive summarization. The evaluation methods for abstractive summarisation still involves human intervention. The major challenges posed in the evaluation is due to unpredictable hallucination in both reference and generated summaries. In this paper we introduce a novel measure of abstraction which could help to rate a given summarization data set, potentially benefit researchers to not only semi-automate the evaluation but also ideas to provide a scope for reward-penalize methods to detect and improve positive hallucination
Fast Image Convolution and Pattern Recognition using Vedic Mathematics on Field Programmable Gate Arrays (FPGAs)

A major part of image processing involves convolution process. The pattern recognition techniques which are implemented through Convolutional Neural Networks (CNN) also involves two-dimensional (2D) convolution. The 2D convolution process consists of enormous multiplication operation, which need to be implemented in real time. There is a requirement of fast multiplier for the same operation. Vedic multiplier proved to be faster compared to the conventional multiplication operation. A 2D convolution-based pattern recognition system, which makes use of Vedic Multipliers is proposed in this paper. The proposed system is implemented on Field Programmable Gate Arrays (FPGA) with Verilog programming. The results of Vedic multiplier based convolution and pattern recognition are compared with the conventional multiplier such as Booths algorithm multiplier. The parametric comparison is done in terms of Number of Slice LUT's, Number of Slice Registers, speed and frequency of operation. Results show that there is significant improvement in above said parameters for Vedic multiplier-based convolution in pattern recognition system.
Power Cognizant Optimization Techniques for Green Cloud Systems

The study focuses on the techniques and applications of green computing in various fields of human life such as biomedical systems and networking, is explored in detail. The influence of green computing, and the possible developments and improvements in its' mode of operation has also been gone over through in this paper. The considerable developments in green computing and the impact it has on the long-term trajectory that various disciplines, such as networking, computing, and artificial intelligence, are going through right now, make it an important area of research and study.