Reviewerly/exHarmony huggingface dataset:
A Comparative Performance Evaluation of Bitcoin Price Prediction Using Machine Learning Techniques

The value of bitcoin as a financial asset is rising and due to its extreme volatility, accurate forecasts are necessary to guide investment choices. Adoption, regulatory developments, geopolitical events, and macroeconomic factors all have an impact on its price. Accordingly, a lot of researchers have looked into a variety of factors that influence the value of bitcoin including the trends that underlie its fluctuations, but limited studies have focused on applying various machine learning techniques in this domain. Therefore, the objective of this study is to analyze multiple algorithms used for machine learning regression model in order to identify the system which can estimate bitcoin values most effectively and accurately based on multiple attributes. To forecast the bitcoin price, the dataset has been analyzed and preprocessed meticulously and then multiple machine learning regression models such as XGBoosting, Gradient Boosting Regressor, Hist Gradient Boosting Regressor, Random Forest, Linear Regression, Support Vector Regressor, Neural Network Regressor, Decision Tree, Gaussian Process Regressor, and K-Nearest Neighbors Regressor. The top findings were 99.497 percent (almost 99.5 percent) R-squared (R2), 0.01281 (RMSE), and 0.005755 (MAE) scores employing gradient boosting regressor model.
BIDInet: Advancing Dark Image Enhancement with Burst-Feeding and Invertible Blocks

This paper presents a novel approach of enhancing dark images using burst photography in developing a burst-fed invertible dark image enhancement network (BIDInet). BIDInet combines multiple invertible blocks with the pyramid structure of VGG-19. With burst-fed image-frames, BIDInet produces a single enhanced image with reduced artefacts. Trained on the See-in-Dark (SID) dataset, experimental results indicate that BIDInet achieves a PSNR value of 33.39 dB and an SSIM value of 0.66 for image enhancement, as well as a PSNR value of 23.43 dB and an SSIM value of 0.76 for image denoising, with 97.86% denoising accuracy. Comparisons with existing models demonstrate the effectiveness of using multiple invertible modules based on the VGG-19 network's pyramid block for enhancing and denoising extremely dark images in various applications.
An Innovative Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

Stroke requires immediate medical care. The WHO states stroke is the leading cause of death and disability world-wide. Thus, early stroke detection is essential. Few studies projected with CNNs or other stacking approaches. CNN-ensemble classifier. This study used unequal Kaggle data. Random over-sampling balances data. After identifying the most important features, several machine learning algorithms are applied, including Logistic Regression, Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbor's Classifier, Gaussian Naive Bayes, Bernoulli Naive Bayes, and Stacking for Six Algorithms (Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, KNN, BernoulliNB, GaussianNB). Training and testing evaluate each algorithm's stroke prediction accuracy. Stacking six algorithms gives the maximum accuracy, testing is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{99.9}\pm\%$</tex> and training is 100%. Machine learning improves this classifier's stroke prediction.
Training StyleGAN2 on BWW-Texture Dataset: Generating Textures Through Transfer Learning

Texture synthesis is a challenging problem in computer vision that aims to generate realistic and diverse images of surface patterns for applications like game development and 3D rendering. Existing methods often suffer from limitations such as low resolution, lack of details, unnatural artifacts, and inability to capture complex and periodic patterns. In this paper, we investigate each of these obstacles in a novel application of StyleGAN2-ADA, a state-of-the-art generative adversarial network (GAN), to a proprietary custom dataset (BWW-Texture) for synthesizing high-quality textures. We create a large and comprehensive dataset of texture images from three categories: wood, brick wall, and plastered wall. We train StyleGAN2-ADA on our dataset using transfer learning techniques from a pre-trained model on human faces (FFHQ). We evaluate the quality of the generated textures using a user study. Our model outperformed two popular text-based image generators: Dall. E 2 and Stable Diffusion 2.1. We also demonstrate the practicality of our model by using the generated textures in 3D graphics software.
Traffic Sign Recognition and Classification Using Machine Learning and Deep Learning

The recognition and classification of traffic signs hold significant significance within intelligent transportation systems and autonomous vehicles. This task entails the precise and real-time identification and categorization of diverse traffic signs through the There are many different types of neural networks that can be used for traffic sign recognition and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). The choice of which network to use depends on the specific requirements and constraints of the task. The field continues to evolve and advance, with new techniques and approaches being developed to address the challenges and limitations of this task [1].
A Data Hiding Technique Combining Steganography and Cryptography for Secured Communication

Data security plays a significant role in the present era. A vast amount of data is transmitted over the internet, including susceptible information such as financial transactions, passwords, and other confidential data. Traditional data security systems are becoming vulnerable due to advancements in technology. The combination of steganography and cryptography is an effective solution to overcome this. This merged approach safeguards data from interception and conceals hidden information, thwarting potential attackers. This research proposes a secured method for data concealment using cryptography and steganography. The private information is first encrypted into ciphertext using the Advanced Encryption Standard (AES) encryption algorithm and then hidden into a text. The suggested approach performs better in embedding capability, invisibility, resilience, and security, establishing it as a standard data-hiding method. By adding an extra layer of security, it becomes much harder for cyber attackers to invade privacy and exploit vulnerabilities. This integrated approach marks a promising advancement in modern data security.
Prediction of Faults in Embedded Software Using Machine Learning Approaches

Software industry owns the most modern technological developments in this era. A software should be working perfectly without any error before handed over to the customer. The process of this quality assurance should be cost effective, less time-consuming, and reliable. But in practical scenario, it takes time to predict, detect and rectify the faults in a software. There are many models to predict the faults of software but no single model can claim to be perfect. This paper studied seven Machine Learning (ML) algorithms: Ada-Boost, CatBoost, LightGBM, Random Forest, XGBoost, Ensemble using Stacking and Voting to predict faults for an embedded software. Experiments were carried out with six datasets and eight performance metrics: accuracy, sensitivity, specificity, F-measure, balance, AUC, MAE, and precision to observe the performance of the algorithms. Random Forest showed the best results for accuracy, precision and specificity for 35%, 25% and 20% test data ratio with prediction of 0.995, 1 and 0.996 respectively. On the other hand, Ensemble (Stacking) showed the best result for specificity for 35% test ratio with prediction of 0.996. These results proved the suggested model better than the previous ones with acceptable outcome.
Impact of Centrality on Automated Vulnerability Detection Using Convolutional Neural Network

Software vulnerabilities have now become a major security threat because of the increasing size and complexity of software programs. These security flaws make vulnerability detection a prior concern. Despite the efficiency of Deep Learning (DL) based approaches, prior studies have faced challenges in maintaining detection accuracy and scalability simultaneously. In order to overcome this challenge, we propose an automated and scalable vulnerability detection technique to acquire both accuracy and scalability while searching for vulnerabilities in source code. Specifically, this method uses complex network analysis theory to effectively convert a function's source code to an image which considers program's syntactic and semantic information and then applies text convolutional neural network to detect vulnerabilities. Experimental results compared against the conventional methods report that our method achieved 5% improvement in accuracy and can detect vulnerabilities in large scale source code.
Olympic Weightlifters' Performance Assessment Module Using Computer Vision

Olympic weightlifting is a highly technical and physically demanding sport that requires athletes to lift heavy weights with precise technique. Accurately assessing an athlete's performance is crucial for identifying areas of improvement in terms of strength, power output, and movement efficiency. Traditional methods ofperformance assessment, such as manual observation and analysis, are time-consuming and subject to human error. Advances in wearable sensor technology, motion capture systems and computer vision algorithms have the potential to revolutionize sports analytics. We report a computer vision pipeline for automated analysis of weightlifting videos and demonstrate the markerless estimation of major joints, joint angles and accurate tracking of barbell trajectory allowing us to superimpose the estimated CoG trajectory, the base of support, and the line of gravity information into the video. The augmented videos can provide instant, objective and detailed feedback and allow longitudinal insights for comprehensive weightlifting performance analysis.
Application of Supervised Learning Techniques for Sports and Daily Activities Identification Using Accelerometer Data

Human Activity Recognition (HAR) is a research area that is receiving increasing attention in recent years. In this paper we propose the application of different supervised learning algorithms to recognize distinct human activities. In particular, we use a dataset that includes inertial measurements recorded from sensors placed in various positions on the subjects' body, while performing sports and daily activities. Considering possible real-life applications of the system, we analyze only the acceleration signal coming from a single and low-complexity sensor placed on the torso of the subjects. We derive different statistical features from the three axial accelerations. These features are the input of Machine Learning algorithms with the purpose of recognizing the particular activity carried out by the subjects. The unprocessed acceleration signals are instead sent to Deep Learning algorithms, giving us the opportunity to compare the performance of the classifiers. In the end, we achieve accuracy values of 73.3% and 86.6% in classifying 19 types of different human activities, using a Random Forest (RF) classifier and a 1D Convolutional Neural Network (CNN) network, respectively.
Time Synchronization in Wireless IMU Sensors for Accurate Gait Analysis During Running

With the advancements in wearable technology, continuous monitoring of physiological and biomechanical parameters has become possible, providing valuable insights into health and fitness. This technology allows for remote patient monitoring, medical support, and even virtual coaching. One area of focus is gait analysis, which can help reduce the risk of running-related injuries. This study aims to develop a small, lightweight, and affordable wireless sensor device for collecting gait data. The device utilizes IMU sensors positioned on the pelvis and foot to measure stability and detect changes due to fatigue during running. Precise time synchronization between the sensors and an Android phone is crucial for accurate data analysis. The study employs Bluetooth LE technology for wireless transmission and proposes a synchronization method to align the data from the asynchronous sensors. The hardware and software components of the sensor device are described, along with the synchronization process. Additionally, a pilot study involving the use of the sensors during treadmill running was performed.
Advancing AI-based Assistive Systems for Visually Impaired People: Multi-Class Object Detection and Currency Classification

Blind and Visually Impaired (BVI) persons depend on the assistive systems. An intelligent assistive system may provide situational awareness and support in the age of machine learning and computer vision with the help of deep learning and computer vision. Among many, multi-classed object detection and currency classification are the two key means for providing situational awareness and environmental information to the BVI people. Thus, the objective of this research is to propose a modified YOLOv7 and a YOLOv8 algorithm for multi-classed object detection and currency classification, respectively. To attain this objective, a modified YOLOv7 and a YOLOv8 was proposed and evaluated considering the datasets for object identification and currency classification, respectively. The dataset for object detection was created from scratch that includes 3046 photos and spans 20 distinct classes, while the dataset used for currency classification used 295 photos divided into eight distinct groups. The evaluation results showed that in comparison to the basic YOLOv7 (89.3% mAP) the modified YOLOv7 has obtained 91.4% mAP for multi-classed object detection, while the YOLOv8 has reached 94.6% mAP for currency classification comparing to the Radial Basis Function Network (91.51% mAP).
Enhancing Communication Among Remote Cybersecurity Analysts With Visual Traces

This article addresses the communication challenges in collaborative cybersecurity analysis amid the rise of remote work during the postpandemic era. We introduce a novel approach that leverages visual traces of analysts’ analytical processes to enhance information transmission and processing in collaborative cybersecurity analysis. Furthermore, we demonstrate the advantages of visual traces in supporting communication in a case study. Our approach offers valuable insights for organizations struggling to attain effective communication in remote teams during collaborative problem-solving tasks in the postpandemic era.
User Experience Towards Chatbots in Myanmar

In recent years, chatbots have become increasingly popular, and many businesses started integrating chatbots into social media and service platforms to provide better services and customer support. However, users still have concerns regarding emerging technology and prefer to communicate with human agents rather than chatbots despite the potential for improved customer service. The study aims to understand the user experience with chatbots among Myanmar people, explicitly focusing on users’ awareness of the chatbot, its usability, and usefulness, how satisfied they are after interacting with it, and their intention to continue using it. A research model comprised of seven factors and eleven hypotheses is developed. Ninety-two chatbot users from Myanmar were collected and analyzed using the statistical analysis tool SmartPLS3 to test the hypothesis, and SmartPLS3 Bootstrapping was used to mitigate the small sample limitation to ensure reliability and validity. The results indicate that user awareness and usefulness of the chatbots positively affect satisfaction, and ease of use positively affects the expectation towards chatbots. Additionally, satisfaction and expectation are also found to affect to continue using chatbots in the future positively. However, the interpretability of the chatbots has no impact on satisfaction or expectation. Similarly, user awareness does not affect expectation, and ease of use also does not affect satisfaction. The findings highlight that the primary obstacle faced by chatbots is their limited social capabilities to interact with users.
Extending the Egocentric Viewpoint in Situated Visualization Using Augmented Reality

Augmented Reality (AR) has the ability to display and interact with additional information aligned with the real world and to support context-driven visualization techniques. Situated Visualization (SV) is a method to represent data in a context that encompasses all the visualizations that change their appearance based on context. Therefore, since AR is typically used so that the user can visualize extra information regarding the surrounding real world environment, this technology is well suited for SV and presents high potential in many situations, as are the cases of collaborative/assistive tasks in the maintenance area and supporting decision-making. AR-based visualizations are commonly constrained to the users' unique egocentric view-point, which reduces their ability to explore all the richness of available information. This limitation is particularly evident in situations that require overviews of all information inside and out of the user's field of view (FOV). This article proposes multi-perspective renderings, transitional interfaces, and the use of multimodal information as methods to extend the user's egocentric viewpoint. The main idea is to coherently present and augment, in the same user's display, dynamic and static feeds of several real and virtual egocentric viewpoints, related to and relevant for the user's task.
Understanding the Forest: A Visualization Tool to Support Decision Tree Analysis

Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different rules. Hence, DTs can be used as a learning process of finding the optimal rules to separate and classify all items of a dataset. Since the algorithm relies on a decision process similar to rule-based decisions, they are easily interpretable. However, DTs can be difficult to analyse when dealing with large datasets and/or with multiple trees, i.e. ensembles. To ease the analysis and validation of these models, we developed a visual tool which includes a set of visualizations that overview and give details of a set of trees. Our tool aims to provide different perspectives over the same data and provide further insights on how decisions are being made. In this article, we overview our design process, present the different visualization models and their iterative validation. We present a use case in the telecommunications domain. In concrete, we use the visual tool to help understand how a model based on DTs decides which is the best channel (i.e., phonecall, e-mail, SMS) to contact a client.
Visualization of Swiping Motion of Competitive Karuta Using 3D Bone Display

Each player of competitive Karuta has a unique posture and a style of swiping the cards, which are important factors in winning a game that requires the swift acquisition of all 50 cards. It is essential to analyze the characteristics of a player's posture in order to achieve the swift acquisition. While previous studies on the analysis of posture of competitive Karuta required special devices to be attached to the body for measuring brain activity or analyzing wrist acceleration to determine the speed of a player. Such approaches were problematic since they may interfere with the natural movement of the players. To address this issue, we are developing a visualization system that displays the three-dimensional skeletal movements of multiple players in response to a read the poetry of Karuta. We utilize skeletal information extracted from videos using Mediapipe, developed by Google, to compare and analyze the movements of different players. This paper presents the results of our analysis, which compared the swiping movements of multiple players using our proposed system.
Using Visualization Methods for Improving Web Navigation

Using either a search engine or website navigation can pose difficulties for web users in certain situations. Therefore, creating a useful visualization of the website structure can aid web users in navigating through a website. By employing visualization techniques, the website structure can be presented as regions that provide clarity on all pages, links, and labels. To address these challenges, our goal is to identify the top three existing visualization layouts that improve website navigation. To achieve this, we aim to evaluate different types of existing visualization layouts by applying the region mechanism to visualize website structures. Initially, we designed a region mechanism that ensures scalability and ease of navigation and understanding for website structures. Subsequently, we conducted a technical evaluation to assess the performance of 12 existing visualization layouts on a website structure. During the evaluation, we generated 12 layouts for each webpage in the University of London (UoL) website. We then calculated three metrics for each layout: average aspect ratio, visualization area, and label overlapping. Finally, based on the results obtained, we identified the three most suitable visualization layouts for visualizing a website structure. These selections were made based on low aspect ratio and stability, effective control of the visualization area, and support for readability. Overall, this research focuses on improving website navigation by evaluating existing visualization layouts and selecting the most suitable ones. The chosen layouts are determined based on their ability to maintain low aspect ratio, ensure stability, control visualization area, and support readability.
Artificial Intelligence in Visual Analytics

Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.