Reviewerly/exHarmony huggingface dataset:
Penguatan dan Bimbingan Trend Fashion Islami Bagi Mahasiswi di Universitas Islam Negeri (UIN) Raden Mas Said Surakarta

Capitalism doesn't just impact women but also men. It has even conquered religion and commodified religious symbols. It can infiltrate religious symbols and transform them from within while offering the concept of "pious yet trendy" or 'worship yes, trendy yes.' Lifestyle and fashion have become inseparable parts of modern society. It's not just about the cover but the essence. This can be seen through the numerous trending models at the moment, leading many young people or teenagers to emulate their clothing styles. The purpose of this writing is to help us understand the difference between lifestyle and fashion in the past and today, as lifestyle and fashion have become integral parts of modern society. The research method used is library research. The research results show the existence of silent social preferences adopted by society, replacing old values.
Improving Aspect-Based Sentiment Analysis for Hotel Reviews with Latent Dirichlet Allocation and Machine Learning Algorithms

The rapid expansion of online platforms has resulted in a deluge of user-generated content, emphasizing the need for sentiment analysis to gauge public opinion. Aspect-based sentiment analysis is now essential for uncovering intricate opinions within product reviews, social media posts, and online texts. Despite their potential, the complexity of human emotions and diverse language nuances pose significant challenges. Our study focuses on the importance and trends of sentiment and aspect-based sentiment analysis in automated review analysis, with a primary focus on Indonesian-language hotel reviews. Our research underscores the need for nuanced tools to unravel multifaceted sentiments. We propose an automation framework that utilizes Latent Dirichlet Allocation (LDA) for feature extraction. We evaluate LDA's performance, enhance it through filtration, and enrich it by integrating it with Word2Vec and Doc2Vec. Our methodology encompasses various machine learning algorithms, including Logistic Regression (LR), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). Empirical results reveal that the optimal combination involves LDA bigram and Word2Vec, alongside the LGBM classifier, yielding an average F1 score of 86.6 across ten aspects. This contribution advances automated aspect-based sentiment analysis, offering concrete implications for e-commerce, marketing, and customer service. Our insights inform precise marketing strategies and enhance customer experiences, underscoring the research's relevance in the digital landscape.
Visualization Application for the Discovery of Products with Characteristic Sales Trends

In this study, we propose an application for detecting anomalies in sales trends by visualizing increases or decreases in the number of products sold (hereafter referred to as “sales”) using a control chart model of the purchasing data. We believe that it is important for companies and stores to understand the current status of sales increases and decreases. If they knew which products were showing a decline in sales and which products were showing an increase in sales, they could implement different sales strategies for each of them, which will lead to an increase in overall profits. While there exist various criteria for judging whether sales are increasing or decreasing, we suggest that one method is to find an estimated range of sales based on past purchase data, and we suggest considering sales to be “increasing” when they exceed that range and “decreasing” when they fall below that range. Based on these criteria, we created an application that detects, in real time, which product genres have increased or decreased in sales. Our application displays a list of possible product genres. Is it is verify by using this application to check which productshow an increase or a decrease in sales, and the sales trend can be presented by visualizing the sales transition. In this way, it is we expect our application to improve the profits of companies and stores.
Analyzing Winning-Related Features to Understand Team Characteristics

In this study, the data published by Fédération Internationale de Football Association officials have been classified according to the difficulty of obtaining the variables. Classification was based on the availability of video, the Global positioning system and experts. Herein the variable importance of random forests, an ensemble learning process, was used. Visualizations were created to analyze the features. This study examines the features of winning and losing in soccer competitions through visualization utilizing a web application with R-Shiny. The visualized information allowed discussion of the matches.
Remarks on an Echo State Network–Based Optimal Predictive Control Using a Metaheuristics Optimisation Approach

In this study, we investigated the capability of applying reservoir computing, especially echo state networks (ESNs), to control dynamic systems. To achieve optimal predictive control (OPC) of nonlinear systems, an ESN was used as the prediction model of the objective plant in a model predictive control approach. We performed computational experiments of the ESN–based OPC to test the tracking tasks of the discrete–time nonlinear plant. The simulation results showed that the ESN–based OPC could perform the control task with applicable accuracy, demonstrating the potential application of ESNs for controlling nonlinear systems.
AI-Assisted Digital Pathology for Computational distinction of adenocarcinoma vs squamous cell carcinoma

Lung diseases owing to their high morbidity and mortality pose a significant burden to the healthcare systems. The evolution in integrated circuit technology and the consequent rise in computational power has paved the way for the realization of digital pathology (DP) tools. These DP tools equipped with recent developments in artificial intelligence (AI) can assist pathologists in improving clinical evaluations and thereby prescribing betterinformed treatments. Previously researchers have proposed such DP tools for the treatments of breast, prostate, and head and neck cancers, however, developing such tools and applying them for the treatment of lung diseases remains a challenge. The applications of AI-based DP tools on radiology images for improved characterization of lung nodules, lung cancer risk stratification, and fibrotic lung disease are well established. However, the subtypes of non-small cell lung cancer (NSCLC), namely, adenocarcinomas (AC) and squamous cell carcinoma (SCC) are still difficult to distinguish, especially in small biopsies. In this article, we discuss potential architectures of convolutional neural networks (CNN) for the construction of an appropriate AI-based DP classification tool in establishing a crisp distinction between adenocarcinoma and squamous cell carcinoma. It is further emphasized that the construction of such a DP tool will help in mitigating the current issues that the pathologists are facing while working with the conventional molecular biomarkers profiling, a process which is both, expensive and requires high technical and interpretative skills.
A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography

Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance gesture recognition capabilities. However, many existing methods fall short in fully exploit the specific spatial topology and temporal dependencies present in HD-sEMG data. Additionally, these studies are often limited number of gestures and lack generality. Hence, this study introduces a novel gesture recognition method, named STGCN-GR, which leverages spatio-temporal graph convolution networks for HD-sEMG-based human-machine interfaces. Firstly, we construct muscle networks based on functional connectivity between channels, creating a graph representation of HD-sEMG recordings. Subsequently, a temporal convolution module is applied to capture the temporal dependences in the HD-sEMG series and a spatial graph convolution module is employed to effectively learn the intrinsic spatial topology information among distinct HD-sEMG channels. We evaluate our proposed model on a public HD-sEMG dataset comprising a substantial number of gestures (i.e., 65). Our results demonstrate the remarkable capability of the STGCN-GR method, achieving an impressive accuracy of 91.07% in predicting gestures, which surpasses state-of-the-art deep learning methods applied to the same dataset.
Collection and Analysis of Datasets for Low-Light Vehicle Detection

Many vision-based traffic monitoring systems are limited to daytime detection and have difficulties at later times of the day where there is low-illumination. Several studies have explored the issue of low-light object detection in different domains, but there is still a lack of understanding on the use of low-light data within the domain of vehicle detection. Thus, this research explored the use of vehicle data covering different lighting conditions and camera angles with regards to improving the performance of vehicle detectors during these aforementioned difficult conditions. A low-light dataset was produced consisting of common vehicle types found in the Philippines containing six different vehicle classes with a total vehicle instance count of 46,669. The effects of using different data variations for model training were explored in terms of their contribution to low-light vehicle detection performance. This research provides insight on how dataset composition affects model performance to produce a high-quality dataset for vehicle detection during low-light conditions. The study was conclusive that in order to produce an accurate dataset for low-light vehicle detection, the dataset must contain training data similar to the environments and features unique to low-light scenes.
Research on Bare-Hand Ray Interaction Based on Pointing Intentions in Virtual Reality

Ray interaction is the most commonly used interaction method for long-distance interactions beyond the reachable domain of virtual space <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . However, for a long time, the jitter and sense of alienation in ray interaction have been relatively strong, and people are seeking good ray interaction to optimize it. This paper proposes three methods for ray interaction in virtual space through daily practice, compares the ergonomic differences between expected (continuous clicks) and unexpected (search clicks) scenarios, and proposes a preferred ray interaction method.
3D Gait Analysis for the Elderly Mobility Based on Multiple RGB Cameras

Walking is a fundamental movement that undergoes performance decline in the elderly. Traditional gait analysis relies on professional motion capture cameras and reflective markers, which are very expensive and cumbersome to use. Recent advancements in human pose estimation and deep learning have shown the feasibility of markerless motion analysis using regular RGB cameras. However, these methods may have deviations due to factors like light conditions, camera views, subject stature, and lack of biomechanical constraints. While some studies have compared markerless systems to professional motion capture systems, few have examined if these systems can still distinguish different motion patterns in real-world applications despite detection errors. This study presents a gait analysis system utilizing four RGB cameras and prior biomechanical knowledge to measure gait patterns in both elderly and young subjects. Personalized skeletal models were created for each subject on OpenSim, driven by 3D human keypoints obtained through human tracking, pose estimation and triangulation algorithms. Gait spatial temporal parameters and kinematics were then calculated for validation. When compared to the Vicon motion capture system, the Pearson correlation coefficients for the elderly gait were found to be 0.97 for hip flexion/extension, 0.92 for hip adduction/extension, 0.98 for knee flexion/extension and 0.85 for ankle dorsi/plantar flexion. Comparisons between the elderly and young individuals revealed shorter step and stride lengths and reduced range of motion in the elderly, which were consistent with existing literature, indicating the weaker gait performance in this population. These findings demonstrate that our RGB camera-based gait analysis system can effectively differentiate between different gait patterns. Therefore, in certain scenarios, motion analysis can be performed simply and affordable with our system.
Low Complexity Fall Detection Using 2D Skeletal Data

Falling remains one of the events that can potentially cause irreversible damage, especially among elderly people if not treated early. Fall detection systems help to identify if a person has fallen and promptly alert the people involved to ensure timely medical care. Existing fall detection systems are found to utilize specialized sensors or require heavy computation requirements to identify the occurrence of falls. An easy-to-adopt, fall detection system is presented which can identify falls from a commodity-grade 2D CCTV camera. 2D skeletal data are extracted from the video frames and representative features are computed. The features specific to fall detection are grouped and analyzed in terms of their discriminative ability to distinguish falling from day-to-day actions. LSTM-based classifiers are trained on the filtered fall features to identify a person falling. The results are validated on the NTU (RGB) skeletal dataset. The precision and recall of the Fall detection classifier are found to be 98% and 100% respectively. The number of parameters in the developed system is found to be several magnitudes lesser than in existing systems, thereby validating low complexity with high accuracy.
Serial-Parallel Dynamic Hand Gesture Recognition Network for Human-Robot Interaction

In recent years, hand gesture recognition has played a crucial role in human-robot interaction (HRI). This paper proposes a skeleton-based serial-parallel dynamic hand gesture recognition network. A set of skeleton-based physical features is designed to model the spatial relationship of joints to construct skeletal space configurations. A slow-fast double-scale parallel network is proposed to extract the temporal dynamics of gestures. The attention mechanism is used to fuse the spatiotemporal information of the gestures, and the recognition result is obtained through the serial 1DCNN structure. In addition, the data enhancement technology based on transformation is used to improve the generalization of the network. The proposed methods are evaluated on the SHREC14 and SHREC28 datasets, which show superior performance, with an accuracy of 95.11% and 92.98%, respectively. The network is fine-tuned on the customized dataset HRIGes, and the recognition results are mapped to a five-fingered dexterous manipulator to realize real-time human-robot interaction.
Camera Distortion Modeling and Digital Twin of Imaging

Camera distortion is an essential component in camera models, needing consideration in both optical design and camera calibration. Numerous distortion factors, including the physical characteristics of lenses and the imperfection of assembly, may affect the overall distortion. The amount of distortion in the final image is also correlated with the parameters of the camera. This research presents the decomposed modeling of major distortion sources and combines the factors into a digital twin of camera distortion (DTCD) according to the realistic imaging process. The DTCD can quantitively simulate the influence of all distortion sources on the final image by analyzing the amount and direction of pixel shift from the ideal distortion-free image. With the simulations, the assumptions in the conventional radial tangential distortion model are proved to be conditionally valid depending on the camera parameters. The existence of COD is discussed with synthetic experiments.
A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning

Deep learning (DL) based fault diagnosis methods have gained considerable attention in the field of machine health monitoring due to their powerful feature learning capabilities. However, embedding domain diagnosis knowledge into the DL framework to obtain enhanced features having better correlation with the exact health conditions of machine elements for improved fault predictions is still an open challenge. In this paper, a fault diagnosis method combining two-dimensional (2D) image representations of squared envelop spectrum (SES) of vibration signals of bearings, a critical machine element, and a pretrained convolutional neural network (CNN) is proposed. SES is one of the most efficient indicator for the assessment of second order cyclostationary symptoms of damages, which are typically observed in bearings. In this paper, we integrate this knowledge in designing a DL framework for efficient fault diagnosis in bearings. The proposed method is tested and evaluated on an experimental bearing vibration dataset collected under different operating and fault conditions. Experimental results demonstrate that the proposed method can achieve a high diagnosis accuracy and present a better generalization ability both in balanced and imbalanced data scenarios.
ConTrans-Detect: A Multi-Scale Convolution-Transformer Network for DeepFake Video Detection

With the recent advancement of generative deep learning technologies, DeepFakes are the outcome of the manipulation to generate synthetic images, such as swapping a person's face in a video with another face in another video. Nowadays, deep generative models make it easy to generate fake videos, which is hard to detect. Existing methods have utilized Convolutional Neural Networks (CNNs) to identify manipulated regions for DeepFake video detection. However, these methods might not entirely tackle the difficulties of learning low-level spatial features and capturing temporal variations in temporal information, which are crucial for face forgery detection. Therefore, we propose a Convolution-Transformer Deepfake Detection (ConTrans-Detect) model, comprising a multi-scale CNN module for spatial feature representation and a multi-branch Transformer for temporal feature modeling. The multi-scale CNN module uses 3D Inception block to extract multi-scale low-level features (e.g., edges, corners, and angles) from videos. The multi-branch Transformer module consists of multi-stream Transformer layers, each taking different temporal resolutions and spatial feature dimensions as input to perceive various motion variations. Our model achieves an AUC of 0.929 and 0.920 f1 score, surpassing several state-of-the-art performances on the DeepFake Detection Challenge Datasets (DFDC).
Creating HDR Image Using Linear Step-by-Step Contrast Changes

The paper presents results on simulating the creation of an image with an extended dynamic range with a twelve-bit representation of each of the primary colors from a high-definition image, originally represented in the red, green and blue color space by an eight-bit code. The color space of the original image is converted into the space of luminance and color-difference signals, in which a step-by-step uniform increase and decrease in the range of luminance and color-difference signals is performed. The resulting intermediate images are added together to form an extended range. The results showed a subjective increase in image contrast and the emergence of fine details in both light and dark shades. The increase in image detail is objectively shown.
Trajectory Design for Multi-UAV-Assisted Data Collection: A Multi-agent Deep Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) are widely employed in wireless communication networks due to their remarkable flexibility and mobility. In this work, we study a multi-UAV-assisted data collection system, where multiple UAVs cooperatively harvest data from ground users (GUs). Aiming at minimizing the mission completion time, we first formulate the trajectory design problem as a min-max optimization problem, subject to the constraints of energy consumption and collision avoidance. Then, by applying the popular QMIX framework, we propose a multi-agent deep reinforcement learning (DRL) algorithm to optimize the trajectory of all UAVs. With the well-designed reward function, the proposed multi-agent DRL approach can coordinate all agents to achieve the final objective based on their local observations. Simulation results verify the superiority of the proposed multi-agent DRL approach over the benchmark algorithms in terms of the mission completion time.
A Class of Extended Generalized RDP Codes

In 2022, [1] proposed a new construction for binary array codes with three parity columns, which can deduce some types of maximum distance separable (MDS) array codes. It was left as an open problem generalizing this construction to support more parity columns. This paper considers such a generalization and reveals it as a class of extended generalized RDP codes. We present the relationship between new codes and codes over all-one polynomial rings. Furthermore, we explicitly provide two new MDS array codes, one designed for four parity columns. We also propose two corresponding approaches for quickly computing syndromes of the new codes, both of which reach the lowest known asymptotic complexities.
Adaptive Waiting Time Asynchronous Federated Learning in Edge Computing

This paper introduces an innovative solution to enhance transportation in smart traffic systems by combining federated learning with edge computing. However, traditional synchronous federated learning lacks efficiency for real-time requirements, while asynchronous federated learning consumes more energy and exhibits unstable training. To address these challenges, the paper proposes an Adaptive Waiting Time Asynchronous Federated Learning (AWTAFL) algorithm based on the Dueling Double Deep Q-Network (D3QN). This algorithm dynamically adjusts the waiting time using D3QN, considering task progress and energy consumption, to accelerate convergence and save energy. Furthermore, the paper improves global model aggregation in federated learning by incorporating data volume weights, freshness level of client parameters, and client contribution level. This comprehensive parameter weighing ensures stability during asynchronous federated learning and aids in achieving convergence. Experimental simulations confirm that the proposed algorithm significantly reduces convergence time, maintains model quality, and effectively reduces energy consumption during asynchronous federated learning. Moreover, the improved global aggregation update method enhances training stability and reduces oscillations in the global model convergence.