Reviewerly/exHarmony huggingface dataset:
Monitoring Asbestos Mine Remediation Using Airborne Hyperspectral Imaging System: A Case Study of Jefferson Lake Mine, US

This study investigated an asbestos mine restoration project using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. The distribution of an abandoned asbestos mine (AAM) and treatment area were analyzed before and after the remediation based on the spectral indices for detecting naturally occurring asbestos (NOA) indicators and encapsulation. The spectral indices were developed for NOA, host rock, and encapsulation by logistic regression models using spectral bands extracted from the random forest algorithm. The detection models mostly used VNIR spectra rather than SWIR and were statistically significant. The overall accuracy of the detection models was approximately 84%. Notably, the detection accuracy of non-treated and treated areas was increased to about 96%, excluding the host rock index. The NOA index detected asbestos in the mine area as well as those in outcrops outside of the mine. It has been confirmed that the NOA index can be efficiently applied to all cases of asbestos occurrence. The remote sensing data revealed that the mine area was increased by ~5% by the remediation, and the treatment activity reduced asbestos exposure by ~32%. Moreover, the integrative visualization between the detection results and 3D high-resolution images provided an intuitive and realistic understanding of the reclamation project.
Offline signature verification using long short-term memory and histogram orientation gradient

The signing process is a critical step that organizations take to ensure the confidentiality of their data and to safeguard it against unauthorized penetration or access. Within the last decade, offline handwritten signature research has grown in popularity as a common method for human authentication via biometric features. It is not an easy task, despite the importance of this method; the struggle in such a system stem from the inability of any individual to sign the same signature each and every time. Additionally, we are indeed interested in the dataset’s features that could affect the model's performance; thus, from extracted features from the signature images using the histogram orientation gradient (HOG) technique. In this paper, we suggested a long short-term memory (LSTM) neural network model for signature verification, with input data from the USTig and CEDAR datasets. Our model’s predictive ability is quite outstanding: The classification accuracy efficiency LSTM for USTig was 92.4% with a run-time of 1.67 seconds and 87.7% for CEDAR with a run-time of 2.98 seconds. Our proposed method outperforms other offline signature verification approaches such as K-nearest neighbour (KNN), support vector machine (SVM), convolution neural network (CNN), speeded-up robust features (SURF), and Harris in terms of accuracy.
Automatic liver segmentation in computed tomography scans using deep semantic segmentation

Division of the liver from figured computed tomography (CT) images is fundamental for the greater part of the PC supported clinical applications, for instance, the arranging period of a liver transfer, liver volume assessment, and radiotherapy. In this paper, a programmed liver location model from clinical CT filters utilizing profound semantic division convolutional neural organization will be introduced, this model will actually want to subsequently isolate the liver utilizing CT images. The proposed model presents simultaneously the liver ID and the probabilistic division utilizing a profound convolutional neural organization. The proposed approach was endorsed on 10 CT volumes taken from open data sets 3Dircadb1. The proposed model is totally programmed with no requirement for client mediation. Quantitative results show that proposed model is reliable and exact for hepatic volume assessment in a clinical course of action with testing exactness 98.8%.
Privacy during epidemic of COVID-19: a bibliometric analysis

1,226 articles on privacy and COVID-19 were published by authors from 69 countries in this year's issue. COVID 19's privacy is now the focus of many researchers' attention. The present body of knowledge on privacy for COVID-19 digital technologies has been thoroughly analyzed, and a concise overview of research status and future developments can be gleaned. This paper conducted a bibliometric examination of privacy using the Scopus dataset. Utilizing VOSviewer software, the relevant literature papers published on this topic were examined to determine the field's development history, research hotspots, and future directions. Over time, there has been a rise in the number of studies published in privacy for COVID-19, particularly after 2020, and the growth rate has been steadily increasing. Regarding published research, the United States and China lead the pack. These articles appeared in primarily English-language journals and conference proceedings. Privacy and COVID-19 research was mostly computer science. The most used terms in privacy and COVID-19 were data privacy and humans. This paper examines the evolution of privacy and COVID-19 research and indicates current research priorities and future research goals. Furthermore, the privacy and COVID-19 study seem to be a promising sphere as this study identifies 26 domains.
Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.
Pengaruh Pembelajaran Daring Di Masa Pandemi Covid-19 Terhadap Prestasi Belajar Matematika Siswa SMA

This study aims to determine the effect of the online learning during the Covid-19 pandemic on the Mathematics learning achievement of twelfth grade students of SMA Negeri 2 Kaledupa. This type of study was a quantitative research with ex post facto method, with a research population of 26 students in one class with 12th grade science students at SMA Negeri 2 Kaledupa. This study used the entire sample of class 12 with the number of students 26 students. The instruments were an online learning questionnaire and a form of documentation of even semester final exam results. The results of this data analysis through simple linear regression analysis, it was concluded that online learning during the Covid-19 pandemic had no impact on the mathematics learning achievement of 12th grade science students at SMA Negeri 2 Kaledupa. This could be seen in the calculated F value of 0.569 with a critical value of 0.574 &gt; 0.05, this meant that H0 was accepted. In other words, there was no effect of the online learning process during the Covid-19 pandemic on the mathematics learning achievement of twelfth grade students of SMA Negeri 2 Kaledupa. Keywords: online learning, covid-19 pandemic, math learning achievement
An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room

To reduce the risk of elderly people falling in a private room without relying on a closed-circuit television system that results in serious privacy and trust concerns, a fall monitoring system that protects the privacy and does not monitor a person’s activities is needed. An ultrasonic-based sensor system for elderly fall monitoring in a smart room is proposed in this study. An array of ultrasonic sensors, whose ranges are designed to cover the room space, are initially installed on a wall of the room, and the sensors are rotated to transmit and receive ultrasonic signals to measure the distances to a moving object while preventing ultrasonic signal interference. Distance changes measured by ultrasonic sensors are used as time-independent patterns to recognize when an elderly person falls. To evaluate the performance of the proposed system, a sensor system prototype using long short-term memory was constructed, and experiments with 25 participants were performed. An accuracy of approximately 98% was achieved in this experiment using the proposed method, which was a slight improvement over that of the conventional method.
A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications

A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human-machine or human-robot interactions. For example, in high-accuracy advent applications such as Driver's Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.
Effective Face Mask and Social Distance Detection with Alert System for Covid-19 Using YOLOv5 Model

Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-to-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting.
Time Series Forecasting of Significant Wave Height using GRU, CNN-GRU, and LSTM

Predicting wave height is essential to reduce significant risks for shipping or activities carried out at sea. Waves inherit a stochastic nature, mainly generated by wind and propagated through the ocean, making them challenging to forecast. In this paper, we design time series wave forecasting using a deep learning model, which is a hybrid Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) or CNN-GRU. We use two time series of wave data sets, i.e., reanalysis data from ERA5 by ECMWF and GFS from NOAA. As a study area, we choose Pelabuhan Ratu, located in the south of West Java which is connected to the open Indian Ocean. Moreover, we also compare the results by using other deep learning models, i.e., the Long Short-Term Memory (LSTM) and GRU. We evaluated these models to forecast 7, 14, and 30 days. Models' performance is assessed using RMSE, MAPE, and Correlation Coefficient (CC). For predicting 30 days, using the ERA5 data, the CNN-GRU model produces relatively accurate results with an RMSE value of 1.8844 and CC of 0.9938, whereas for the GFS data, results in RMSE value of 1.8852 and CC of 0.9915.
Consent: A Research and Design Lens for Human-Computer Interaction

Consent has become an important concept across multiple areas within HCI/CSCW, community advocacy work, and the tech industry, for understanding social computing problems and designing safe and agentic computer-mediated communication. Recent research has studied consent in various topics, such as online-to-offline interaction and harm, data privacy and security, research ethics, and human-robot interaction. The goal of this panel is to bring together researchers and practitioners to discuss how consent has been defined and studied within HCI and adjacent fields, and how cross-field discourse around consent can inform future work that pursues safe and equitable computing. We aim to introduce consent as a multifaceted research and design lens to the HCI and CSCW community and illuminate ways that consent can contribute to better understanding or re-imagination of contemporary research interests. Lastly, the panel aims to spark cross-field communication around consent to identify latent connections across research topics and foster synergistic collaborations.
EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.
Blockchain Design with Optimal Maintenance Planning

Rapid advancement of data management and sharing technology has urged organizations to develop effective maintenance management systems. This research, therefore, proposes and implements an Electronic Repair Records (ERR) system for the blockchain of maintenance planning and management. In ERR, this research develops two optimization models for scheduling and sequencing failure repairs from different types over multiple periods. Each failure repair is treated as a block, for which the data, current hash, and previous hash are obtained from failure repair parameters, resources availability information, and the optimal values of the start and finish times of the assigned failure repairs. The scheduling model assigns failure repairs to technicians and in maintenance shops on each scheduling period at a minimal total cost of delay, undertime, and overtime costs, while the sequencing model sequences the assigned failure repairs at minimal total overtime costs and minimum total repair start times. Once the blocks are confirmed, the blockchain is then shared through an electronic network among all maintenance departments. The developed ERR system was implemented to manage the repairs of 36 failures from different failures in six maintenance shops over a period of three days. The results showed that this system is found to be effective in managing optimal repairs and efficient in improving the utilization of available resources in maintenance shops. These advantages may result in significant savings in maintenance costs and better utilization of resources. In conclusion, the developed ERR system including the optimization models can provide in real-time assistance and de-centralized technology to planning engineers when managing maintenance activities over multiple periods in a wide range of business applications.
EFAFN: An Efficient Feature Adaptive Fusion Network with Facial Feature for Multimodal Sarcasm Detection

Sarcasm often manifests itself in some implicit language and exaggerated expressions. For instance, an elongated word, a sarcastic phrase, or a change of tone. Most research on sarcasm detection has recently been based on text and image information. In this paper, we argue that most image data input to the sarcasm detection model is redundant, for example, complex background information and foreground information irrelevant to sarcasm detection. Since facial details contain emotional changes and social characteristics, we should pay more attention to the image data of the face area. We, therefore, treat text, audio, and face images as three modalities and propose a multimodal deep-learning model to tackle this problem. Our model extracts the text, audio, and image features of face regions and then uses our proposed feature fusion strategy to fuse these three modal features into one feature vector for classification. To enhance the model’s generalization ability, we use the IMGAUG image enhancement tool to augment the public sarcasm detection dataset MUStARD. Experiments show that although using a simple supervised method is effective, using a feature fusion strategy and image features from face regions can further improve the F1 score from 72.5% to 79.0%.
A Printed Arabic Optical Character Recognition System using Deep Learning

Recognizing Arabic script is challenging for many reasons: The Arabic language is cursive and morphologically rich.There is a high similarity between Arabic letters.Moreover, Arabic has many diacritics and dots, and they change the letter's phonetic transcription.This study proposes a Printed Arabic Optical Character Recognition approach (PAOCR) based on the state-of-the-art You Only Look Once (YOLO) object detector.Four techniques were proposed and implemented to design an end-to-end Arabic OCR system.First, the YOLO4 object detector is customized and trained on deep Convolutional Neural Networks (CNNs) to recognize Arabic characters.Second, the overlapped bounding boxes are processed to keep the most accurate box for each character.Third, the Hunspell library checks the word spelling and corrects the wrong ones.Fourth, edit distance is used to compare OCR misspelled words with Hunspell's suggestions and choose the closest correct word.The experimental results in the Arabic Printed Text Image (APTI) dataset showed that the Word Recognition Rate (WRR) of customized YOLO4 Arabic OCR is 66.7%, whereas applying the three proposed techniques with YOLO4 achieved 82.4%.A comparison with two existing OCR systems Tesseract and ABBYY showed that the proposed approach achieved a 95.7% Character Recognition Rate (CRR), whereas Tesseract and ABBYY achieved 92.3 and 84.8%, respectively.
Using Low-immersive Virtual Reality in Online Learning: Field Notes from Environmental Management Education

Recent research in the field of virtual reality (VR) education is dominated by the application, experience, and effectiveness of high-immersive environments. However, high-immersive VR may not be accessible to all learners, with online distance learning students in particular unable to fully engage without being supplied with appropriate accessories. These field notes shed light on the role of low-immersive VR as a desktop tool for online distance learning students, exploring student experience of using 360° virtual spaces to undertake a summative assessment. Primary data collection in the form of an anonymous online survey was employed to gather feedback from postgraduate environmental management students who used low-immersive VR to undertake an environmental management system audit of a university campus. Quantitative results were analysed using descriptive statistics and qualitative responses using thematic analysis. Findings indicated that with guidance from the academic teaching staff and practice using the software, the majority of students felt both prepared and happy to undertake a summative assessment using VR spaces. Skills development and an appreciation of the effectiveness of the assessment approach were also highlighted as positive outcomes reinforcing findings from literature on the value of VR to improve learning outcomes particularly with practical tasks. Limitations of the assessment content and software were however noted by students, but both could be resolved with adaptations to the tool. It is hoped this research will be valuable to online education providers to demonstrate the value of using low-immersive VR within their programmes.
A Prefeasibility Solar Photovoltaic Tool for Tropical Small Island Developing States

Small island developing states (SIDS) are the lowest emitters of greenhouse gases yet are the most vulnerable to the impacts of global climate warming. Many islands, such as the Caribbean islands, identified solar photovoltaics as a technology for reducing greenhouse gas emissions from their electricity sector. However, prefeasibility economic studies for photovoltaics are challenging as operational photovoltaic system data are nonexistent, and the measured solar radiation datasets are limited. Thus, a prefeasibility PV tool that uses ground-measured global horizontal irradiation and a supplementary photovoltaic derating factor model is proposed for use in tropical SIDS. In addition, the bias of a modelled irradiation dataset was quantified with limited solar radiation data for a tropical Caribbean SIDS, Trinidad and Tobago. For this SIDS, the tool estimates the annual energy output of a 50 MW photovoltaic system to be 57,890 MWh and the levelized cost of electricity to be USD 0.12/kWh. The performance of the proposed tool was comparable with two existing prefeasibility models, RETScreen and SAM, which use past ground measurements and modelled data, respectively. The biases in the annual irradiation data for RETScreen and SAM were determined to be 6% and 25%, respectively, against the solar irradiance dataset used. The proposed tool may be useful for first approximation prefeasibility photovoltaic studies in similar regions with limited climatic data.