Reviewerly/exHarmony huggingface dataset:
Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios.
Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution

The additive Gaussian white noise (AGWN) level in real-life images is usually unknown, for which the empirical setting will make the denoising methods over-smooth fine structures or remove noise incompletely. The previous noise level estimation methods are easily lost in accurately estimating them from images with complicated structures. To cope with this issue, we propose a novel noise level estimation scheme based on Chi-square distribution, including the following key points: First, a degraded image is divided into many image patches through a sliding window. Then, flat patches are selected by using a patch selection strategy on the gradient maps of those image patches. Next, the initial noise level is calculated by employing Chi-square distribution on the selected flat patches. Finally, the stable noise level is optimized by an iterative strategy. Quantitative, with association, to qualitative results of experiments on synthetic real-life images validate that the proposed noise level estimation method is effective and even superior to the state-of-the-art methods. Extensive experiments on noise removal using BM3D further illustrate that the proposed noise level estimation method is more beneficial for achieving favorable denoising performance with detail preservation.
Geology, mineralization, and alteration of B Prospect of the epithermal Au-Ag deposit in Central Thailand: A study on Chatree?s satellite deposit for future gold exploration

The B prospect is located at the southeast of the Chatree gold-silver deposit.The mineralization is hosted in the Late Permian-Early Triassic Chatree volcanic sequence consisting of volcaniclastic and volcanogenic-sedimentary rocks ranging in composition from basaltic andesite to rhyolite.At the study area, the total thickness of volcanic succession is about 300 meters, and the succession can be divided into 3 main stratigraphic units from bottom to top, namely 1) porphyritic andesite unit (Unit 3), 2) polymictic intermediate breccia unit (Unit 2), and 3) volcanogenic sedimentary unit (Unit 1).The ore zones are mainly confined to polymictic intermediate breccia and volcanogenic sedimentary units (Unit 1 and 2).At least 3 stages of mineralization have been identified, namely 1) quartz-pyrite (Stage 1), 2) quartz-chlorite-calcite-sulfide-electrum (Stage 2), and 3) quartz-calcite (Stage 3) veins/veinlets.Gold occurs chiefly in Stage 2 mineralization which is characterized by typical vein textures of low sulfidation epithermal deposit (e.g., crustiform, colloform banding, and comb textures).Pyrite is the primary sulfide mineral with minor sphalerite, chalcopyrite, and galena.Gold occurs as electrum with fineness ranging from 506 to 632.The hydrothermal alteration at B prospect is composed of 2 main types: (1) quartz-adularia (silicic alteration) assemblage close to ore zone and (2) adularia-quartz-illite-calcite-chlorite (phyllic alteration) assemblage distal to ore zone.Based on characteristics of mineral assemblages, textures, and alterations, the mineralization at B prospect can be classified as a low sulfidation epithermal gold-silver style deposit.
Automatic Detection of Lung Cancer Using the Potential of Artificial Intelligence (AI)

The histopathology images are effective in identifying the location and level of cancer. In this chapter, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge-based top-down level set (SDREL). Further, the feature descriptors—Alexnet and Gray Level Co-Occurrence Matrix (GLCM) features—were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50% of accuracy on the LC25000 lung histopathological image dataset.
An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM

Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network-4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters-were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted.
Position Detection of Doors and Windows Based on DSPP-YOLO

Autonomous exploration of autonomous mobile robots in unknown environments is a hot topic at present. Object detection is an important research direction in improving the autonomous capability of autonomous mobile robots in unknown environments. In object detection, doors and windows have many similar features and are difficult to distinguish. Therefore, improving the detection accuracy of doors and windows is helpful to improve the autonomous ability of autonomous mobile robots. Aiming at the problem of insufficient doors and windows detection accuracy caused by the large difference between the receptive fields of doors and windows, this paper proposes DSPP-YOLO (DenseNet SPP) algorithm. Firstly, on the basis of deepening the network addition, to prevent the loss of shallow location feature information, some residual blocks in YOLOV3 are improved to dense blocks by using the idea of DenseNet. Secondly, the spatial pyramid pooling (SPP) structure is fused into the YOLOV3 feature extraction network to realize multiscale receptive field fusion. Finally, K-means ++ algorithm is used to re-cluster the size of candidate boxes to reduce the error caused by candidate boxes. DSPP-YOLO realizes the position detection of doors and windows by an autonomous robot in an unknown, complex environment. This method is tested. Under the condition of the same data set, the detection accuracy of the DSPP-YOLO algorithm is 77.4% for doors and 38.1% for windows. Compared with YOLOV3 algorithm, the calculation consumption time of the DSPP-YOLO algorithm does not increase, and the detection accuracy of doors is improved by 3.3%, the detection accuracy of windows is improved by 8.8%, and the average accuracy of various types is improved by 6.05%.
Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs).Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length.In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments.This work proposes polynomial functions for path planning and obstacle avoidance.Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path.Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation.An accurate data set and metrics were used to measure the efficiency of the proposed method.The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.
Energy threshold changes in volcanic activity at Mt. Etna (Italy) inferred from volcanic tremor

From the 2010s on, pattern classification has proven an effective method for flagging alerts of volcano unrest before eruptive activity at Mt. Etna, Italy. The analysis has been applied online to volcanic tremor data, and has supported the surveillance activity of the volcano that provides timely information to Civil Protection and other authorities. However, after declaring an alert, no one knows how long the volcano unrest will last and if a climactic eruptive activity will actually begin. These are critical aspects when considering the effects of a prolonged state of alert. An example of longstanding unrest is related to the Christmas Eve eruption in 2018, which was heralded by several months of almost continuous Strombolian activity. Here, we discuss the usage of thresholds to detect conditions leading to paroxysmal activity, and the challenges associated with defining such thresholds, leveraging a dataset of 52 episodes of lava fountains occurring in 2021. We were able to identify conservative settings regarding the thresholds, allowing for an early warning of impending paroxysm in almost all cases (circa 85% for the first 4 months in 2021, and over 90% for the whole year). The chosen thresholds also proved useful to predict that a paroxysmal activity was about to end. Such information provides reliable numbers for volcanologists for their assessments, based on visual information, which may not be available in bad weather or cloudy conditions.
Alpha-SGANet: A multi-attention-scale feature pyramid network combined with lightweight network based on Alpha-IoU loss

The design of deep convolutional neural networks has resulted in significant advances and successes in the field of object detection. However, despite these achievements, the high computational and memory costs of such object detection networks on the edge or in mobile scenarios are one of the most significant barriers to their broad adoption. To solve this problem, this paper introduces an improved lightweight real-time convolutional neural network based on YOLOv5, called Alpha-SGANet: A multi-attention-scale feature pyramid network combined with a lightweight network based on Alpha-IoU loss. Firstly, we add one more prediction head to detect different-scale objects, design a lightweight and efficient feature extraction network using ShuffleNetV2 in the backbone, and reduce information loss using the SPP module with a smaller convolutional nucleus. Then, cleverly, employ GAFPN to improve feature transition processing in the neck region, including the usage of the Ghost module to construct efficient feature maps to help prediction. The CBAM module was further integrated to find areas of interest in the scene; finally, combined with Alpha-IOU loss for model supervision training, the biggest performance improvement was achieved. The experiment results show that, compared with YOLOv5s, our proposed method can achieve higher accuracy with fewer parameters and has real-time speed through verification on the PASCAL VOC dataset and MS COCO dataset.
Detection of Epithelial Giant Cells in Nasal Aspirate Cytological Smears Using Deep Learning and Computer Vision Techniques: an Approach for Early Diagnosis of Measles Disease

Measles disease is usually diagnosed through an RT-PCR test, which detects the measles-specific IgM antibody in serum samples. However, this approach is highly expensive and requires a lot of sample preprocessing making it a laborious task. Another approach for measles identification could be a cytological smear test – which is a low-cost, quick, and accurate method for the early detection of measles disease. Due to the lack of experience in interpreting smears, its usage in clinical practice is currently limited. We have designed a MesoSpot, an automatic segmentation method for the first time that can precisely define the region of giant cells in nasal aspirate cytological smears and assist medical experts in the speedy diagnosis of measles disease. MesoSpot was developed by training 500 cytological smear images collected from various web sources. We have used 50 cytological smear images to validate the model and evaluated its performance using various metrics. The main purpose of the proposed study is to detect multinucleated giant cells and estimate their size and number, which aids in assessing the disease's severity. To annotate the giant cells in each cytological smear, we have applied a semantic segmentation approach using APEER software. UNET architecture was used to train the model, and image processing techniques were used to spot giant cells and predict their size. Various performance metrics were employed to assess the model's efficiency. The model's output was also compared to the ground truth image and statistically confirmed.
The Use of Augmented Reality to Strengthen Competence in Data Analysis and Problem Solving in Engineering Students at the Universidad del Valle de México

The objective of this research was to analyze the improvement in the data analysis and problem-solving competence of students of industrial and systems engineering (IIS) and mechatronics engineering (IMEC) through the use of this technology and its impact on the results of the undergraduate general examination (EGEL). A training course was held for teachers and students for the design of learning objects (LO), and a questionnaire on the use of AR and the improvement in learning was administered. AR is a technology that has begun to be introduced in different contexts and at different educational levels. The results obtained through the Wilcoxon test and the multiple correspondence analysis (MCA) showed that there were improvements in academic performance with the use of AR and an interest in this tool being used during the academic training process.
A milestone in the era of esports: The Olympics through the lens of virtual reality

OPINION article Front. Psychol., 01 November 2022Sec. Movement Science and Sport Psychology Volume 13 - 2022 | https://doi.org/10.3389/fpsyg.2022.990189
Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM

User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings.
A mathematical model of coronavirus transmission by using the heuristic computing neural networks

In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented by coupled system of ordinary differential equations and is studied for three different cases of initial conditions with suitable parametric values. This model is studied subject to seven class of human population N(t) and individuals are categorized as: susceptible S(t), exposed E(t), quarantined Q(t), asymptotically diseased IA(t), symptomatic diseased IS(t) and finally the persons removed from COVID-19 and are denoted by R(t). The stochastic numerical computing SCGNNs approach will be used to examine the numerical performance of nonlinear mathematical model of COVID-19. The stochastic SCGNNs approach is based on three factors by using procedure of verification, sample statistics, testing and training. For this purpose, large portion of data is considered, i.e., 70%, 16%, 14% for training, testing and validation, respectively. The efficiency, reliability and authenticity of stochastic numerical SCGNNs approach are analysed graphically in terms of error histograms, mean square error, correlation, regression and finally further endorsed by graphical illustrations for absolute errors in the range of 10−05 to 10−07 for each scenario of the system model.
Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation

As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.
Assessor Selection Process for Perceptual Quality Evaluation of 360 Audiovisual Content

For accurate and detailed perceptual evaluation of compressed omnidirectional multimedia content, it is imperative for assessor panels to be qualified to obtain consistent and high-quality data.This work extends existing procedures for assessor selection in terms of scope (360 • videos with high-order ambisonic), time efficiency, and analytical approach, as described in detail.The main selection procedures consisted of a basic audiovisual screening and three successive discrimination experiments for audio (listening), video (viewing), and audiovisual using a triangle test.Additionally, four factors influencing quality of experience, including the simulator sickness questionnaire, were evaluated and are discussed.After the selection process, a confirmatory study was conducted using three experiments (audio, video, and audiovisual) and based on a rating scale methodology to compare performance between rejected and selected assessors.The studies showed that (i) perceptual discriminations are influenced by the samples, the encoding parameters, and some quality of experience factors; (ii) the probability of symptom occurrence is considerably low, indicating that the proposed procedure is feasible; and (iii) the selected assessors performed better in discrimination than the rejected assessors, indicating the effectiveness of the proposed procedure.
A Computational-Efficient Deformable Convolution Network Accelerator via Hardware and Algorithm Co-Optimization

Deformable convolution networks (DCNs) shows performance boosts on object recognition tasks by enabling variable geometric modeling. However, the irregular addressing of memory accesses makes it inefficient for hardware acceleration. In this paper, we propose a computational-efficient hardware accelerator for DCNs. First, a hardware-friendly DCNs inference scheme is introduced based on the original DCNs algorithm with little accuracy loss. Secondly, a hardware accelerator architecture is presented correspondingly, and an speed matching method is introduced to maximizing the number of deformable layers without latency increase. The proposed accelerator is implemented on the Arria 10 FPGA, results of which show that the proposed design achieves the highest throughput and DSP efficiency compared with state-of-the-art DCNs accelerators.