Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Facilitators in Online Discussion in Relation to Communication Apprehension and Attitudes Toward Nonverbal Communication

Increasingly, discussions with resident participation take place online. In online discussions, there are many limitations, and nonverbal communication is often inhibited. This study analyzed the relationship between participants' attitudes toward nonverbal communication and communication apprehension. The relationship between awareness of the use of voice behavior and communication apprehension was found, suggesting that uncertainty about whether one's voice is being heard accurately may be a factor in apprehension. In addition, since there is a relationship between characteristic communication anxiety and state communication anxiety among participants with low awareness of the use of voice behaviors, it is possible that raising awareness of the use of voice behaviors can lower communication apprehension.
Analysis of the Reconstruction Accuracy of Compact Polarized SAR Pseudo Quad-Pol Data for Use in Ocean Surface Observations

This paper assesses the capability of Compact Polarized (CP) Synthetic Aperture Radar (SAR) to explore ocean surface backscattering by reconstructing the pseudo quad-pol data. Besides using Souyris or Nord algorithms, 8 scenes of Radarsat-2 quad-polarized images are used to develop an empirical model for estimating the initial value of N in Collins's algorithm. The robustness the three published algorithms are discussed together with the reconstruction error of polarization scattering matrix [S]. The comparisons show that Collins's algorithm is superior to the other two algorithms in terms of reconstruction errors for the HH, VV, especially, the HV channels. In addition, the influence of each algorithm on polarimetric parameters is investigated. Based on the retrieval of the polarimetric parameters, it is concluded that Souyris, Nord and Collins algorithms all have similar (high) accuracies for copolarization backscattering coefficient σ0HH, σ0vv, co-polarization correlation coefficient $p$ and first eigenvalue l1, while for cross-polarization backscattering coefficient σ0vv and scattering angle α, the accuracy of Collins's algorithm performs better than that of Souyris or Nord. Although all three algorithms produced low accuracies for second eigenvalue $l$2, third eigenvalue l3, scattering entropy $H$ and scattering anisotropy A, the results obtained using Collins algorithm were better than those produced by the other two algorithms.
Detecting Fake News using Blockchain and Deep Learning

The modern world relies on the internet for information regarding global news, which can be found through online resources or social media sites like Twitter, Facebook etc. News spreads swiftly among a very wide number of people as a result of various media. Also, it contributes to the spread of fake news, which can have a highly negative effect on society or an individual. For instance, spammers will profit from click-bait ads with alluring news headlines about the stock market, which causes countries’ revenues to decline as a result of wrong investments. As a result, there are problems and a lot of positive and realistic news that is difficult to believe. The objective is to find a solution that enables users to differentiate between legitimate and fraudulent news by combining Deep Learning with Blockchain, addressing these issues effectively and ensures that results cannot be tampered with once they have been announced.
The Study and Improvement of X Band Patch Antenna Integrated on Junction Solar Cells

This study focuses on studying the achievement of an antenna that works in an emergency situation that is self-feeding so that it can ensure communication with the GEO satellite, and it also works in a normal situation as an alternative backup antenna. Although the solar cell reduces the efficiency of the antenna, the gain is improved from 5 dB to 6.5 dB but minimized adaptation of S11 from -39.8dB to -23.07 dB, the antenna performance remains acceptable and achieves the required percentage for the effective functioning of the tracking TTC system application. The shape of the antenna used blocks the passage of light and therefore the energy efficiency is small in cells parallel to the antenna, but the effect of the antenna on the cutting tension remains negligible.
An Optimal Scheduling Scheme of Microgrid Based on Improved Differential Evolution Algorithm

Considering the power balance, charge-discharge control of battery and output limit of micro-source as the system constraints, this paper proposes a multi-objective optimal scheduling model for stand-alone microgrid. The model contains multiple distributed power sources. The model ensures the consumption of new energy sources such as wind power and photovoltaic, as well as the reliable power supply to the loads. Then the improved Differential Evolution (DE) algorithm, to which a daptive variational factor is introduced, is used to solve the above model. The results show that the improved DE has better global search capability. The microgrid optimization model based on the improved DE algorithm reduces the total cost by 9.8% compared to the classical DE algorithm.
Emotion Detection Using Speech and Text Recognition: An Overview

One of the central concepts behind all recognition techniques used to date is the identification of speech emotions. To gain a more comprehensive understanding of the process, it is essential to have prior knowledge of how this is handled. This paper presents numerous techniques for detecting speech emotions, and several approaches have been developed for recognition in various contexts. This paper discusses some of the techniques previously mentioned for different scenarios.
A Surrogate Optimization Method for Automatically Generating Greener Software Using LLVM

Traditionally, computational hardware (HW) has undergone a quick evolution that, in recent years, was complemented with the arise of a large variety of architectures. Modifying software (SW) so that it can perform efficiently in these new platforms is a costly process, but necessary in many cases. Therefore, the development of novel methods to automatically adapt SW to efficiently perform in any HW architecture is required. However, this is a very complex and time consuming problem, as the impact of code optimizations on the performance of SW is unknown, with a strong dependence on (i) the other applied optimizations, (ii) the code to optimize, and (iii) the HW where it executes. In this work, we propose the Faevol framework, which makes use of evolutionary computation to find the best sequence of LLVM optimizations that maximizes the performance of a given SW on the underlying HW. The performance is measured in terms of both the run time and the energy consumption of the computer when executing the SW. Additionally, we develop two predictive models based on artificial neural networks to accurately estimate the impact of any sequence of LLVM optimizations on a SW code. These models are used in a surrogate version of Faevol in order to accelerate the optimization process. We demonstrate how incorporating the proposed predictive models for energy consumption and run time in Faevol leads to similar results as when actually running the SW to take performance measurements, with a considerably lower time demand.
Flood Prediction Using Logistic Regression

Flood Prediction is crucial in mitigating its impact on human life, property, and the environment. This paper proposes a machine learning approach using Logistic Regression for flood prediction, which involves the usage of rainfall data to determine the probability of flood occurrence. By analyzing the average rainfall and training the model with the vast dataset, our model can generate more accurate predictions on flood occurrence. Our mobile application provides an early warning system through the flood risk map that is accessible to users. Our dataset comprises 37 regions in Tamil Nadu, which are the meteorological stations and also the base stations of dams. The latter allows for notification in the event of flood and dam overflow respectively. Furthermore, our mobile app enables users to easily identify camps and shelters established during flood periods and even add information about them. In addition, our mobile app also provides users with the necessary helpline numbers to reach out for assistance during flood times.
Core Temperature Estimation of Lithium-ion Batteries Under Internal Thermal Faults Using Neural Networks

This paper presents a core temperature estimation scheme for LIBs under internal thermal faults. First, a model-based incipient internal fault detection scheme is proposed using the SOH-coupled electro-thermal-aging model of the LIB. A nonlinear observer estimates the states and parameters (internal resistance) of the SOH-coupled state space model and generates multiple residuals, such as output voltage and surface temperature. Second, an adaptive threshold is designed to detect convective cooling and internal thermal resistance faults. The adaptive threshold minimizes the false positives due to the battery’s health degradation and model uncertainties. Finally, a second neural network (NN)-based observer is proposed to learn the fault dynamics and estimate the core temperature under fault conditions. Analytical and numerical results are presented to show the convergence of the state and NN weight estimation errors.
Explainability of Machine Learning Models for Hydrological Time Series Forecasting: The Case of Neuro-Fuzzy Approaches

This research evaluates the explainability of rules obtained in Neuro-Fuzzy (NF) models in hydrological time series forecasting. Thus, three NF models (Adaptive Network-based Fuzzy Inference System (ANFIS) and two versions of the Self-Identification Neuro-Fuzzy Inference Model (SINFIM)) were developed, considering the rain-runoff modeling as a particular case. The ANFIS model had the lowest performance, with many fuzzy rules challenging to interpret. The SINFIM 01 model performed best, but not all fuzzy rules were well explained. However, the SINFIM 02 model had a lower performance than the SINFIM 01 model but with more explainable fuzzy rules. With the examples developed, it is highlighted that within the NF models, it is necessary to focus on their performance during the forecast and look for a trade-off with explainability, thus taking advantage of their characteristic of more transparency than the black-box models.
Sentimental Analysis from Social Media Post for Disaster Management

In this modern era, the use of social media is very important for raising consciousness and for amusement. Posts that are related to disasters are collected from social media like Twitter and Facebook and the writer's attitude is analyzed using sentimental analysis. Social media in times of calamities and natural disasters look into whether first responders may use this information to enhance crisis management and situational awareness. For disaster management, social media posts are a useful source of information. They can be used to detect sentiment and pinpoint the needs of disaster victims. Experimental results reveal that Bidirectional long short-term memory performs relatively better when compared to other models.
Channel Estimation of RIS-Assisted Communication System Based on Residual Ratio Threshold

Reconfigurable intelligent surface (RIS) can improve the coverage range and signal quality of wireless networks. But its performance depends on the estimation accuracy of channel state information (CSI). The compressed sensing based on stage-wise orthogonal matching pursuit (StOMP) algorithm can be employed to estimate the CSI, however, the hard threshold methods involved in the traditional StOMP can introduce the undesired noise components and redundant atoms, thereby reducing the accuracy of estimated CSI and even lower the convergence speed. To this end, a novel channel estimation approach using the improved StOMP algorithm is proposed in RIS-assisted system to improve the channel estimation quality while achieving the high-speed convergence. By utilizing the difference between residual ratios of adjacent iterations, a normalization procedure can be performed to optimize the iterative stopping conditions, and the algorithm can effectively reduce the impact of out-of-band noise while optimizing the iteration stopping condition with normalized residual ratios. Furthermore, it can reduce redundant atom selection and leads to faster system operation. The results demonstrate that the proposed method can reduce the run time by 5 times at a relatively low complexity with favorable estimation accuracy. Moreover, it can also achieve 86% reduction in the selection of redundant atom.
Investigation of Integrated Bidirectional Converter with Eight Switch Inverter for Electric Vehicle Application

An efficient EV can be made as an impressive eco-friendly option. In order to achieve this highly reliable bi-directional converters are required. The aim of this work is to investigate integrated Bidirectional Interleaved Converter along with an eight-switch inverter. The eight-switch inverter allows bidirectional flow of power. The interleaved converter connects the low voltage battery pack and the inverter. These have some of the advantages like cancellation of ripples due to current splitting, higher efficiency of system, high power density. The eight-switch inverter facilitates an additional single- phase output.
Experimental Comparison of H-Infinity Synthesis and Feedback Linearization on a MIMO Gimbal Example

This study investigates the controller types and effects of the design techniques on gimbal stabilization. Since implementing the controllers and determining the system’s robustness is straightforward, the frequency domain design approaches are widely used with linear controllers in electro-optical inertial stabilized systems. Besides, the required system models can be obtained from experimental data easily. Nevertheless, the system model could change in the operational conditions. Therefore, nonlinear controllers are needed in this situation. In this investigation, we obtained a proper system model using system identification methods to design a mixed-sensitivity H∞ controller. Then, we proposed an input-output feedback linearization controller with a linear quadratic regulator (LQR) based state feedback controller design to stabilize the 2 degrees of freedom (DOF) gimbal. Finally, we compared these two controllers regarding reference tracking, sensitivity function, and disturbance rejection performances on the experimental setup under 6 DOF disturbance profiles.
Segmentation and Classification of 3D Lung Tumor Diagnoses Using Convolutional Neural Networks

This work uses data from the 2022 Kaggle Data Science Bowl to provide a CAD system for determining the classification of Computed Tomography (CT) images with unidentified nodules in the context of lung cancer. As a first stage in the segmentation procedure, threshold is used to isolate the tissue in the lungs from the other parts of the CT image. The second-best result for lung segmentation was achieved by thresholding. As a result, the first strategy of feeding the segmented CT images into 3 Dimension Convolution Neural Networks (3D CNNs) for classifications failed. Instead, a customized U-Net trained on LUNA16 data (CT images with tagged nodules) was used to identify potential nodule locations in the Kaggle database CT scans. Due to the high number of false positives generated by the U-Net nodule identification, the output from segmentation CT scans of the lung was fed into lung cancer scan positivity is often determined using 3D convolutional neural networks. In existing method is not compatible to detect lung nodules and does not predict cancer types. A proposed work is Lung Cancer Diagnosis utilizing Heterogeneous Neural Network (LCD-HNN) is a unique approach proposed in this study for early and precise detection. The CT characteristics are extracted using deep neural networks. Detecting malignant cells at an early stage is crucial to saving the patient's life, and this detection relies heavily on the precision of GLCM feature extraction. This research also makes use of a state-of-the-art using a 3D convolutional neural network to boost diagnostic precision. In terms of 3D CNN precision, the test set was 98.6%. Proposed CAD approach outperforms existing CAD systems in the literature since it consists of just three main steps (segmentation, nodule candidate identification, and malignancy classification) and so requires less labeling data for training and detection and may be utilized for a broader range of malignancies. Differentiating between both benign and malignant tumors is also possible using the proposed method. Standard statistical methods are used to analyze the data, and the findings show that the suggested hybrid DL methodology is effective at detecting lung cancer at an early stage.
Detection of Retinopathy of Prematurity using ResNet Based Deep Features and Support Vector Machine Classifier

Retinopathy of prematurity (ROP) is a major problem that affects preterm babies all over the world. It can cause blindness in children, but it can be treated if it is found early and correctly. A way for diagnosing ROP disease called "deep learning" is suggested. First, the step of cleaning is done. Then, the suggested model uses the ResNet-50 feature map and gives it to a Support vector machine (SVM) to be categorized. So, metrics like accuracy, specificity, sensitivity, and precision are used to measure how well the model works. The results show that the suggested model is 98% more accurate than other methods. This shows that this method is better at predicting ROP in premature babies.
An Impulsive Noise Suppression Method Based on Chi-square Distribution and Robust Local Regression Smoothing

The impulsive noise with large amplitude exists in distribution network, which brings great challenge to power system monitoring. Aiming at the issue of low measurement accuracy of phasor measurement unit (PMU) in presence of impulsive noise. , a method for suppressing impulsive noise is proposed. Firstly, the spectrum threshold is determined according to the characteristic that the normalized power spectrum of background noise follows the chi-square distribution; Then, taking advantage of the fact that the amplitude of impulsive noise is much greater than background noise, the residual is used to locate impulsive noise, and the idea of robust local regression smoothing is used to suppress impulsive noise; Finally, the noise is removed according to the spectrum threshold determined in the first step. The simulation results show that this method can effectively suppress impulsive noise and improve the measurement accuracy of the phasor measurement unit.
A Comprehensive Review of Glaucoma Detection from Fundus Images using Deep Learning

Glaucoma, a leading cause of irreversible vision loss, necessitates early detection for effective management. With the advent of deep learning techniques and advancements in imaging technology, automated systems for glaucoma detection using fundus images have gained significant attention. This research study presents a comprehensive review of recent research and advancements in glaucoma detection from fundus images using deep learning approaches. This study elaborates the fundamentals of glaucoma and fundus imaging, followed by that explores the implementation of deep learning in medical image analysis. Further, this study discusses about various pre-processing techniques, deep learning models, and performance evaluation methods employed recently. Furthermore, this study highlights the challenges and potential future directions. By offering a holistic understanding of the current state-of-the-art, this research study aims to contribute to the progress of glaucoma detection by ultimately improving early diagnosis and treatment outcomes for glaucoma patients.
Evolução do grau de fadiga em pacientes com câncer de mama em tratamento radioterápico

A radioterapia e um dos tratamentos mais usados no combate ao câncer de mama. Entretanto, apesar de tratar a doenca, tambem causa efeitos colaterais sistemicos e entre os principais apresentados, estao a indisposicao e a fadiga. Diante disso, objetivou-se avaliar a evolucao do grau de fadiga em mulheres com câncer de mama em tratamento radioterapico. Estudo longitudinal, prospectivo e descritivo, realizado em um centro de tratamento oncologico em Fortaleza, de novembro de 2013 a janeiro de 2014, com amostra de 56 mulheres em radioterapia adjuvante. A fadiga esteve presente no inicio, na metade e no final do tratamento radioterapico em respectivamente 67,5%, 73,2% e 91,1% das pacientes. O grau leve foi o que mais acometeu as pacientes nos tres estagios avaliados, sendo 51,7%, no inicio; 57,1%, no meio; e 71,4%, no final do tratamento. Pelo teste T pareado, ocorreu significância na evolucao do grau de fadiga entre o inicio e o final (p˂0,00) e entre a metade e o final (p˂0,00) do tratamento radioterapico ambos apresentaram p˂0,05. Os sintomas mais relatados entre as mulheres que apresentaram fadiga foram dor no corpo, em 9 (13,6%), e alteracao na coloracao da pele irradiada, em 8 (12,1%). As principais causas que elas relacionaram ao aparecimento da fadiga foram o deslocamento de casa ate o local de realizacao da radioterapia, 17 (25,4%), e as tarefas domesticas/ atividades diarias, 12 (17,9%). Os fatores apontados como os que mais aliviam a fadiga foram: deitar/ dormir, 26 (32,1%) e tomar banho, 16 (19,1%). A partir da analise dos dados com apoio da pratica assistencial e da literatura, conclui-se que o principal fator que desencadeava ou mesmo potencializava essa fadiga nao era apenas o tratamento radioterapico em si ou somado a propria doenca, mas os fatores socioeconomicos, dentre estes, a baixa renda familiar e atividade laboral ou domestica, associados ao modo de deslocamento para realizacao do tratamento e o tempo de espera para realiza-lo.
Multigroup and Multilevel Residential Segregation:: The U.S. case, 1989-2005

This paper presents a multigroup and multilevel approach to the study of residential segregation for the public school population in U.S. urban areas in 1989 and 2005. The multigroup approach includes Native Americans, Hispanics, Asians, blacks and whites. The multilevel approach distinguishes neighborhoods, cities, and regions, leading to notions of within-cities, between-cities, and between-regions residential segregation. Measures of these notions are computed using the Mutual Information, or M segregation index. The decomposability properties of the M index are exploited for two purposes: first, to identify the smallest set of regions in the U.S. which include similar states in terms of racial mix; and second, to establish the precise relationship between our measurements and the classic residential segregation literature that focuses on pairwise comparisons. Among the empirical results, three findings should be highlighted. First, the set of regions and racial groups for whom between-regions and between-cities segregation is important differs from the set for whom the within-cities component dominates. Second, minorities segregation declines but their population share goes up, while whites population share declines but their segregation index increases. Third, the portion of segregation disregarded by the traditional pairwise approach is large in all cases and increasing in the case of within cities segregation between whites and blacks.