Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Super-Resolution via Self-Distillation and Adversarial Learning

Semantic super-resolution (SR) is an approach that improves the SR performance by leveraging semantic information about the scene. This study develops a novel semantic SR method that is based on the generative adversarial network (GAN) framework and self-distillation. A discriminator is adversarially trained along with a generator to extract semantic features from images and distinguish semantic differences between images. To train the generator, an additional adversarial loss is computed from the discriminator’s outputs of SR images belonging to the same category and minimized via self-distillation. This guides the generator to learn implicit category-specific semantic priors. We conducted experiments for SR of text and face images using the Enhanced Deep Super-Resolution (EDSR) generator and the SRGAN discriminator. Experimental results showed that our method can contribute to improving both the quantitative and qualitative quality of SR images. Although the improvement varied depending on image category and dataset, the peak signal-to-noise ratio (PSNR) value increased by up to 0.87 dB and the perceptual index (PI) decreased by up to 0.17 by using our method. Our method outperformed existing semantic SR methods.
A Survey on Intrusion Detection System for IoT Networks Based on Artificial Intelligence

Internet of Things(IoT) devices is the prime factor in automating the real world in the past few years. These networks provide efficient utilization of resources, quality data, and benefits with reduced human input as well as have secured processing which results in a high potential for automating the healthcare and defence systems. Noticing the large number of attacks on these networks in previous years, there is a vast study to detect the intrusion by attackers to ease the use of these devices without the fear of losing data. This survey is a deep study of works on detecting the intrusion in IoT networks and devices revolving around the use of Machine Learning and Deep Learning based algorithms on real-world datasets like “Bot-IoT” and “KDD”. The survey provides a building block in understanding the basics of Intrusion and also the benefits of Intrusion detection systems and thus promotes the researchers to focus more on prevention of such attacks. Furthermore, it compiles the studies from the year 2016 and compares the models of Machine Learning and Deep Learning segregated based on Data sets used in training; also, summarizes the performance of such mechanisms.
Angular Tracking Consistency Guided Fast Feature Association for Visual-Inertial SLAM

Sparse feature-based visual-inertial simultaneous localization and mapping (SLAM) system shows great potential for accurate pose estimation in real time, especially for low-cost devices. However, the feature correspondence outliers inevitably degrade the localization accuracy, or cause failures. Unlike the existing methods that eliminate outliers by fitting a geometric model, which have high complexity and rely on model hypothesis, we present a general and efficient model-free scheme to address these challenges. In particular, we propose a novel uniform bipartite motion field (UBMF) to exactly measure the spatial transforms of sparse feature correspondences in consecutive frames. Moreover, a new recursive angular tracking consistency (RATC) guided fast feature association algorithm is designed, which can efficiently select correspondence and update UBMF simultaneously, and it also holds the linear computational complexity and theoretical performance guarantee. Furthermore, we develop a lightweight angular tracking consistency guided visual-inertial SLAM (ATVIS) system, which achieves better robustness and outperforms the state-of-the-art methods. Massive qualitative and quantitative validations are conducted by both public benchmarks and different real-world experiments, which extensively demonstrate the superiority of our method in both localization accuracy and computational efficiency.
Chess Match Outcome Prediction via Sequential Data Analysis with Deep Learning

Chess, a timeless game of strategy, has captivated minds for centuries and continues to challenge enthusiasts and grandmasters alike. “Chess Match Outcome Prediction via Sequential Data Analysis with Deep Learning” presents an innovative approach to forecasting the results of chess games by harnessing the sequential data inherent in each move. This research capitalizes on extensive datasets of chess games, encompassing diverse player ratings, time controls, and openings. The pre-process this data into a structured format, allowing for the encoding of chess positions and move sequences. Through the application of cutting-edge deep learning techniques such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, proposed model the intricate temporal relationships within the games. Suggested study addresses multiple prediction tasks, from binary classification (win/loss) to multi-class classification (win/draw/loss), enabling a comprehensive evaluation of the model's predictive capabilities. The research also delves into hyperparameter tuning and model interpretability, shedding light on the crucial factors influencing prediction accuracy. The results of proposed experiments unveil the potential of deep learning in chess outcome prediction, offering promising accuracy and performance. As chess remains an evolving game, proposed model serves as a dynamic tool adaptable to the ever-changing landscape of strategies and player behaviors. In an age where chess is both a game of tradition and innovation, this research bridges the two worlds, enriching the analytical toolkit of chess enthusiasts and offering practical applications in chess analysis tools and services.
Biogenic Aerosols—Effects on Clouds and Climate (BAECC) Final Campaign Summary

Atmospheric aerosol particles impact human health in urban environments, while on regional and global scales they can affect climate patterns, the hydrological cycle, and the intensity of radiation that reaches the Earth’s surface. In spite of recent advances in the understanding of aerosol formation processes and the links between aerosol dynamics and biosphere-atmosphere-climate interactions, great challenges remain in the analysis of related processes on a global scale. Boreal forests, situated in a circumpolar belt in the Northern latitudes throughout the United States, Canada, Russia, and Scandinavia, are, of all biomes, among the most active areas of atmospheric aerosol formation. The formation of aerosol particles and their growth to cloud condensation nuclei sizes in these areas are associated with biogenic volatile organic emissions (BVOC) from vegetation and soil.
Femtosecond laser plane-by-plane inscription of high-quality fiber Bragg gratings

Fiber Bragg gratings (FBGs) as a significant optical fiber component are widely used in many fields, such as fiber laser, optical communication and optical fiber sensing. Here, we propose and demonstrate two plane-by-plane (Pl-b-Pl) methods for inscription of fiber Bragg gratings in optical fiber using femtosecond laser. At first, the spherical aberration (SA) combined with the scanning process was used to introduce an expanded rectangular refractive index modification (RIM) region in conventional single-mode fiber (SMF). The cladding mode resonances can be suppressed thoroughly. And this FBG exhibits a significant enhancement in the coupling strength coefficient (i.e. 3177.6 m-1), In addition, we also propose using SA and cylindrical telescope to achieve RIM region expansion in sapphire fiber without scanning process. The created Pl-b-Pl sapphire fiber Bragg grating (SFBG) exhibits an improved reflectivity of 6.34% and a reduced -3 dB bandwidth of 3.43 nm, and the fabrication process consumes 20 s. As such, these Pl-b-Pl methods simplify the direct grating inscription process and improve the performance of the FBGs component.
Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Oral squamous cell carcinoma (OSCC) is a significant health issue in the oral cancer domain; a screening tool for timely and accurate diagnosis is essential for effective treatment planning and prognosis in patients' life expectancy. In this paper, we address the problem of object detection and classification in the context of OSCC, by presenting a comparative analysis of three state-of-the-art architecture: YOLO, FasterRCNN, and DETR. We propose a deep learning ensemble model to address both object detection and classification problem leveraging the strengths of individual models to achieve higher performance than single models. The proposed architecture was evaluated on a real-world dataset developed by experienced clinicians who manually labeled individual photographic images, producing a benchmark dataset. Results from our comparative analysis demonstrates the ensemble detection model achieves superior performance compared to the individual models, outperforming the average value of the individual models' map@50 metric by 24% and the value of the map@95-50 metric by 44%.
Dieta de Menticirrhus americanus (Linnaeus, 1758) e Cynoscion jamaicensis (Vaillant e Bocourt, 1883) (Pisces, Sciaenidae) no sudeste do Brasil, Estado de Sao Paulo

the aim of this work was to study the diet of the scienid fishes Menticirrhus americanus and Cynoscion jamaicensis. the degree of association between the diets of the two species was investigated, since both species coexist in the southeast coast of Brazil. the food preference degree method (GPA) was employed to stomachs with level 3 of repletion. the stomachs of 306 specimens were analyzed, being 233 of M. americanus and 73 of C. jamaicensis. The food items found in the stomachs of M. americanus were crustaceans, fishes, polychaets, and molluscs. Fishes and crustaceans were found in the stomachs of C. jamaicensis. the use of the same food items by the two species can contribute for an indicative of feeding overlap. the food preference of M. americanus for crustaceans and of C. jamaicensis for fishes can minimize this overlap, making possible a coexistence of these scienid in the study area. the obtained result is in accordance with the characteristics regarding mouth position and foraging sites used by both species: M. americanus has sub-terminal mouth and forages on the bottom, whereas in C. jamaicensis the mouth is terminal, making it possible to forage in the upper of the water column.
Anomaly Detection of Smart City in IoT Cyberattacks based on Hybrid A-BiLSTM-CRF Method

In recent years, the new Internet of Things (IoT) paradigm has gained widespread adoption, and as a result, smart cities have evolved. “Smart cities” deploy their solutions in real time to enhance the convenience and happiness of urban dwellers. The network traffic of a smart city via IoT systems is rising dramatically, generating new cyber security issues, because these IoT devices are being connected to sensors that are directly connected to massive cloud servers. Researchers need to improve techniques for detecting compromised Internet of Things (IoT) devices in order to defend against these incursions. The proposed method employs the steps of preprocessing the data, selecting the features to use, and training the model. Data preprocessing in machine learning and statistics typically involves the use of normalization and standardization feature scaling techniques to convert the numerical values included in the dataset into the desired scales and ranges. Chi-square, Pearson's Correlation Coefficient, and mutual information are the three main metrics used in the feature selection process. A-BiLSTM-CRF is then used to train the models with the input features. When compared to the two most common alternatives, CRF and Bi-LSTM, the proposed method performs better. The proposed method was successful with a success rate of 97.32%.
Nutritional composition and staling properties of gluten‐free bread‐added fermented acorn flour

Abstract The present study aimed to improve the nutritional quality of gluten‐free bread with the addition of acorn flour and to determine the characteristics of the final product. Formulations were adjusted with separately non‐fermented and two different fermented acorn flours at different levels (0, 15, 30, and 45%). The breads were assessed in terms of their chemical and physical properties, and their staling characterization was also estimated. Results showed that the fermentation of acorn flour before adding it to the formulation affected some chemical properties, and the addition of increasing amounts of acorn flour generally had a positive effect on the chemical composition. Furthermore, the highest protein, total phenolic content, Ca, K, Mg, Mn, and Fe values were obtained from breads, including fermented acorn flour with chickpea yeast (FAC). However, compared to non‐fermented acorn flour (AF), FAC and fermented acorn flour without chickpea yeast (FA) addition caused decreases in weight and volume of breads. Both crust L* and crumb L* values showed a significant reduction with increased acorn addition levels (from 71.88 and 77.22 to 42.26 and 41.15, respectively). The highest initial and final hardness values (T 0 and T ∞ ) were observed with FAC‐added samples, and Avrami exponent n was higher than 1 for only FAC‐added breads. Although fermented acorn addition had positive effects on the nutritional profile of breads, the sensory properties of the samples were negatively affected.
Performance Evaluation of Machine Learning Algorithms for Prediction of Cardiac Failure

Over the past few years, the world is experiencing a drastic rise in deaths caused by Heart Failures, and it is a prevalent and perhaps dangerous medical condition, which has an impact on millions of people globally. The outcomes of patients can be greatly enhanced by early detection and accurate cardiac failure prediction which could reduce healthcare costs. In recent times, artificial intelligence and machine learning algorithms have emerged as powerful tools for predicting heart failure. This paper performs a comparative study and investigation of leading machine learning algorithms for heart failure prediction. This research study presents an explanation, implementation, and comparison on the basis of various evaluation parameters like accuracy, precision, recall, Fl score, etc. The study is performed for multiple regression, KNN, support vector machines, random forest, Naive Bayes, and decision tree models. This study also reviews the different types of factors effecting heart failure prediction, such as chest pain, cholesterol, exercise angina and more. Finally, this study evaluates how well various machine learning algorithms perform on tasks that predict cardiac failure and point up the difficulties and restrictions in this area. The findings of this study demonstrate that Naive Bayes method gives highest accuracy 85.87 % and it is a useful tool in medical practice and are capable of accurately predicting cardiac failure.
Interaction of Wild Type and V804l and V804m-Mutated Ret Protein Kinase with Emodin: In Silico Approach

Medullary thyroid carcinoma (MTC) is a malignant endocrine tumor originating from parafollicular calcitonin-producing C cells. The proto-oncogene RET (REarranged during Transfection) is known as the main actor in the development and outbreak of MTC. Gain of function mutations of RET constitutively activate the receptor which are found to be responsible for the high percentage of MTC cases. The two FDA-approved drugs used for MTC, vandetanib and cabozantinib, are resistant to two mutant variants of RET which are V804L and V804M. In this study, the interactions of emodin, a natural molecule found in plants, with wild-type as well as V804L and V804M-mutated RET kinase were investigated via molecular docking. Pymol was used to create point mutations on wild type RET. Vandetanib and cabozantinib were used as the reference drugs. The binding free energy of vandetanib with wild-type RET, V804L and V804M variants were found to be -9.3, -9.1 and -8.6 kcal/mol. Similarly, the binding free energy of cabozantinib with wild-type RET, V804L and V804M variants were found to be -10.6, -10.4, and -9.5 kcal/mol, respectively. Clearly, the binding affinity of vandetanib and cabozantinib to RET kinase was found to be reduced in mutated variants as compared to wild type. In the meantime, the binding energy between emodin and wild-type RET was shown to be -9.3 kcal/mol. Interestingly, the binding affinity of emodin to V804L and V804M variants was determined to be increased (-9.9 and -9.8 kcal/mol, respectively) compared to wild type. Furthermore, many H-bonds and hydrophobic interactions between emodin and mutated RET variants were shown. Therefore, strong binding affinity of emodin to wild-type and the mutated variants of RET was suggested in this study. In conclusion, emodin was found to be a potential molecule to inhibit RET kinase activity and could be used as a therapeutical agent against medullary thyroid carcinoma.
Unraveling Body Vitals as Traumatic Event-Caused Stress Indicators

Exploration and analysis of changes in human bio-metrics, such as heart rate and blood pressure associated with exposure to traumatic events is the primary goal of this article. We aimed at answering the questions on whether there is a significant difference in biometrics observed in the peaceful and disaster times. Overall, we developed and tested a new technique to measure the difference in the indicators of stress during relatively peaceful times, and during natural and human-made disasters and crises. The proposed approach holds significant potential in the context of e-health and mass migration, offering a valuable tool to recognize and address stress in traumatic events resulting from, for example, forced displacement, armed conflicts, and the impacts of climate change.
Lossy Mode Resonance Sensors Based on Planar Waveguides: Theoretical and Experimental Comparison

Lossy mode resonance (LMR) has garnered significant attention in sensor applications. LMR was primarily explored in fiber-based systems, however, there has been a recent upsurge in its application within planar waveguides. This article compares the LMR phenomenon in planar waveguides with the most employed coatings in the field, specifically SnO2, TiO2, and ITO. Additionally, the experimental findings are compared with simulations conducted using the finite element method (FEM) within the COMSOL Multiphysics environment. The novelty of this research lies in the integration of both experimental results and theoretical calculations, utilizing strong FEM simulation tools, in a single study.
Detection of Disk Filtration Attacks Using Random Forest (RF) Algorithm Comparing with ML Algorithms for Improving Accuracy

The purpose of the research is to compare Random Forest (RF) Algorithm methods to Extra Trees (ET) Classification algorithms for detecting the Disk Filtration attacks in air gapped computers. Materials and Methods: The sample set comprises 609 positive records and 612 negative records were taken for conducting the experiment to compare Random Forest Algorithm with Extra Trees Algorithm for Disk Filtration Attacks. This sample size was obtained using clinical analysis, which had alpha and beta values of 0.05 and 1, an enrollment ratio of 1, pre-test G power of 80%, and 0.5, respectively, with a 95% confidence level. Random Forest and ET classifiers were built using a framework for disk filtration detection. Results: The Disk Filtration attacks on the data set is detected with 99.40% accuracy by the Novel Random Forest classifier,the ET generates 99.20%, in contrast. With a 95% confidence interval, there is a statistically significant difference between the two groups (p=0.002; p0.05). Therefore, RF classifiers outperform ET classifiers. Conclusion: The outcomes demonstrate that RF performs more accurately when compared to ET.
Optimizing Diabetes Prediction: A Comparative Analysis of Ensemble Machine Learning Models with PSO-AdaBoost and ACO-XGBoost

The primary objective of this study is to investigate the early identification of diabetes in order to lessen the health and economic consequences associated with this prevalent chronic condition. One notable research gap in the existing in the existing disease detection model is the restricted applicability of prediction models. The proposed diabetes prediction technique encompasses a data preprocessing procedure to assure the quality of the data. Here, comparative study has been carried out on two ensemble learning strategies, namely Particle Swarm Optimization (PSO) integrated AdaBoost, and Ant Colony Optimization (ACO) integrated XGBoost, for diabetes prediction. The optimization approaches employed in this study optimizes the model parameters in order to improve its predictive capabilities. A thorough evaluation is then undertaken by utilizing a variety of performance measures to assess its overall performance. The results indicate that the PSO-AdaBoost ensemble model outperforms the ACO-XGBoost ensemble model in terms of accuracy, precision, F1-score, and AUC score.
Substance Abuse and Its Association with Adherence to ART Drugs Among HIV-positive Pregnant Women at Selected Hospitals of East Ethiopia

Background: Substance abuse among Human Immune Deficiency Virus-positive patients causes depression, carelessness, and anxiety, which in common results in a decrease in adherence to antiretroviral treatment. The purpose of the study is to assess the prevalence of substance abuse and its association with adherence to ART drugs. Methods: The current study was conducted at three hospitals (Hiwot Fana Comprehensive University Hospital, Dillchora Hospital, and Jugal Hospital) located in East Ethiopia. Bivariate and multivariate logistic regression analysis was used to identify the association of independent variables with the dependent variables. Results: In this study, 119 HIV-positive pregnant patients were included. While 74.8% of the patients were adherent to their medication, the remaining 25.2% were nonadherent. The major reasons for nonadherence to ART medications prescribed were getting better (73.3%) and finishing medication (16.7%). Moreover, 96 (80.7%) respondents abused alcohol while 36 (30.3%) and 75 (35.6%) abused opioids and cigarette smoking, respectively. Mothers who abused alcohol were 38.1 times more likely to be nonadherent to ART medications for PMTCT [AOR = 38.1% CI: 21.47–56.54] compared to their counterparts. Additionally, mothers who abuse opioids were 19 times more likely to be nonadherent to ART treatment medication [AOR = 19, 95% CI: 2.46– 19.46] than their counterparts. Furthermore, mothers who smoked cigarettes were 43.1 more likely to be nonadherent [AOR = 43.19, 95% CI: 8.42–187.84] than nonsmokers. Unemployment was also found to be associated with nonadherence to ART medication to PMTCT of HIV [AOR = 5.4, 95% CI: 2.8–5.7]. Smoking of marijuana/cannabis/hashish was not significantly associated with nonadherence to ART [AOR = 0.22, 95% CI: 0.084–0.355]. Conclusion: Abuse of alcohol, cigarette smoking, abuse of opioids, and joblessness were found to be significantly associated with nonadherence to ART medications prescribed to PMCT of HIV. Wheareas, smoking of marijuana/cannabis/hashish was not significantly associated with nonadherence to ART medications.