Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive COVID-19 Fake News Detection Model based on Machine Learning Approach

In today's era of information explosion, when information and knowledge are transmitted through social platforms, people often misbelieve wrong information or false information maliciously created, causing varying degrees of impact on society. This process is called "Infodemic". The term "information epidemic" first appeared during the SARS epidemic in 2003. False information spread rapidly and massively around the world through various communication channels, causing national security, economy, and politics to be affected. Therefore, this research applies the latent dirichlet allocation (LDA) method into the topic model, combined with TF and TF-IDF for COVID-19 fake news detection comparison. As the result of five classification models comparison - SVM, random forest, XGBoost and AdaBoost, the LDA combined with TF-IDF features can improve both SVM and random forest models of F1-score, among which the SVM model has the most significant improvement effect. After 10-fold cross-validation, the average F1-score growth rate of SVM increased by 1.13%, the accuracy was 98.04%, and the F1-score reached 98.10%.
3D Shear‐Wave Velocity Model of Central Makran Using Ambient‐Noise Adjoint Tomography

The Makran subduction zone is unique in its wide onshore thick accretionary prism, and volcanic arc not parallel to the E–W trend of the Makran accretionary prism. To investigate the internal structure of the accretionary prism, the crustal nature of Jaz Murian Depression, and the trend of the subducting plate hinge, we have calculated a 3D shear‐wave velocity model for a region around the border between eastern and western Makran using ambient‐noise adjoint tomography and data from IASBS/CAM Makran temporary seismic network. In close agreement with previous works, our velocity model shows that the onshore accretionary prism consists of a low‐velocity zone in the south and a high‐velocity zone in the north with an average thickness of accreted sediments of 22 and 30 km, respectively. The young age of the surface rocks of the high‐velocity part of the prism suggests the presence of a significant volume of igneous rocks and metamorphosed sedimentary rocks. The velocity model indicates a continental crust of ∼40 km with a thick sedimentary cover of ∼20 km for the eastern part of Jaz Murian Depression. The presence of a NE‐SW trending low‐velocity region at a depth interval of 40–60 km subparallel with the trend of the volcanic arc, intermediate‐depth earthquakes, and geometry of the overriding plate might be related to the trend of the subducting plate hinge. This implies that the observed NE‐SW trending volcanic arc might be related to the geometry of the subducting plate hinge and not the eastward reduction of the subduction angle.
Psychogenic Nonepileptic Seizures Associated with an Eating Disorder and PTSD Are Responsive to Cognitive Processing Therapy

Objective Eating disorders (EDs) are often associated with prior histories of trauma, subsequent PTSD and related psychiatric comorbidities. There is a paucity of information about their relationship to somatic symptom disorders, specifically psychogenic nonepileptic seizures (PNES), a type of functional neurological symptom disorder or conversion disorder. Methods We report a case of a 39-year-old bisexual female with bulimia nervosa (BN), PTSD, recurrent major depressive disorder (MDD), cannabis use disorder, and PNES who responded to integrated trauma-focused treatment during residential ED treatment using cognitive processing therapy (CPT). Symptoms of ED, PTSD, major depression, and state-trait anxiety were measured using validated assessment instruments. Results During the course of CPT treatment, the patient's total scores on the PTSD Symptom Checklist for DSM-5 (PCL-5) went from 59 to 26, which is below the diagnostic threshold for PTSD. In addition, she demonstrated improvements in the Eating Disorder Examination Questionnaire (EDE-Q) Global Severity score, the Eating Disorder Inventory (EDI-2) total score, the Patient Health Questionnaire (PHQ-9) total score, the Spielberger State and Trait Anxiety Inventory scores, and the Eating Disorder Quality of Life (EDQOL) total score. Furthermore, her PNES also abated, and she remained seizure free for ∼1 year following discharge with the exception of one short seizure, per report of the patient. Conclusion The use of CPT as part of an integrated trauma-informed treatment approach during residential ED treatment was successful in a woman with PNES, BN, PTSD, MDD, and cannabis use disorder.
Deep Q-Network-Based Open-Set Intrusion Detection Solution for Industrial Internet of Things

Industrial Internet of Things (IIoT) has brought a lot of convenience for the industrial world to digitization, automation, and intelligence, but it inevitably introduces inherent cyber security risks, resulting in an issue that traditional intrusion detection techniques are no longer sufficient for IIoT environments. To solve this issue, we propose an open-set solution called a DC-intrusion detection system (IDS) for IIoT based on deep reinforcement learning. In this solution, the open-set recognition problem in intrusion detection is modeled as a discrete-time Markov decision process, and a deep $Q$ -network (DQN) is employed to solve it. Meanwhile, a conditional variational autoencoder is introduced to the value network in DQN. Therefore, the open-set recognition problem in intrusion detection is divided into two subproblems, namely, known traffic fine-grained classification problem and unknown attacks recognition problem. We use DQN to solve the known traffic fine-grained classification problem. Since the reconstruction error of known traffic is generally smaller than the reconstruction error of unknown attacks, we use reconstruction error to recognize unknown attacks. Experiments on the IIoT data set TON-IoT demonstrate the effectiveness of the DC-IDS model, which achieves better performance in terms of the recognition rate of unknown attacks as well as the stability of the model compared to previous proposed methods.
Relevance of Quadrature Phase For Replay Detection in Voice Assistants (VAs)

There have been various studies involving Instantaneous Frequency (IF) estimation for Spoofed Speech Detection (SSD) task, such as the derivative of the phase obtained by Hilbert Transform (HT) approach and Energy Separation-based method. However, IF estimation by HT leads to lack of good temporal resolution. On the other hand, ESA-based method leads to excellent time resolution, however, lacks the relative phase information. Therefore, in this paper, we have proposed Cochlear Filter Cepstral Coefficients-based Instantaneous Frequency using Quadrature Energy Separation Algorithm (CFCCIF-QESA) feature set, which merits of having an excellent time resolution as well as inclusion of the relative phase information. Hence, we illustrate the significance of incorporating the quadrature-phase component along with the in-phase component for SSD of replay detection in VAs. To that effect, we perform experiments on the Realistic Replay AttackF Microphone-Array Speech Corpus (ReMASC) dataset. Furthermore, the proposed CFCCIF-QESA feature set gives 28.71 and 29.89 %EER using GMM and CNN respectively, on Eval set. The proposed feature set is evaluated using various performance metrics, including EER and other confusion matrix-based metrics. Finally, the latency for CFCCIF-QESA and CFCCIF-ESA is presented, showing the better suitability of the proposed CFCCIF-QESA feature set w.r. t. practical deployment.
Blockchain Technology Enabled Traceability Framework: Food Supply Chain Perspective

In the global market, consumers have expectations of good quality food products. To provide flawless food products, traceability has become dominant in a complex food supply chain (SC) network. Stakeholders of the food SC hesitate to share their data with each other, as they have different business strategies. Blockchain Technology (BCT) brings all the parties of food SC on single platform to improve interorganizational trust. The implementation of BCT in food SC offers the traceability of products. This paper proposes a framework for traceability of products in food SC using BCT that offers the consumers to trace the origin of the food products. Blockchain enhances the food traceability, minimize the data tampering, and improves data transparency in food SC. BCT enabled food SC framework has been tested using case study of rice SC and found the enhancement in traceability.
Implementation of an Edge-Computing Vision System on Reduced-Board Computers Embedded in UAVs for Intelligent Traffic Management

Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). UAVs, owing to their aerial perspective, offer a more effective means of detecting vehicles involved in these maneuvers. The primary objective is to develop, evaluate, and compare different computer vision models and reduced-board (and small-power) hardware for optimizing traffic management in these scenarios. A dataset was constructed using two sources, several models (YOLO 5 and 8, DETR, and EfficientDetLite) were selected and trained, four reduced-board computers were chosen (Raspberry Pi 3B+ and 4, Jetson Nano, and Google Coral), and the models were tested on these boards for edge computing in UAVs. The experiments considered training times (with the dataset and its optimized version), model metrics were obtained, inference frames per second (FPS) were measured, and energy consumption was quantified. After the experiments, it was observed that the combination that best suits our use case is the YoloV8 model with the Jetson Nano. On the other hand, a combination with much higher inference speed but lower accuracy involves the EfficientDetLite models with the Google Coral board.
Constraining the source location of the 30 May 2015 (Mw 7.9) Bonin deep‐focus earthquake using seismogram envelopes of high‐frequency P waveforms: Occurrence of deep‐focus earthquake at the bottom of a subducting slab

In this study, the source location of the 30 May 2015 (Mw 7.9) deep‐focus Bonin earthquake was constrained using P wave seismograms recorded across Japan. We focus on propagation characteristics of high‐frequency P wave. Deep‐focus intraslab earthquakes typically show spindle‐shaped seismogram envelopes with peak delays of several seconds and subsequent long‐duration coda waves; however, both the main shock and aftershock of the 2015 Bonin event exhibited pulse‐like P wave propagations with high apparent velocities (~12.2 km/s). Such P wave propagation features were reproduced by finite‐difference method simulations of seismic wave propagation in the case of slab‐bottom source. The pulse‐like P wave seismogram envelopes observed from the 2015 Bonin earthquake show that its source was located at the bottom of the Pacific slab at a depth of ~680 km, rather than within its middle or upper regions.
HIERARCHY OF NUMERICAL SCHEMES FOR THE BIE SOLUTION IN 2D FLOW SIMULATION USING VORTEX METHODS

The problem of 2D incompressible flow simulation around airfoils with sharp edges and corner points is considered. The solution of the boundary integral equation with respect to vortex sheet intensity, arising in Lagrangian vortex method, has weak singularity that cannot be resolved correctly in the framework of the existing Galerkin-type numerical schemes. Known numerical schemes with piecewise-constant or piecewise-linear numerical solution representation provide solution reconstruction with high quality and the second order of accuracy only for piecewise-smooth bounded solutions. For singular solutions their order of accuracy goes down to the first. It is shown that wrong behaviour of numerical solution takes place only on the panels that adjust to the corner point. Modified numerical scheme is developed that is based on the Galerkin–Petrov approach and allows us to obtain integral characteristics of solution (the components of the added masses tensor) with the second order of accuracy. The scheme can be easily implemented in codes developed for flow simulations using the vortex particle method.
Fundamentals of low-cost aerosol sensor design and operation

Abstract Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor’s internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols. We developed a physics-based model of particle light scattering within the sensor, which we used to predict counting and sizing efficiency for 0.30 to 10 µm particles. We found that the PMS5003 counts single particle scattering events, acting like an imperfect optical particle counter, rather than a nephelometer. As particle flow is not focused into the core of the laser beam, >99% of particles that flow through the PMS5003 miss the laser, and those that intercept the laser usually miss the focal point and are subsequently undersized, resulting in erroneous size distribution data. Our model predictions of PMS5003 response to varying particle diameters, aerosol compositions, and relative humidity were consistent with laboratory data. Computational fluid dynamics simulations of the PurpleAir monitor housing showed that for wind-speeds less than 3 m s−1, fine and coarse particles were representatively aspired to the PMS5003 inlet. Our measurements and models explain why the PurpleAir overstates regulatory PM2.5 in some locations but not others; why the PurpleAir PM10 is unresponsive to windblown dust; and why it reports a similar particle size distribution for coarse particles as it does for smoke and ambient background aerosol. Copyright © 2023 American Association for Aerosol Research
BIG-STEP: Better-Initialized State Estimator for Legged Robots with Fast and Robust Ground Segmentation

Legged robots are crucial in various applications, from search and rescue operations to exploration missions in challenging terrain. Accurate estimation of the robot's state is paramount for achieving precise and reliable navigation. However, estimating the state of legged robots presents unique challenges due to inherent uncertainties, dynamics, and environmental interactions. We propose a novel state estimator for legged robots that leverages the ground plane to mitigate errors in the z-component of the state estimate especially. By exploiting the information provided by the effectively estimated ground plane, which serves as a reliable reference, the proposed estimator compensates for errors and enhances the accuracy of the estimated state. To validate the effectiveness of the proposed state estimator, real-world experiments are conducted on a legged robot platform. The results demonstrate significant improvements in state estimation accuracy, particularly in the z-component when compared with conventional state estimation methods. The proposed state estimator can potentially enhance the performance and autonomy of legged robots in various applications, including locomotion control, terrain mapping, and environment perception. Furthermore, its robustness and accuracy make it well-suited for scenarios where precise state estimation is crucial for safe and effective operation.
Long-Term Investigation of Marine-Derived Aspergillus Diversity in the Republic of Korea

Abstract Aspergillus species play a crucial role in terrestrial environments as degraders and are well known for producing various secondary metabolites. Recently, Aspergillus species have been discovered in marine environments, exhibiting adaptability to high salinity and producing diverse secondary metabolites with valuable properties. However, limited research has focused on their marine diversity, leading to inaccurate species identification. The current study addresses this gap by investigating diverse marine habitats in the Republic of Korea, including sediment, seawater, seaweed, and marine animals. From three coasts of the Korean Peninsula, 472 Aspergillus strains were isolated from the various marine habitats. A total of 41 species were accurately identified using multigenetic markers: internal transcribed spacer, calmodulin, and β-tubulin. The findings underscore the importance of accurate identification and provide a basis for elucidating the functional role of marine-derived Aspergillus species in marine ecosystems.
A Diagnostic Study of the Indian Ocean Dipole Mode in El Niño and Non–El Niño Years

Abstract The Indian Ocean dipole mode (IODM) is examined by comparing the characteristics of oceanic and atmospheric circulations, heat budgets, and possible mechanisms of IODM between El Nino and non–El Nino years. Forty-year ECMWF Re-Analysis (ERA-40) data, Reynolds SST data, and ocean assimilation data from the Modular Ocean Model are used to form composites of the IODM that occur during El Nino (1972, 1982, and 1997) and non–El Nino (1961, 1967, and 1994) years. In El Nino years, two off-equatorial, anticyclonic circulations develop, associated with the increased pressure over the eastern Indian Ocean. The anticyclonic circulation over the Northern Hemisphere enhances the easterly component of the winds in the northwestern Indian Ocean. This enhanced easterly component increases the mixed layer temperature by inducing an anomalous westward ocean current that advects the warm mean mixed layer from the central to the western Indian Ocean. Meanwhile, the anticyclonic circulation over the southeastern Ind.
A Doppler Compensation Method for MIMO-OFDM Systems on Fading Channels

The paper proposes a Doppler compensation method for the MIMO-OFDM systems on fading channels. To deploy this method, we first propose a strategy to arrange the pilot symbols in the spatial domain, so that the orthogonality of subcarriers is maintained. Then, we develop a time synchronization method in the time domain, which uses both the correlation and the mean squared error function of the guard interval to improve the time synchronization performance. Finally, a novel Doppler compensation method is implemented in the frequency domain by using a non-uniform fast Fourier transform (FFT) matrix. The numerical simulations show that the proposed architecture provides a significant improvement in terms of system performance compared to the state of the art. At the same level of BER of $10^{-4}$, our system mechanism gains SNR of 2 dB in the case of using the FEC coding scheme. Without FEC coding, the gain of the SNR is above 1 dB in the same level of BER mentioned above.
Modified Genetic Algorithm with Flexible Crossover for the Capacitated Electric Vehicle Routing Problem

This paper proposed a modification of genetic algorithm with a new technique called the flexible crossover operation for solving the capacitated electric vehicle routing problem (CEVRP). The framework of this paper is based on the concept of the classic genetic algorithm (GA). To improve the performance of the genetic algorithm, two aspects have been proposed, 1) The Pre-Post mutation is present to obtain the ability of local search capacities. 2) The flexible crossover operation is proposed to enhance the diverse capabilities. The proposed algorithm has been evaluated and compared to the state-of-the-art algorithms. The experimental result on seven data sets demonstrated that the proposed algorithm is effective for solving a small and medium data set of capacitated electric vehicle routing problem.
Cross-Subject Classification of Spoken Mandarin Vowels and Tones with EEG Signals: A Study of End-to-End CNN with Fine-Tuning

Direct speech brain-computer interface (DS-BCI) is an ideal way for speech communication by decoding signals collected from the brain. Electroencephalogram (EEG) has gained widespread use in DS-BCI studies due to its simplicity of operation and high temporal resolution. However, as human brain exhibits considerable inter-individual variability, classification models trained on the basis of data from one subject may not generalise well to other individuals, which is a major challenge in existing EEG signal classification studies. In this paper, the cross-subject classification performance of spoken Mandarin speech with EEG signals was investigated by using an end-to-end convolutional neural network (CNN) model pretrained on the source data and fine-tuned on the target data. The raw EEG signals were directly used as the input to the model without using extracted features. In addition, adding Gaussian noise was used as the data augmentation method in order to deal with the unbalanced dataset. The proposed method was tested on a collected EEG dataset of spoken Mandarin speech, including vowel classification and tone classification tasks. The average classification accuracies of four vowels and four tones were 63.1% and 51.7% respectively. The average accuracy of tone classification was significantly improved compared with the machine learning and subject-dependent methods. The results of this work showed the potential of the fine-tuning based CNN model in the cross-subject studies of EEG decoding.
X2Ka Translation Network: Mitigating Ka-Band PolSAR Data Insufficiency via Neural Style Transfer

Data insufficiency poses a significant challenge in Ka-band polarimetric synthetic aperture radar (PolSAR) applications. Traditional PolSAR simulation approaches fail to conquer this issue due to the intricate modeling and computational complexities induced by high-frequency. In this article, the authors propose to mitigate this issue through neural style transfer (NST). An X2Ka translation network is proposed to transfer X-band PolSAR images to Ka-band. Leveraging the well-verified generative network Pix2Pix, the authors adapt it to accommodate the specific discrepancies between PolSAR and optical data. Experiments are conducted on X- and Ka-bands PolSAR images acquired by an Airborne PolSAR system from the Chinese Academy of Sciences. Both qualitative and quantitative evaluation results demonstrate the effectiveness of the proposed network.
Methane reduction from ruminant microbiota of sheep using polyphenols extract of different olive mill wastewater

Olive oil production generates considerable quantities of olive mill wastewater, a powerful pollutant dumped in nature without any prior treatment. Olive mill wastewater is considered as a potential source of natural products of high additive value, due to their content of phenolic compounds and other natural antioxidants. This study aims to investigate the impact of phenolic substances, extracted from different olive mill wastewater of different olive varieties, on the composition of the ruminal microbiota of sheep. The results of the quantification of phenolic compounds show the richness of the three varieties (Sigoise, Azzeradj and Chemlal) in polyphenols with respective values of 26.3, 23.97 and 20.09 g/L. Moreover, a stimulation in the fermentative activity was reported, which caused a significant reduction in methane production (24 hours of incubation) of 7.31, 39.36 and 30.06% for Sigoise, Azzeradj and Chemlal, respectively. In addition, this decrease creates a reduction in the production of ammonia and the number of protozoa.