Miracle-dz/UniFAR-FTUs huggingface dataset:
Bangla Fake News Detection using Machine Learning, Deep Learning and Transformer Models

News Categorization is one of the primary applications of Text Classification, especially, Fake news classification. In recent days, many researchers have done plenty of work on Fake news detection in rich resource languages like English. But, due to a lack of resources and language processing tools, research on low-resource languages like Bangla is still insignificant. In this study, we try to build a Bangla Fake news dataset combining newly collected fake news data and available secondary datasets. Previously available datasets contained redundant data, which we reduced in our experiment. Finally, we build a Fake news dataset that contains 4678 distinct news data. We experimented with our data with multiple Machine Learning (LR, SVM, KNN, MNB, Adaboost, and DT), Deep Neural Networks (LSTM, BiLSTM, CNN, LSTM-CNN, BiLSTM-CNN), and Transformer (Bangla-BERT, m-BERT) models to attain some state of the art results. The best performing models are CNN, CNN-LSTM, and BiLSTM, with the accuracy of 95.9%, 95.5%, and 95.3%, respectively. We also tested our models by applying the previously existing datasets, and we got a 1.4% to 3.4% improvement in accuracy from previous results. Besides accuracy improvement, our models show a significant increase in recall of fake news data compared to the prior studies.
Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning

Recently, there has been a rapidly emerging demand for localization technologies to provide various location-based services in indoor environments, such as smart buildings, smart factories, and parking lots, as well as outdoor environments. Ultra-wideband (UWB), an emerging wireless technology, provides opportunities for precise indoor positioning with sub-meter accuracy, much more accurate than WiFi or BLE-based techniques, thanks to its signal and communication characteristics. UWB technology has recently begun to be applied to personal devices such as smartphones and is expected to be used for indoor localization of personal devices. However, personal devices often cause signal problems because they are worn on human hands or bodies and move dynamically or are in a non-line-of-sight (NLoS) condition, such as pockets or bags. Therefore, these challenges in the dynamic environment of personal devices must be addressed to enable accurate indoor positioning services based on UWB. In this paper, we propose a novel UWB-based indoor positioning approach that significantly improves localization accuracy under dynamic personal device environments. Our proposed approach detects various NLoS conditions of personal environments by leveraging channel impulse response (CIR) and deep learning. Based on the detected NLoS conditions, the proposed approach adjusts the Kalman filter to adaptively estimate the position of the target UWB-based personal device. Specifically, the distance measurement errors by NLoS are minimized by applying weights determined by deep learning to the Kalman filter. Through experiments conducted in practical indoor environments, we have shown that our proposed approach considerably improves the accuracy compared to the traditional Kalman filter-based and trilateration approaches. According to our method of counting the number of points relative to ground truth, the proposed positioning system improved positioning accuracy significantly by 20.84% to 27.22% with an error tolerance of ±25 cm and by 7.78% to 22.78% with an error tolerance of ±50 cm, compared to the traditional approaches.
Non-equilibrium dissociating flow over spheres

Previous work on the correlation of dissociative non-equilibrium effects on the flow field in front of blunt bodies considered the dependence of the dimensionless shock stand-off distance on the dimensionless dissociation rate immediately after the normal shock in the simple case of a diatomic gas with only one reaction. In this paper, the correlation is corrected to take into account the additional parameter of the dimensionless free-stream kinetic energy, and extended to the case of complex gas mixtures with many species and many reactions, by introducing a new reaction rate parameter that has a clear physical meaning, and leads to an approximate theory for the stand-off distance. Extensive new experimental results and numerical computations of air, nitrogen and carbon dioxide flow over spheres were obtained over a large range of total enthalpy. The results comprise surface heat flux measurements and differential interferograms. Both experimental results and numerical computations substantiate the approximate theory.
Heartbeat evoked potential assessment in moderate and severe obstructive sleep apnea patients

Sleep apnea disorder is defined as more than five apnea events per hour or more than 10 seconds of respiratory airflow ceasing during sleep. Obstructive sleep apnea (OSA) manifests as recurrent attacks of the upper respiratory tract, with an unaltered breathing rhythm. However, it remains unclear if brain responses to cardiac impulses relate with the severity of OSA. In this paper, we aimed to assess heartbeat evoked potentials (HEPs) during non-rapid eye movement (non-REM) and rapid eye movement (REM) sleep stages in moderate and severe OSA patients. The HEP signal is a cerebral reaction to the heartbeat and a measure of brain-body interactions like interoceptive processing and intrinsic arousal levels. We identified significant clusters over the central region (i.e., C4 electrode) only during non-REM sleep when comparing the amplitude of HEPs between moderate and severe OSA patients independently during non-REM and REM periods. These results show that interoceptive processing impairments may be most prevalent during non-REM sleep.
A Design Method of High Parallelism QC-LDPC Decoder Based on FPGA

QC-LDPC(Quasi-Cyclic LDPC) is widely used in deep space communications due to its superior performance. Overlapped-NMSA(Normalization Min-Sum Algorithm) has similar error correction performance to NMSA, and it takes less time to complete an iteration process, so it is more suitable for the design and implementation of high-speed decoder. In this paper, an efficient storage addressing scheme suitable for high parallelism decoder is proposed, and the overall architecture of decoder is designed based on the storage addressing scheme. After the design of the decoder is completed, VIVADO and MATLAB simulation tests show that the decoder can run with a maximum clock frequency of 210MHz and a minimum throughput of about 1.1Gbps.
An Evaluation of Transformer-Based Models in Personal Health Mention Detection

In public health surveillance, the identification of Personal Health Mentions (PHM) is an essential initial step. It involves examining a social media post that mentions an illness and determining whether the context of the post is about an actual person facing the illness or not. When attempting to determine how far a disease has spread, the monitoring of such public posts linked to healthcare is crucial, and numerous datasets have been produced to aid researchers in developing techniques to handle this. Unfortunately, social media posts tend to contain links, emojis, informal phrasing, sarcasm, etc., making them challenging to work with. To handle such issues and detect PHMs directly from social media posts, we propose a few transformer-based models and compare their performances. These models have not undergone a thorough evaluation in this domain, but are known to perform well on other language-related tasks. We trained the models on an imbalanced dataset produced by collecting a large number of public posts from Twitter. The empirical results show that we have achieved state-of-the-art performance on the dataset, with an average F1 score of 94.5% with the RoBERTa-based classifier. The code used in our experiments is publicly available1.
Deep Highlight Removal Using Temporal Dark Prior in High-Speed Domain

This paper proposes a deep highlight removal method based on the dichromatic model under alternating current (AC) light sources with a new diffuse prior on temporal domain, temporal dark prior. An input image is decomposed into specular and diffuse components using a deep network. Due to AC powered lights, both incident and reflected lights are time-varying. We exploit the periodic variation property of the specular and diffuse reflections as a prior for dichromatic model based image decomposition. In addition, we propose a new temporal dark prior as a pseudo-diffuse reflection. Unlike the conventional prior in the spatial domain, to the best of our knowledge, this is the first study to utilize a diffuse prior on the temporal domain for highlight removal. The blurred version of the temporal dark prior is additionally fed to the network to alleviate hole artifacts. It is demonstrated through diverse experiments that these temporal priors can strongly contribute to accurate image decomposition, leading to better highlight removal.
Vulnerability Assessment Method for Electronic Devices Excited by Transient Electromagnetic Disturbances

Vulnerability assessment of electronic devices excited by various transient electromagnetic disturbances (TEDs) has been a key issue in electromagnetic susceptibility research. This paper proposes a generalized vulnerability assessment method that includes acquiring data and building assessment model. The data is normally acquired by carrying out experiments, while the assessment model is mainly determined by prior information. As different devices under test (DUTs) could offer a different amount of prior information, a generalized assessment model consisting of a white-box model, a grey-box model, and a black-box model is built to deal with situations of sufficient prior information, partial prior information, and no prior information, respectively.
Biocryptosystems for Template Protection: A Survey of Fuzzy Vault

Biometric recognition systems provide security to systems by preventing unauthorized access. In recent times, research proved that unprotected biometric systems can be invaded by hackers. A spoofed biometric security can be a great risk as a compromised biometric template can be lost forever. It can also be used to gain unauthorized access into systems. There is the need to protect biometric template from hackers and possible misuse. This study presents various template protection schemes in literature. In addition, this paper reviewed fuzzy vault biocryptosystem template protection scheme by comparing some fuzzy vault schemes in literature based on their False acceptance rates (FAR) and Genuine acceptance rates (GAR) with different degrees of polynomial. The study also reported past studies with the lowest and the highest FAR as 0.00% and 0.87% respectively while the lowest and the highest GAR stand at 72% and 99% respectively.
Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back-end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the-art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.
Human Monkeypox Disease Detection Using Deep Learning and Attention Mechanisms

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease symptoms are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performance in the image-based diagnosis of Covid-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer learning-based methods, along with a convolutional block attention module (CBAM) to focus on the relevant portion of feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep learning models—VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2 along with integrated channel and spatial attention mechanisms and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying monkeypox and other diseases with a validation accuracy of 83.89%.
Biomechanical Analysis of A Mini Force-Closed Cemented Hip Stem for Juvenile Arthritis Patients

Standard implants are available in a wide variety of models and sizes to meet the needs of the normal population. Such implants, however, cannot be used for patients with abnormal hip joint anatomy. This study assessed the mechanical strength and the range of motion of a mini stem, designed for juvenile arthritis patients with substantially small femoral bones. The geometry of the mini stem, designed based on the cemented force-closed philosophy, was determined such that it could accommodate in the very narrow intramedullary canal of a sample patient. A 3D finite element model of the bone-cement-implant construct was developed to assess the strength of the mini stem against yield and fatigue failure, the strength of the cement mantle against yield, and the subsidence of the stem in cement due to the cement creep. Also, the range of motion of the hip implant was estimated by an impingement analysis in which the interference of the femur-pelvis or stem-acetabular cup was examined while rotating the femoral bone in the anatomical planes. Results indicated factors of safety of 7.33 and 4.76 for the mini stem against yield and fatigue failure, and 4 for the cement against yield. The subsidence of the mini stem in the cement over time was obtained as 1.6 micrometers for a 50-hour loading period. Finally, the designed implant could provide large ranges of motion in different planes with no impingement between the implant components and the bones in daily activities. It was concluded that the designed mini stem could satisfy the biomechanical requirements for successful total hip arthroplasty of juvenile arthritis patients.
Seizure Prediction in Epileptic Patients Using EEG and Anomaly Detection

Epilepsy is a neurological disorder in which abnormal brain activity occurs, causing seizures. Seizures may involve convulsions and loss of consciousness and can harm patients and the people around them. Many patients are drug-resistant, and medication does not improve their situation. Predicting the onset of epileptic seizures may improve their quality of life. For this purpose, many studies have utilized EEG signal, which reflects the brain's electrical activity. This paper contains a new seizure prediction method based on Anomaly Detection and with the help of One-Class SVM. The average sensitivity and False Alarms Rate were 94% and 0.89 per hour. The advantage of this method over other methods, such as classification approaches, is that the network needs much less data for training, as only 8 hours of training data have been used for each patient. Low computational complexity and ease of use make it suitable for real-time prediction compared to other studies.
Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis

ABSTRACTFormation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The .
Multi-scale Attention Adaptive Network for Object Detection in Remote Sensing Images

Remote sensing images (RSI) have a large range of variations in the aspect of inter- and intra-class size variability across objects. As a key technology in the field of RSI processing, RSI object detection has been widely applied. Multilevel features fusion network is commonly used to improve the performance of object detection. However, the existing multilevel feature fusion networks for RSI lack the ability to combine global information. Aiming at this problem, A multi-scale attention adaptive network (MA2Net) is proposed to object detection in RSI. The main contributions of this paper are twofold. Firstly, a multi-scale attention adaptive network is designed to adaptively integrate the multilevel features. This network is composed of integrating (IG) block, channel self-attention (CS) block, and adaptive fusion (AF) block. Specifically, IG is designed to transform the multi-level features into an intermediate size. The CS block is an embedded gaussian self-attention module used to model the relationship between the feature channels. AF is developed to learn the multilevel expression of self-attention features to obtain multi-scale feature maps. Secondly, to achieve a balance between multi-task and higher accuracy, a feature align head is utilized to correctly locate and classify objects. The experimental results on DIOR show that our network can achieve higher detection accuracy than the state-of-the-art RSI object detector.
A nonparametric on-line quality control procedure for vectorial observations

We consider an on-line nonparametric quality control procedure for multivariate observations. The goal of the procedure is to rapidly detect an out-of-control situation, that is, to detect a change in sampling distribution after a change point. Each successive observation creates a Voronoi cell indexed by the observation number. Suppose observation n+1 falls into the Voronoi cell with index i, where i is from 1 to n. Then observation n+1 has associated rank i. In the on-target situation these ranks are uniformly distributed but in the off-target situation these ranks tend to be large corresponding to the fact that the later off-target observations tend to clump together because later observations fall according to the off-target distribution. We use these ranks in a Cusum procedure. We found that we can approximately predict the on-target average run length (ARL) of our procedure and get reasonable off-target run lengths for any kind of structural break like a change of mean or a change of dispersion.
Estimation of Gamma Raindrop Size Distribution Parameters: Statistical Fluctuations and Estimation Errors

Abstract The gamma distribution is often used to characterize raindrop size distributions (DSDs). However, the estimation of measured raindrop distributions suffers from the shortcomings of statistical sampling errors, which become increasingly significant when the collecting surface of the measuring instrument and the integration time are small. Different estimators of the three parameters (N0*, μ, and Dm) that characterize a normalized gamma distribution have been computed from simulated DSD. A database has been established, containing 22 950 simulated DSDs, corresponding to a wide set of various rainfall situations. Moment, least squares, and maximum likelihood estimators have been evaluated. Error measurement considerations are discussed, in particular the difficulty encountered in measuring small drops (diameter <0.5 mm) with a disdrometer. Modified estimation approaches are proposed to compensate for the lack of small drops accounted for by real measurements. For each of the different methods, syste.
The Water Drop Rebound Problem: Dynamics of Collision

Abstract A method for studying the dynamics of liquid drops by numerical integration of the Navier-Stokes equations is used to examine the motion of colliding drops, with application to the raindrop problem. Consideration is limited to drops of nearly equal size impacting along their line of centers. The numerical solutions are developed to study the problem of water drop rebound in the presence of air, a situation in which the drops fall to make physical contact, but the results also ]have application to a wider variety of phenomena. The internal motion of the drop, changes in the surface configuration, and the associated energetics of the collision are discussed. The variations with impact speed are presented. Calculations are made for drops of 40 μm to 1 mm diameter. It is demonstrated that except for a small viscous effect the collision characteristics, including the drop shape at the stage of maximum deformation and the time for rebound, suitably normalized, are uniquely determined by the Weber numbe.