Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of use of Artificial Intelligence on Cyber Security and the Fifth-Generation Cyber-attacks and its analysis

Not only have there been a lot more cyberattacks in recent years, but they have also gotten much more advanced. Therefore, developing a cyber-resilient strategy is of utmost significance. In the event of a cyberattack, traditional security measures are insufficient to prevent data leaks. Cybercriminals have mastered the use of cutting-edge methods and powerful tools for data intrusion, hacking, and assault. Fortunately, applications of artificial intelligence (AI) technology the creation of intelligent models for securing systems against attackers. AI technologies can quickly advance to meet complicated problems, making them useful as fundamental cybersecurity tools. In order to identify malware attacks, AI-based systems are capable of providing efficient and robust cyber security against phishing and spam emails, network intrusions, and data breaches capabilities and alert the security during the impact. In this essay, we examine AI's place in cybersecurity and analyze the pertinent literature in terms of its advantages.
An infinite-order integral disturbance observer for improved estimation of periodic disturbances in motion control systems

ABSTRACT In this paper, a disturbance observer is devised for motion control systems suffering unknown periodic disturbances. In the proposed scheme, the structure of a so-called integral disturbance observer is utilized for its error-feedback form, and the repetitive learning control theory is applied to the observer design, constituting an infinite-order integral disturbance observer to obtain an enhanced estimation of a periodic disturbance. A comparative study of different disturbance observers is performed, and experimental results are reported, practically showing the effectiveness of the proposed scheme.
Research on fast semantic segmentation algorithm of road potholes under rain and snow

To meet the speed and accuracy requirements of road semantics segmentation algorithm scenarios, a lightweight semantics segmentation model, MADNet, based on MobileNetV2, is presented, which effectively reduces the computational load of convolution neural network. The feature enhancement module uses a pooled pyramid of empty space convolution. In the deep and shallow part of the MADNet network, attention mechanism is added to compensate for the decline in feature extraction accuracy of MobileNetV2. Finally, the data enhancement algorithm is used to train the identification task in rain and snow weather, road depression and automobile dataset scenarios. The results of ablation test and algorithm comparison test verify that the algorithm proposed in this paper can achieve a better effect and faster speed for road depression and fast semantic segmentation of vehicles in rain and snow weather.
Estimation of Gait Subphase Time Parameters Based on a Human Electrostatic Field Detection System

The parameters of gait phases obtained by detecting gait events can provide a robust basis for the pathological diagnosis and rehabilitation of patients with lower limb disorders. In this article, a method is proposed to divide the inner-stance phase and inner-swing phase by detecting the gait events of heel-off (HO) and swing to the highest position (SHP) in the stepping electrostatic gait signal (EGS) and to estimate the subphase time parameters. The gait events are detected by using the local extreme points of the integrated signal of the EGS. Four subphase temporal parameters are obtained after combining the local extrema of the EGS representing the initial contact (IC) and toe-off (TO) for the division of the inner-stance and inner-swing phases. Compared with the motion capture system (VICON), the detection accuracy of the proposed method reaches 98.5%, and the measurement error is within the range of ±60 ms. Pearson’s coefficient “<inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula>” was used to verify the correlation between the four gait subphases estimated by the two methods, and excellent agreement was obtained (<inline-formula> <tex-math notation="LaTeX">${r} >0.95$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${p} < 0.05$ </tex-math></inline-formula>). Most of the measurement results of the four subphase temporal gait parameters are within the 95% confidence interval of the Bland–Altman analysis chart. The repeatability of the proposed method was calculated through intraclass correlation coefficients (ICC) and showed good test-retest reliability (ICC <inline-formula> <tex-math notation="LaTeX">$>$ </tex-math></inline-formula> 0.85, <inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>). This method detects gait events and estimates subphase duration with high accuracy and can provide a foundation for the gait analysis of EGSs.
Photoacoustic tomography: applications for atherosclerosis imaging

Atherosclerosis is a debilitating condition that increases a patient’s risk for intermittent claudication, limb amputation, myocardial infarction, and stroke, thereby causing approximately 50% of deaths in the western world. Current diagnostic imaging techniques, such as ultrasound, digital subtraction angiography, computed tomography angiography, magnetic resonance angiography, and optical imaging remain suboptimal for detecting development of early stage plaques. This is largely due to the lack of compositional information, penetration depth, and/or clinical efficiency of these traditional imaging techniques. Photoacoustic imaging has emerged as a promising modality that could address some of these limitations to improve the diagnosis and characterization of atherosclerosis-related diseases. Photoacoustic imaging uses near-infrared light to induce acoustic waves, which can be used to recreate compositional images of tissue. Recent developments in photoacoustic techniques show its potential in noninvasively characterizing atherosclerotic plaques deeper than traditional optical imaging approaches. In this review, we discuss the significance and development of atherosclerosis, current and novel clinical diagnostic methods, and recent works that highlight the potential of photoacoustic imaging for both experimental and clinical studies of atherosclerosis.
Novel concept for systematic testing of AI models for MRI acquisition shifts with simulated data

Recently published guidelines recommend the validation of models for medical image processing against variations of image acquisition parameters. This work presents a novel concept for systematic tests that investigate a model’s performance to MRI acquisition shifts in brain scans of multiple sclerosis patients. Contrast changes of T2-weighted FLAIR images related to changes of two scan parameters (TE, TI) are simulated using artificial data. These images were created from 114 brain MRI, normal tissue segmentation masks and expert MS lesion masks (NAMIC, OpenMS, MSSEG2, ISBI15, LC08). Contrast changes were simulated by using the FLAIR signal equation. The experiments evaluate the F1 scores of models trained on images based on different uniform and non-uniform acquisition parameters when tested on typical acquisition parameter shifts. The acquisition parameter variations of the experiments are based on guidelines for quality assurance and the analysis of protocols and images from different institutions and open datasets. The dependence of the F1 score on both parameters could be approximated by linear functions with R2 scores of 0.94 to 0.98. Thus, linear functions can be used to model the dependence of the F1 score on the influencing factors, thereby allowing for the derivation of a "save" range of image acquisition parameters to meet a desired performance metric. The segmentation model was more sensitive to changes in TE compared to TI. The maximum lesion F1 loss when applying the models to out-of-distribution data ranged from 0.04 to 0.36 and was significant in all the experiments, even when the model was trained on data representing scans of different contrast (p<0.01). This underlines the need for testing segmentation models against acquisition shifts.
Performance Analysis of ML-Based MTC Traffic Pattern Predictors

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.
Multimodal face shape detection based on human temperament with hybrid feature fusion and Inception V3 extraction model

ABSTRACT In this research, the multi model (image and video) based face shape detection with human temperament is developed. Here, the video is captured by webcam via live recording session and the proposed model undergoes three major stages namely, pre-processing, extracted feature fusion and temperament detection. Extraction of facial landmark and facial boundary takes place in the pre-processing stage. In the feature extraction stage, the handcrafted features from image are extracted (i.e. face, forehead, eyes, cheeks, nose and mouth). From the video frames, the intrinsic features related to face region are extracted using pretrained Inception V3 model. Then robust principal component analysis (RPCA) is introduced to reduce the dimension of extracted features. Further, the feature fusion process is performed using discriminant correlation analysis (DCA) and canonical correlation analysis (CCA) at hybrid phase. Finally, the gated recurrent unit (GRU) classifier model is applied to identify the human temperaments based on face shapes. In the experimental scenario, the performance measures of accuracy (98.51%, 98.86%), precision (96.14%, 97.89%), recall (96.34%, 97.95%), F-measures (96.24%, 97.94%), etc are evaluated and compared with state-of-the-art methods under two datasets. In addition to this, the statistical test is also conducted to validate the efficacy of the proposed model.
Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients

The severity of the nCOVID infection relies on the presence of Ground Glass Opacities (GGO) present in the patient's chest CT scan images. Although, detecting and delineating the precise boundaries of GGO in the chest CT images is challenging. Here, we proposed a fast and novel technique to automatically segment the regions containing GGO in lung CT images using mathematical morphology. We have tested our algorithm on the chest CT images of 145 Covid-positive cases. This unique segmentation approach correctly segments the lung field from chest CT images and identifies GGO with average sensitivity, specificity, and accuracy of 96.89%, 95.23%, and 97.22%, respectively. We used expert radiologists' hand-curated segmentation of GGO as ground truth for quantificational performance analysis. Our research results indicate that this algorithm performs well found in the literature.
A Martingale Representation for Matching

Matching estimators (Rubin, 1973a, 1977; Rosenbaum, 2002) are widely used in statistical data analysis. However, the large sample distribution of matching estimators has been derived only for particular cases (Abadie and Imbens, 2006). This article establishes a martingale representation for matching estimators. This representation allows the use of martingale limit theorems to derive the large sample distribution of matching estimators. As an illustration of the applicability of the theory, we derive the asymptotic distribution of a matching estimator when matching is carried out without replacement, a result previously unavailable in the literature. In addition, we apply the techniques proposed in this article to derive a correction to the standard error of a sample mean when missing data are imputed using the \hot deck", a matching imputation method widely used in the Current Population Survey (CPS) and other large surveys in the social sciences. We demonstrate the empirical relevance of our methods using two Monte Carlo designs based on actual data sets. In these Monte Carlo exercises the large sample distribution of matching estimators derived in this article provides an accurate approximation to the small sample behavior of these estimators. In addition, our simulations show that standard errors that do not take into account hot deck imputation of missing data may be severely downward biased, while standard errors that incorporate the correction for hot deck imputation perform extremely well.
Dynamic 3D imaging of contrast medium flow on an interventional C-arm using a pulsed injection protocol

This study investigates a method of time resolved 3D (4D) x-ray imaging of contrast dynamics internal to a vascular structure (e.g. intracranial aneurysm) to enable evaluation of blood flow patterns during an interventional procedure. The proposed method employs repetitive-short-pulse injection of small contrast boluses, rotational x-ray imaging with a C-arm, and retrospectively gated iterative image reconstruction. Under conditions where the passage of each contrast pulse through a vascular region is repeatable and the C-arm rotation is slow compared to the injection cycle, each flow state (spatial distribution of contrast agent at an instant) is imaged at multiple projection angles. After partitioning the projections by flow state, a sequence of 3D volumes corresponding to different states of contrast passage can be reconstructed. Feasibility was demonstrated in a patient-specific 3D-printed aneurysm phantom with 1 Hz simulated cardiac flow waveform. A custom-built power injector was programmed to produce repetitive 100ms injections of iodinated contrast agent upstream of the aneurysm, synchronized to the mid-diastolic phase of the simulated cardiac cycle (1 Hz, 0.4 mL/pulse, 20 pulses, 8 mL total). An interventional C-arm short-scan was performed with 11.3 s rotation time and 27fps frame rate. Modified PICCS reconstruction was used to generate the 4D images. The temporal evolution of contrast agent in the 4D x-ray images was visually similar to the flow patterns observed in MRI imaging and CFD simulation of the same phantom. 95% of the surface deviations between the 4D aneurysm volume and traditional 3D-DSA aneurysm volume were within -0.02 ± 0.24 mm.
GRMHD simulations of accretion onto Sgr A*: How important are radiative losses?

We present general relativistic magnetohydrodynamic (GRMHD) numerical simulations of the accretion flow around the supermassive black hole in the Galactic centre, Sagittarius A* (Sgr A*). The simulations include for the first time radiative cooling processes (synchrotron, bremsstrahlung, and inverse Compton) self-consistently in the dynamics, allowing us to test the common simplification of ignoring all cooling losses in the modeling of Sgr A*. We confirm that for Sgr A*, neglecting the cooling losses is a reasonable approximation if the Galactic centre is accreting below ~10^{-8} Msun/yr i.e. Mdot < 10^{-7} Mdot_Edd. But above this limit, we show that radiative losses should be taken into account as significant differences appear in the dynamics and the resulting spectra when comparing simulations with and without cooling. This limit implies that most nearby low-luminosity active galactic nuclei are in the regime where cooling should be taken into account. We further make a parameter study of axisymmetric gas accretion around the supermassive black hole at the Galactic centre. This approach allows us to investigate the physics of gas accretion in general, while confronting our results with the well studied and observed source, Sgr A*, as a test case. We confirm that the nature of the accretion flow and outflow is strongly dependent on the initial geometry of the magnetic field. For example, we find it difficult, even with very high spins, to generate powerful outflows from discs threaded with multiple, separate poloidal field loops.
A New Simple Empirical Formula for Calculating Body Surface Area in Children & Adults

The aim of the study was simply to compare how well this empirical new formula for calculating body surface area compared to five commonly used empirical formulae that calculate BSA m 2 , in children and adults over a wide range of clinical conditions. The DuBois verses HeartSmart (HS) values of BSA m 2 agreement the mean of differences (MOD) DuBois and HS values within the 95% limits of agreement (LOA) was - 0.044964 m 2 to 0.062491m 2 , the BSA values for HS were 349.72 m 2 the values Du Bois were 348.09 m 2 , the actual difference between both calculations was 0.47% m 2 . The Mostellier verses HeartSmart values of BSA m 2 agreement the mean of differences, Mosteller and HS values within the 95% limits of agreement was -0.064211 m 2 to 0.0669 m 2 , the BSA values for HS were 349.97 m 2 the values Mostellier were 349.92 m 2 , the actual difference between both measurements was 0.07% m 2 . The Haycock verses HeartSmart values of BSA m 2 agreement the mean of differences Haycock and HS values within the 95% limits of agreement was -0.064264 m 2 to 0.084264, the BSA values for HS were 351.58 m 2 the values Haycock were 349.72 m 2 , the actual difference between both measurements was 0.53% m 2 . The Gehan &George versesthe HeartSmart values of BSA m 2 agreement the mean of differences. Gehan &George and HS the 95% within the limits of agreement was - 0.055177 m 2 to 0.083241, the BSA values for HS were 351.58 m 2 the values G&G were 349.72 m 2 , the actual difference between both measurements was 0.74% m 2 . The Boyd verses HeartSmart values of BSA m 2 agreement the mean of differences, Boyd and HS the 95% limits of agreement was - 0.055177 m 2 to 0.083241, the BSA values for HS were 349.72 m 2 the values Boyd were 352.99 m 2 , the actual difference between both measurements was 0.97% m 2 . The Paediatrick calculations using HeartSmart values of BSA m 2 agreement the mean of differences. DuBois and Haycock and HS the 95% limits of agreement was -0.013911 m 2 to 0.013539 m 2 , the BSA values for HS were 91.06 m 2 the values Boyd were 91.08 m 2 , the actual difference between both measurements was 0.0001% m 2 . Conclusions: The CCDM-HeartSmart empirical physiological formula (EPF) for calculating body surface in children and adults, are based upon the robust clinical evidence, the mean of the differences (bias) are small between the comparator BSA formulae and that of HeartSmart. The new BSA formula could replace the existing BSA bioexponential formulae, that have not been clinical trialled over a wide range of diverse patient populations (children & adults) or those patients in sepsis - shock, whereas the comparator BSA formulae have not.
A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.
Cascaded H-bridge Converters with Sensorless Voltage Balance through Parallel Branches

Cascaded H-bridge converter (CHBC) enjoys the benefits of modularity and scalability, and is widely applied in photovoltaic power generation, power electronic transformer, static synchronous compensators (STATCOM), and other occasions. However, dc-side capacitor voltage balancing is a technical difficulty in the practical application of CHBC. The traditional software-based balancing methods will cause heavy use of the dc-side voltage sensors and high computational burden on balancing algorithm. In this paper, an effective hardware-based balancing method is proposed to realize capacitor voltages of sub-modules balance by adding balancing arms between sub-modules of CHBC to build capacitors parallel branches. The proposed balancing method balances the capacitor voltages in a voltage sensorless fashion, which translates into the removal of a large number of voltage sensors and great simplification of the control system. In addition, the existence of the parallel branches enables capacitors precharging in a simple way. Finally, the simulation results verify the effectiveness of the proposed balancing strategy in capacitor voltage balancing of CHBCs as a STATCOM.
Computing in Algebraic Closures of Finite Fields

We present algorithms to construct and perform computations in algebraic closures of finite fields. Inspired by algorithms for constructing irreducible polynomials, our approach for constructing closures consists of two phases; First, extension towers of prime power degree are built, and then they are glued together using composita techniques. To be able to move elements around in the closure we give efficient algorithms for computing isomorphisms and embeddings. In most cases, our algorithms which are based on polynomial arithmetic, rather than linear algebra, have quasi-linear complexity.
Stock Market Prediction Approach: An Analysis

Data mining is a method that can glean meaningful information from a wealth of data. The data mining method known as prediction analysis uses current data to forecast potential future outcomes. Pre-processing the image, to extract the features, and classify the image are just a few of the operations used in the prediction analysis methodology. The stock market forecast is the foundation of this review essay. This study reviews a number of classification-based stock market prediction systems. Python is used to put the stock market forecasting concepts into practice.