Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative study of machine learning algorithms for face recognition

Background: The fundamental need for authentication and identification of humans using their physiological, behavioral or biological characteristics, continues to be applied extensively to secure localities, property, financial transactions, etc. Biometric systems based on face characteristics, continue to attract the attention of researchers, major public and private services. In the literature, many methods have been deployed by different authors. The best performance must be found in order to be able to recommend the most effective method. So, the main objective of thisarticle is to make a comparative study of different existing techniques.Methods: A biometric system is generally composed of four stages: acquisition of facial images, preprocessing, extraction of characteristics and finally classification. In this work, the focus is on machine learning algorithms for classification. These algorithms are: Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), Naive Bayesian Classification (NB: Naive Bayes’ Classifiers) and deep learning techniques such as Convolutional Neural Networks (CNN). The comparison criterion is the average performance, calculated using three performance measures: recognition rate, confusion matrix, and the Area Under Receiver Operating Characteristic (ROC) curve.Results: Based on this criterion, the performance comparison of selected machine learning algorithms, shows that CNN is the best, with an average performance of 100.00% On ORL face database. However, on the YALE database, classical algorithms such as artificial neural networks have obtained the best performances, the highest being a rate of 100%. Discussion: Deep learning techniques are very efficient in image classification as proven by the results on the ORL database. However, their inefficiency on YALE face database is due to the small size of this database which is inappropriate for some deep learning algorithms. But this weakness can be corrected by image augmentation techniques. The comparison of these results with existing state-of-the-art methods is nearly the same. Authors achieved performances of 94.82%, 95.79%, 96.15%, 96.44%, 97.27%, 98.52% and 98.95% for NB, KNN, RF, LR, ANN, SVM and CNN classifiers, respectively. Finally, in depth discussion, it is concluded that between all these approaches which are useful in face recognition, the CNN is the best classification algorithm.
Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input–output datasets (using voltage–current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally.
The effects of football juggling learning on executive function and brain functional connectivity

This study aimed to explore the relationship between motor skill learning and executive function (EF), with an emphasis on the potential effects of football juggling learning. A randomized controlled trial involving 111 participants aged 17–19 years was conducted. Participants were randomly assigned to either the football juggling learning (FJL) group or a control group. The FJL group underwent 70 sessions of football juggling learning, while the control group engaged in their normal daily activities without any exercise intervention during the same time frame. Both groups were assessed for EF performance and underwent functional magnetic resonance imaging (fMRI) scans before and after the experiment. The executive function test included three tasks, namely, inhibition, working memory, and shifting. The results showed significant improvement in inhibition and shifting in both groups, and the FJL group showed greater improvement in these aspects of EF compared to the control group. Additionally, in comparison to the control group, the FJL group exhibited increased functional connectivity within the frontal, temporal, and cerebellar regions from the pre-test to the post-test. Notably, enhanced functional connectivity between the right superior temporal gyrus (posterior division) and left cerebellum 6 was identified in the FJL group and was associated with improved EF performance induced by football juggling learning. These findings shed light on the potential causal relationship between motor skill learning, EF, and brain plasticity. Importantly, our study provides preliminary evidence supporting the use of motor skill learning, such as football juggling, as a potential avenue for cognitive enhancement.
Drift Adaptive Online DDoS Attack Detection Framework for IoT System

Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.
Effect of gamete aging on fertilization success of the sea urchin Arbacia dufresnii (Arbacioida: Arbaciidae)

Introduction: Short-term gametes storage is an inexpensive and simple technique that allows the use of the same batch of eggs or sperm at different times, maximizing the application of research protocols and the use of gametes in production. Arbacia dufresnii is a sea urchin species with proven aquaculture potential and already used in the nutraceutical industry. Aging of its gametes is unknown and is a needed information to scale up the production. Objective: Determine the effect of male and female gamete aging on the fertilization success of Arbacia dufresnii. This will allow optimizing the use of gametes after collection decoupling spawning from fertilization. Methods: A. dufresnii individuals were induced to spawn and gametes were kept at 12 ± 1 °C throughout each bioassay. Sperm was separated into two treatments: activated sperm in seawater (AS), and dry sperm (DS). Two bioassays were made: Bioassay 1 evaluated the effect of time on fertility by performing fertilization tests at 0 h, 24 h, 48 h, 72 h, and 96 h after spawning. Bioassay 2 evaluated the contribution of each type of aged gamete on fertility, combining aged gametes (96 h) with fresh gametes (0 h). Results: Bioassay 1: the fertilization success obtained by combining eggs (E) with AS or DS presented important differences. While the fertilization success remained acceptable (greater than 50 %) for up to 72 h using ExDS, it only remained acceptable for up to 48 h using ExAS. Bioassay 2: acceptable fertilization success was found by combining aged E (96 h) with fresh sperm, or aged DS (96 h) with fresh E, but not using aged AS with fresh E. Conclusions: The findings of this work show that fertilization success in A. dufresnii gametes remains relatively unchanged for up to 48 h after spawning when combining ExAS, and for up to 72 h when combining ExDS. However, when combining aged E or aged DS with a fresh gamete, post-collection fertilization can be extended up to 96 h. In this work, the first steps have been taken to understand the conservation time of A. dufresnii gametes with minimum intervention.
Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition

The presence of outliers in the training data affects the accuracy of the constructed model. To cope with the outlier interference in the model construction process, some robust methods have been proposed on the basis of the nonparametric method, Gaussian process regression (GPR), without eliminating the outliers previously. However, the high complexity of these robust GPR methods makes them unable to cope with situations where the amount of data is too large. In this article, we analyze the impact of outliers on model construction in the setting of big data and propose a robust version based on the sparse GPR. Empirical evaluations conducted on two publicly available datasets, as well as a nitrogen oxides soft sensor designed for a physical diesel engine whose data exist outliers that are difficult to distinguish from normal data, provide compelling evidence to support the notion that the proposed method leads to significant enhancements in performance.
Distribution of actual evapotranspiration over Yinchuan Plain, an arid area in China

Evapotranspiration (ET) is a major loss flux of the water balance in arid and semi-arid areas. The estimation of actual evapotranspiration in regional scale has significance for environmental and hydrological purposes. Spatial distribution of evapotranspiration and its relationship with vegetation, water table depth in the arid Yinchuan plain of China was studied by combining remote sensing data with groundwater depth observation. The vegetation cover was quantified with the NDVI, ET was estimated with the Surface Energy Balance System (SEBS), and the water table depths were measured in monitoring wells. There were 3 cloud-free and atmospherically corrected MODIS satellite images in July 2004 used in the SEBS algorithm to determine the actural evapotranspiration. 520 groundwater observations in the same season were applied to analyze the relationship between evapotranspiration and water table depth. The result showed that the evaporation from the farmland is at the value of 3.9 mm per day and the minimum value of 2 mm is for the bare soil. The evapotranspiration of shrub and grassland is in average value of 2.5 mm per day and 3.2 mm is for the farmland in the plain. The plain is covered by vegetation when the NDVI of the area is larger than 0.2 and that water table depth is an important factor influencing the ET in the arid region: the deeper the groundwater, the less the ET. The threshold depth of groundwater evaporation is 6 m with vegetation cover in
Analysing the Hidden Relationship between Long-Distance Transport and Information and Communication Technology Use through a Fuzzy Clustering Eco-Extended Apostle Model

The study analyses the hidden relationship between transport and ICT use for an extensive sample of 26,500 EU citizens. To that aim, a fuzzy clustering Eco-extended apostle model is applied to both latent variables: interurban transport trips and ICT use. The interurban long-distance trip (LDT) latent variable is measured by four different indicators (long- and medium-distance trips for work and leisure in the past twelve months), and the ICT use is based on a ten-item scale that provides information on different transport modes. The fuzzy Eco-extended apostle model is compared with the classical apostle model, translating the satisfaction and loyalty dimensions to our case. The fuzzy clustering model shows that most EU citizens are similar to the representative citizen who moved and used ICT at very low rates (56.5 and 50.4 per cent, respectively). The classical apostle model shows that the quadrants low LDT–high ICT and low LDT–low ICT are more represented by 38.5 and 35.2 per cent, respectively. However, the Eco-extended apostle model reinforces the results of the quadrant of low LDT–low ICT (40.22%) but softens those obtained in the quadrant of low LDT–high ICT (21.01%). Interesting insights of the effects of gender, age, education, and employment status are discussed.
Experiences of Foster Parents and Fostered Adolescent Females’ Sexual Behavior in Eastern Cape, South Africa

ABSTRACT The purpose of the study was to investigate the experiences of four foster parents and sexual behavior of four fostered adolescent females (aged 15-18), who were purposively sampled from the Department of Social Development, Alice, Eastern Cape, South Africa. This paper is underpinned by social exchange theory pioneered by Emmerson, which posits that adolescent females may be linked to risky sexual behavior and exchange sex for money with older men owing to poverty and poor parent-child communication. This study followed qualitative approach in collecting data by means of focus group interviews. Findings from this study indicate that foster parents are financially and emotionally abusive and contributing towards adolescent females’ alcohol abuse and sexual promiscuity. The recommendations are that the government should ensure that foster parents do not to contravene the South African Children's Act 38 of 2005 section 181, which advocates for nurturance of every child living in foster care.
A Hierarchical unsupervised method for power line classification from airborne LiDAR data

ABSTRACT The automatic classification of power lines from airborne light detection and ranging (LiDAR) data is a crucial task for power supply management. The methods for power line classification can be either supervised or unsupervised. Supervised methods might achieve high accuracy for small areas, but it is time consuming to collect training data over areas of different conditions and complexity. Therefore, unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand. In this paper, we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points (including the power line corridor direction detection based on a layered Hough transform, connectivity analysis, and Douglas–Peucker simplification algorithm), followed by the extraction of contextual linear and angular features for each candidate laser points, and finally by setting the feature threshold values to identify the power line points. We tested the method over both forest and urban areas and found that the precision, recall and quality rates were up to 96.7%, 88.8% and 78.3%, respectively, for the test datasets and were higher than the ones from a previously developed supervised classification method. Overall, our approach has the advantages of achieving relatively high accuracy and being relatively fast.
Silver Nanoparticles Selectively Treat Neurofibromatosis Type 1-Associated Plexiform Neurofibroma Cells at Doses That Do Not Affect Patient-Matched Schwann Cells

Neurofibromatosis Type 1 (NF1) is a common neurogenic condition characterized by heterozygous loss of function mutations in the neurofibromin gene. NF1 patients are susceptible to the development of neurofibromas, including plexiform neurofibromas (pNFs), which occurs in about half of all cases. Plexiform neurofibroma are benign peripheral nerve sheath tumors originating from Schwann cells after complete loss of neurofibromin; they can be debilitating and also transform into deadly malignant peripheral nerve sheath tumors (MPNSTs). Here, our data indicates that silver nanoparticles (AgNPs) may be useful in the treatment of pNFs. We assessed the cytotoxicity of AgNPs using pNF cells and Schwann cells derived from the same NF1 patient. We found that AgNPs are selectively cytotoxic to pNF cells relative to isogenic Schwann cells. We then examined the role of neurofibromin expression on AgNP-mediated cytotoxicity; restoration of neurofibromin expression in pNF cells decreased sensitivity to AgNP, and knockdown of neurofibromin in isogenic Schwann cells increased sensitivity to AgNP, outlining a correlation between neurofibromin expression and AgNP-mediated cytotoxicity. AgNP was able to selectively remove pNF cells from a co-culture with patient-matched Schwann cells. Therefore, AgNPs represent a new approach for clinical management of NF1-associated pNF to address significant clinical need.
Bringing Age Back In: Accounting for Population Age Distribution in Forecasting Migration

The link between age and migration propensity is long established, but existing models of country-level net migration ignore the effect of population age distribution on past and projected migration rates. We propose a method to estimate and forecast international net migration rates for the 200 most populous countries, taking account of changes in population age structure. We use age-standardized estimates of country-level net migration rates and in-migration rates over quinquennial periods from 1990 through 2020 to decompose past net migration rates into in-migration rates and out-migration rates. We then recalculate historic migration rates on a scale that removes the influence of the population age distribution. This is done by scaling past and projected migration rates in terms of a reference population and period. We show that this can be done very simply, using a quantity we call the migration age structure index (MASI). We use a Bayesian hierarchical model to generate joint probabilistic forecasts of total and age- and sex- specific net migration rates over five-year periods for all countries from 2020 through 2100. We find that accounting for population age structure in historic and forecast net migration rates leads to narrower prediction intervals by the end of the century for most countries. Also, applying a Rogers&Castro-like migration age schedule to migration outflows reduces uncertainty in population pyramid forecasts. Finally, accounting for population age structure leads to less out-migration among countries with rapidly aging populations that are forecast to contract most rapidly by the end of the century. This leads to less drastic population declines than are forecast without accounting for population age structure.
Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition with Completeness Analysis

With the contemporary digital technology approaching, deep neural networks are emerging as the foundational algorithm of the artificial intelligence boom. Whereas, the evolving social demands have been emphasizing the necessity of novel methodologies to substitute traditional neural networks. Concurrently, the advent of the post-Moore era has spurred the development of quantum-inspired neural networks with outstanding potentials at certain circumstances. Nonetheless, a definitive evaluating system with detailed metrics is tremendously vital and indispensable owing to the vague indicators in comparison between the novel and traditional deep learning models at present. Hence, to improve and evaluate the performances of the novel neural networks more comprehensively in complex and unpredictable environments, we propose two hybrid quantum-inspired neural networks which are rooted in residual and dense connections respectively for pattern recognitions with completeness representation theory for model assessment. Comparative analyses against pure classical models with detailed frameworks reveal that our hybrid models with lower parameter complexity not only match the generalization power of pure classical models, but also outperform them notably in resistance to parameter attacks with various asymmetric noises. Moreover, our hybrid models indicate unique superiority to prevent gradient explosion problems through theoretical argumentation. Eventually, We elaborate on the application scenarios where our hybrid models are applicable and efficient, which paves the way for their industrialization and commercialization.
Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy

Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image domains. However, it is non-trivial to extend SSM to the point cloud field, because of the causality requirement of SSM and the disorder and irregularity nature of point clouds. In this paper, we propose a novel SSM-based point cloud processing backbone, named Point Mamba, with a causality-aware ordering mechanism. To construct the causal dependency relationship, we design an octree-based ordering strategy on raw irregular points, globally sorting points in a z-order sequence and also retaining their spatial proximity. Our method achieves state-of-the-art performance compared with transformer-based counterparts, with 93.4% accuracy and 75.7 mIOU respectively on the ModelNet40 classification dataset and ScanNet semantic segmentation dataset. Furthermore, our Point Mamba has linear complexity, which is more efficient than transformer-based methods. Our method demonstrates the great potential that SSM can serve as a generic backbone in point cloud understanding. Codes are released at https://github.com/IRMVLab/Point-Mamba.
Dark Branes for Dark Matter

We propose a setup for the origin of dark matter based on spacetime with a warped extra dimension and three branes: the Planck brane, the TeV brane, at a (few) TeV scale $\rho_T$, and a dark brane, at a (sub)-GeV scale $\rho_1\lesssim 100$ GeV $\ll\rho_T$. The Standard Model is localized in the TeV brane, thus solving the Higgs hierarchy problem, while the dark matter $\chi$, a Dirac fermion with mass $m_\chi<\rho_1$, is localized in the dark brane. The radion, with mass $m_r<m_\chi$, interacts strongly ($\sim m_\chi/\rho_1\sim\mathcal O(1)$) with dark matter and very weakly ($\sim m_{f}\rho_1/\rho_T^2\ll 1$) with the Standard Model matter $f$. The generic conflict between the bounds on its detection signatures and its proper relic abundance is avoided as dark matter annihilation is $p$-wave suppressed. The former is determined by its very weak interactions with the SM and the latter by its much stronger annihilation into radions. Therefore, there is a vast range in the Dark Matter's parameter space where the correct relic abundance is achieved consistently with the existing bounds. Moreover, for the dark brane with $\rho_1\lesssim 10$~GeV, a confinement/deconfinement first order phase transition, where the radion condensates, produces a stochastic gravitational waves background at the nanoHz frequencies, which can be identified with the signal detected by the Pulsar Timing Array (PTA) experiments. In the PTA window, for $0.15 \textrm{ GeV}\lesssim m_\chi\lesssim 2$ GeV the relic abundance is reproduced and all constraints are satisfied.