Restrictive Voting Technique for Faces Spoofing Attack

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications

A Mathematical Approach to Enhance the Performance of Air Pollution Models

The main objective of this chapter is to introduce a mathematical method for enhancing the correctness of the output results of air pollution dispersion models via the calibration of input background concentrations. For developing this method, an air pollution model was set up in ADMS‐Roads for a study area in the City of Nottingham in the UK. The method was applied iteratively to the input background concentrations, which effectively reduced the error between calculated and monitored air pollution concentrations on both the annual mean and hourly levels. The inclusion of the traffic flow profiles of the modeled road network reduced further the error between the hourly, but not the annual mean, calculated and monitored concentrations. The application of the calibration approach to the model in ADMS‐Roads was compared to the use of grid air pollution sources for the same model in ADMS‐Urban. In terms of the accuracy of the model results, the calibration approach was better than using grid sources on the annual mean level and was much better on the hourly level. Compared to the use of grid sources in ADMS‐Urban, the use of the calibration approach in either ADMS‐Roads or ADMS‐Urban can significantly reduce the air pollution model runtime

Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume. Cement replacement materials provide a wide variety of benefits such as lower cost, reduced consumption of natural resources, reduced carbon dioxide emissions and improved fresh and hardened properties. SCC is used in many applications such as sections with congested reinforcement and high rise shear walls and there is a need for the prediction of the performance of SCC used. Artificial Neural networks (ANN) are widely used in civil engineering for the prediction of the performance of some engineering materials such as compressive strength and durability. However, currently, studies on SCC containing silica fume are very rare. In this paper, an artificial neural networks (ANN) model is developed to predict the compressive strength of SCC with silica fume using the Levenberg-Marquardt back propagation algorithm based on a database from 366 experimental studies. The model developed was correlated with a nonlinear relationship between the constituents (input) and the compressive strength of SCC (output). To evaluate the predictive ability and generalize the developed model, other researchers’ experimental results were compared with the model prediction and good agreements are found. A parametric study was conducted to study the sensitivity of the ANN proposed model to some parameters such as water/binder ratio and superplasticizer content. The model developed in this study can potentially be used for SCC compressive strength prediction with very acceptable results and a high correlation coefficient R2=0.93. The developed model is practical, easy to use and user friendly. Doi: 10.28991/cej-2021-03091642 Full Text: PD

Phonological and morphological variation in the speech of Fallahis in Karak (Jordan)

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Novel technology for sustainable petroleum oily sludge management : bio-neutralization by indigenous fungal-bacterial co-cultures

The treatment and disposal of petroleum oily sludges represent major challenges for petroleum industries. The oily sludges consume a high portion of a refiner's budget and pose a serious threat to the environment. Montreal and Kyoto protocols introduced significant restrictions on the disposal of petroleum wastes and reduced the options available for treating this type of hazardous wastes. This research considered the application of bioremediation principles to petroleum oily sludge using a new testing technique. Approximately, 35% of the total petroleum hydrocarbons, and 81% of the aliphatic hydrocarbons in the sludge were degraded using a fungal-bacterial co-culture. To my knowledge, this was the first time fungal-bacterial co-cultures have been used in the treatment of petroleum oily sludge. Prior to treatment with the co-cultures, the sludge was subjected to a special electrokinetic separation technology that reduced its oil and water contents. The cultures were isolated from petroleum oily sludge taken from the bottom of crude oil storage tanks. The fungal and bacterial strains, used in the co-culture, were identified as Paecilomyces variotii and Bacillus cereus , respectively. The cultures were inoculated on 0.22 om filters laid over the sludge. Reduction in aliphatic hydrocarbons was estimated using Fourier transform infrared spectrometry (FTIR). Total Petroleum Hydrocarbons (TPH) were estimated using solvent extraction. An amphoteric surfactant was used in the studies but it did not improve biodegradation rates. The testing technique gave a comprehensive indication of the efficiency of the process and the toxicity of the sludge for defined cultures of the microorganisms

Time-Fractional KdV Equation for the plasma in auroral zone using Variational Methods

The reductive perturbation method has been employed to derive the Korteweg-de Vries (KdV) equation for small but finite amplitude electrostatic waves. The Lagrangian of the time fractional KdV equation is used in similar form to the Lagrangian of the regular KdV equation. The variation of the functional of this Lagrangian leads to the Euler-Lagrange equation that leads to the time fractional KdV equation. The Riemann-Liouvulle definition of the fractional derivative is used to describe the time fractional operator in the fractional KdV equation. The variational-iteration method given by He is used to solve the derived time fractional KdV equation. The calculations of the solution with initial condition A0*sech(cx)^2 are carried out. Numerical studies have been made using plasma parameters close to those values corresponding to the dayside auroral zone. The effects of the time fractional parameter on the electrostatic solitary structures are presented.Comment: 1 tex file + 5 eps figure