Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

Fully sequential selection procedures with a parabolic boundary

We present two fully sequential indifference-zone procedures to select the best system from a number of competing simulated systems when best is defined in terms of the maximum or minimum expected performance. These two procedures have parabola shaped continuation regions rather than the triangular continuation regions employed in several papers in the existing literature. The procedures we present accommodate unequal and unknown variances across systems and the use of common random numbers. However, we assume that basic observations are independent and identically normally distributed. We compare the performance of our procedures with those of other fully sequential procedures available in the literature.     

Pressure control system for electrospinning process

Electrospinning is used to produce micro‐ and nano‐sized synthetic fibers through the use of electrostatic forces. Commercially, viable production of fibers requires high throughput of uniform fibers that are free of defects. To achieve greater control over the process variables that affect the fiber formation, a scalable closed loop control system that can maintain a constant pressure at the capillary tip was designed and tested. Two sensing technologies, infrared and ultrasonic, were used and compared for their ability to detect the height of polymer solution in the electrospinning fluid container. The air pressure above the solution was measured with a pressure transducer and adjusted through a controllable syringe pump. The closed loop electrospinning system was successful at controlling and maintaining a constant pressure at the capillary tip to within 2% of the specified pressure continuously. The controlled pressure at the capillary tip showed a strong correlation to fiber diameter and uniformity for polydimethylsiloxane‐based polyurethane/DMF‐based fibers. However the control system was less effective to control fiber diameter for polyethylene oxide/Water‐based fibers. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers     

Eigenface-domain super-resolution for face recognition

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.     

On line measurement of crystallinity of nylon 6 nanocomposites by laser Raman spectroscopy and neural networks

A neural network is trained to estimate the unknown crystallinity and temperature of Nylon 6 and its nanocomposites while the material is undergoing cooling at a fixed rate. The innovation of the work is that the full spectrum captured by the laser Raman spectroscope is used to train a neural network for estimation of crystallinity and temperature. The small‐angle light scattering (SALS) and differential scanning calorimetry (DSC) data were used to provide the training examples for the neural network. Results indicate that the neural network can provide reliable estimates of the crystallinity and temperature provided that there is a sufficient number of training data available. Neural network methodology is also efficient in establishing the crystallization–temperature relationship as a function of cooling rate and demonstrates the heterogeneous nucleation effect of nanoclay in the nylon 6 matrix. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 474–483, 2004     

Aggregate volumetric estimation based on PCA and momentum-enhanced BP neural network

This paper proposes a Back Propagation (BP) neural network with momentum enhance-ment aiming to achieving the smooth convergence for aggregate volumetric estimation purpose. Net-work inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image. To simplify the network structure, principal component analysis technique is applied to reduce the input dimension. The specific network structure is finalized based on both em-pirical expertise and analysis on selecting the appropriate number of neurons in hidden layer. The network is trained using the finite number of randomly-picked particles. The training and test results suggest that, compared to the generic BP network, the training duration of the proposed neural network is greatly attenuated, the complexity of the network structure is largely reduced, and the es-timation precision is within 2%, being sufficiently up to technical satisfaction.     

Investigation of flow parameters of Reiner–Philippoff nanofluid flow with higher-order slip properties,activation energy,and bioconvection by artificial neural networks

In this study, the flow parameters of Reiner–Philippoff nanofluid flow with high-order slip properties, activation energy, and bioconvection have been analyzed using artificial neural networks (ANNs). Local Nusselt number (LNN), local Sherwood number (LSN), and motile density number (MDN) are considered as flow parameters. Numerical values have been obtained by numerical methods using flow equations. To estimate the flow parameters, three different ANN models have been designed. The Levenberg–Marquardt training algorithm is used in multilayer perceptron network models with 10 neurons in the hidden layers. In all, 70% of the data set has been used for training the models, 15% for validation, and 15% for testing. The performance analysis of the network models has been made by calculating the determined performance parameters. The R values for the LNN, LSN, and MDN parameters have been calculated as 0.99261, 0.98769, and 0.99102, respectively, and the average deviation values are −0.65%, 0.06%, and −0.11%, respectively. The attained outcomes showed that the ANNs can predict the LNN, LSN, and MDN, which are the flow parameters of the Reiner–Philippoff nanofluid flow, with high accuracy.     

Robust adaptive control for a MEMS vibratory gyroscope

This paper presents an adaptive sliding mode controller for a microelectromechanical systems (MEMS) vibratory z-axis gyroscope. The proposed adaptive sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. The stability of the closed-loop system can be guaranteed with the proposed adaptive sliding mode control strategy. The numerical simulation for MEMS gyroscope is investigated to show the effectiveness of the proposed control scheme. It is shown that the proposed adaptive sliding mode control scheme offers several advantages such as real-time estimation of gyroscope parameters and large robustness to parameter variations and external disturbance.     

基于Flash技术的校园电子地图的实现

电子地图技术是如今计算机科学应用的一个热点,将Flash技术引入校园电子地图的研究中,为校园环境的规划与展示提供一种全新的手段,结合新疆农业大学电子地图的开发,阐述电子地图的各种功能的实现方法,并通过与HTML的整合集成,完成校园电子地图开发.