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.

     

Investigating the effect of production process of ball mill refiner on some physical quality parameters of compound chocolate: response surface methodology approach

Chocolate compound was produced using ball mill refiner, and the effect of agitator shaft speed and refining time on the physical quality parameters (particle size, colour and steady‐state rheology) of compound chocolate was determined using response surface methodology. The shaft speed and refining time range were selected between 40–60 r.p.m. and 10–30 min, respectively. Determination coefficient of the models established for particle size, Newtonian viscosity and colour parameters (brightness, chroma and hue angle) were found to be very close to unity. Increasing shaft speed and time induced a reduction in particle size and an increase in viscosity of the samples. Temperature sweep test was also performed, and the obtained data were successfully fitted to Arrhenius equation to calculate the corresponding parameters representing temperature dependency of the compounds. The results highlighted that the establishment of such models can provide essential information in terms of optimisation of production processes regarding usage purpose of the compound chocolate.     

Mechanical properties and repair bond strength of polymer-based CAD/CAM restorative materials

Information on the mechanical properties and repairability of computer-aided design/computer-aided manufacturing (CAD/CAM) blocks is scarce. Five CAD/CAM blocks with similar indications were evaluated, including four resin nanoceramics, one polymer-infiltrated ceramic network (PICN), and one feldspathic ceramic. CAD/CAM blocks were sectioned into 4 mm × 1.2 mm × 13 mm bars for flexural strength (FS), and 4 mm thick blocks were prepared for elastic modulus (EM), nanohardness (NH), and microshear bond strength (µSBS) testing. FS of the CAD/CAM blocks was determined using a three-point bending test, whereas EM and NH values were measured using a nanoindenter. The reparability of the tested block materials was determined by the µSBS test. One-way ANOVA was conducted for FS, EM, NH, and µSBS followed by Tukey's pairwise comparison (α < 0.05). FS ranged from 115 to 207 MPa, EM from 8.21 to 44 GPa, NH from 0.76 to 7.24, and µSBS from 24.9 to 30.6 MPa. The findings of the present study revealed that resin nanoceramic blocks exhibited higher FS values than PICN and feldspathic ceramic, and they have acceptable mechanical properties for the fabrication of single-unit restorations according to the ISO 6872:2008. Furthermore, all CAD/CAM blocks tested can be successfully repaired regarding their recommended repair protocol.     

Microwave Dehydration of Borax: Characterization,Dehydration Kinetics,and Modelling

Glass Physics and Chemistry - In the present study, borax (Na2B4O7·10H2O) was dehydrated using microwave radiation and the dehydration rate curves were calculated. The kinetic parameters and...     

The Residual Stress Relaxation Behavior of Weldments During Cyclic Loading

Accurate measurement of residual stress is necessary to obtain reliable predictions of fatigue lifetime and enable estimation of time-to-facture for any given stress level. In this article, relaxation of welding residual stresses as a function of cyclic loading was documented on three common steels: AISI 1008, ASTM A572, and AISI 4142. Welded specimens were subjected to cyclic bending (R = 0.1) at different applied stresses, and the residual stress relaxation existing near the welds was measured as a function of cycles. The steels exhibited very different stress relaxation behaviors during cyclic loadings, which can be related to the differences in the microstructures of the specimens. A phenomenological model, which treats dislocation motion during cyclic loading as being analogous to creep of dislocations, is proposed for estimation of the residual stress relaxation.     

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.