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.

     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Spatio‐temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces

Multi‐view reconstruction aims at computing the geometry of a scene observed by a set of cameras. Accurate 3D reconstruction of dynamic scenes is a key component for a large variety of applications, ranging from special effects to telepresence and medical imaging. In this paper we propose a method based on Moving Least Squares surfaces which robustly and efficiently reconstructs dynamic scenes captured by a calibrated set of hybrid color+depth cameras. Our reconstruction provides spatio‐temporal consistency and seamlessly fuses color and geometric information. We illustrate our approach on a variety of real sequences and demonstrate that it favorably compares to state‐of‐the‐art methods.     

Self-repair ability of evolved self-assembling systems in cellular automata

Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair.     

Polymeric absorbent for water sorption based on chemically crosslinked poly (acrylamide/2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt) hydrogels

Summary Swelling equilibrium of polyelectrolyte copolymer gels containing of acrylamide (AAm) and 2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt (AMPS) have been studied as a function of copolymer composition. AAm/AMPS hydrogels were prepared by free radical solution polymerization in aqueous solution of AAm with AMPS as anionic comonomer and two multifunctional crosslinkers such as ethylene glycol dimethacrylate (EGDMA) and trimethylolpropane triacrylate (TMPTA). Swelling experiments were performed in water at 25 °C, gravimetrically. The influence of AMPS content in hydrogels was examined. Swelling of AAm/AMPS hydrogels was increased up to 1018% (for containing 2% AMPS and crosslinked by EGDMA) 15246% (for containing 8% AMPS and crosslinked by TMPTA), while AAm hydrogels swelled up to 804% (crosslinked by TMPTA)–770% (crosslinked by EGDMA). The values of equilibrium water content of the hydrogels are 0.8851–0.9935. Diffusion behavior was investigated. Water diffusion into hydrogels was found to be non-Fickian in character.     

In situ esterification of rice bran oil with methanol and ethanol

In situ esterifications of high-acidity rice bran oil with methanol and ethanol and with sulfuric acid as catalyst were investigated. In the esterification with methanol, all free fatty acids (FFA) dissolved in methanol were interesterified within 15 min, and it was possible to obtain nearly pure methyl esters. The amount of methyl esters obtained from a given rice bran was dependent on the FFA content of the rice bran oil. In the esterification with ethanol, it was not possible to obtain pure esters as in methanol esterification, because the solubilities of oil components in ethanol were much higher than those in methanol.     

A Game Theoretical Approach for Improving the Operational Efficiencies of Less-than-truckload Carriers Through Load Exchanges

Less-than-truckload (LTL) transportation offers fast, flexible and relatively low-cost transportation services to shippers. In order to cope with the effects of economic recessions, the LTL industry implemented ideas such as reducing excess capacity and increasing revenues through better yield management. In this paper, we extend these initiatives beyond the reach of individual carriers and propose a collaborative framework that facilitates load exchanges to reduce the operational costs. Even though collective solutions are proven to provide benefits to the participants by reducing the inefficiencies using a system-wide perspective, such solutions are often not attainable in real-life as the negotiating parties are seeking to maximize their individual profits rather than the overall profit and also they are unwilling to share confidential information. Therefore, a mechanism that enables collaboration among the carriers should account for the rationality of the individual participants and should require minimal information transfer between participants. Having this in mind, we propose a mechanism that facilities collaboration through a series of load exchange iterations and identifies an equilibrium among selfish carriers with limited information transfer among the participants. Our time-efficient mechanism can handle large instances with thousands of loads as well as provide significant benefits over the non-collaborative management of LTL networks.

     

Improvements of the structural,thermal, and mechanical properties of structural adhesive with functionalized boron nitride nanoparticles

Investigations on the production and development of nanoparticle-reinforced polymer materials have been attracted attention by researchers. Various nanoparticles have been used to improve the mechanical, chemical, thermal, and physical properties of polymer matrix composites. Boron compounds come to the fore to improve the mechanical and thermal properties of polymers. In this study, mechanical, thermal, and structural properties of structural adhesive have been examined by adding nano hexagonal boron nitride (h-BN) to epoxy matrix at different percentages (0.5, 1, 2, 3, 4, and 5%). For this purpose, nano h-BN particles were functionalized with 3-aminopropyltriethoxysilane (APTES) to disperse the h-BN nanoparticles homogeneously in epoxy matrix and to form a strong bond at the matrix interface. Two-component structural epoxy adhesive was modified by using functionalized h-BN nanoparticles. The structural and thermal properties of the modified adhesives were investigated by scanning electron microscopy and energy dispersion X-ray spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis techniques. Tensile test and dynamic mechanical analysis were performed to determine the mechanical properties of the adhesives. When the results obtained from analysis were examined, it was seen that the nano h-BN particles functionalized with APTES were homogeneously dispersed in the epoxy matrix and formed a strong bond. In addition that, it was concluded from the experimental results that the thermal and mechanical properties of adhesives were improved by adding functionalized nano h-BN particles into epoxy at different ratios.