Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are useful for all kinds of failure datasets and applications. Hence finding the most optimal model for reliability prediction is an important concern. This paper suggests a novel method to substantially pick the best model of reliability prediction. This method is the combination of analytic hierarchy method (AHP), hesitant fuzzy (HF) sets and technique for order of preference by similarity to ideal solution (TOPSIS). In addition, using the different iterations of the process, procedural sensitivity was also performed to validate the findings. The findings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.     

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in climate are of serious concern. The leading causes beyond these destructive variations are ozone layer depletion, greenhouse gas release, deforestation, pollution, water resources contamination, and UV radiation. This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues, e.g., skin cancer, damage to marine life, crops damage, and impacts on living being’s immune systems. We have tried to classify the ozone images dataset into two major classes, depleted and non-depleted regions, to extract the required persuading features through F-RCNN. Furthermore, CNN has been used for feature extraction in the existing literature, and those extricated diverse RoIs are passed on to the CNN for grouping purposes. It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results. The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91% to 93% in identifying climate variation through ozone concentration classification, whether the region in the image under consideration is depleted or non-depleted. Our proposed model presented 93% accuracy, and it outperforms the prevailing techniques.     

Effect of terbium doping in bismuth ferrite nanoparticles for the degradation of organic pollutant under sunlight irradiation

Journal of Materials Science: Materials in Electronics - Herein, we have studied the influence of terbium (Tb) doping in bismuth ferrite (BiFeO3/BFO) nanoparticles towards its photocatalytic...     

Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

Industrial internet of things (IIoT) is the usage of internet of things (IoT) devices and applications for the purpose of sensing, processing and communicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-related processes to attain more profits. However, such IoT based smart industries need internet connectivity and interoperability which makes them susceptible to numerous cyber-attacks due to the scarcity of computational resources of IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoT environment. In this paper, we propose a hyperelliptic curve cryptography (HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with the aim of improving security while lowering computational and communication overhead in IIoT environment. HECC with 80-bit smaller key and parameters sizes offers similar security as elliptic curve cryptography (ECC) with 160-bit long key and parameters sizes. We assessed the IIoT-CS scheme security by applying formal and informal security evaluation techniques. We used Real or Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formal security analysis and proved that the IIoT-CS scheme provides resistance to various attacks. Our proposed IIoT-CS scheme is relatively less expensive compared to the current state-of-the-art in terms of computational cost and communication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead, respectively, compared to the most recent protocol.     

A novel efficient mixed formulation for strain-gradient models

Various finite elements based on mixed formulations have been proposed for the solution of boundary value problems involving strain-gradient models. The relevant literature, however, does not provide details on some important theoretical aspects of these elements. In this work, we first present the existing elements within a novel, single mathematical framework, identifying some theoretical issues common to all of them that affect their robustness and numerical efficiency. We then proceed to develop a new family of mixed elements that addresses these issues while being simpler and computationally cheaper. The behavior of the new elements is further demonstrated through two numerical examples.     

Empirical evaluation of the fault detection effectiveness and test effort efficiency of the automated AOP testing approaches

Context

Testing process is a time-consuming, expensive, and labor-intensive activity in any software setting including aspect-oriented programming (AOP). To reduce the testing costs, human effort, and to achieve the improvements in both quality and productivity of AOP, it is desirable to automate testing of aspect-oriented programs as much as possible.

Objective

In recent past years, a lot of research effort has been devoted to testing aspect-oriented programs but less effort has been dedicated to the automated AOP testing. This denotes that the current research on automated AOP testing is not sufficient and is still in a stage of infancy. In order to advance the state of the research in this area and to provide testers of AOP-based projects with a comparison basis, a detailed evaluation of the current automated AOP testing approaches in a thorough and experimental manner is required. Thus, the objective of this paper is to provide such evaluation of the current approaches.

Method

In this paper, we carry out an empirical study based on mutation analysis to examine four (namely Wrasp, Aspectra, Raspect, and EAT) existing automated AOP testing approaches, particularly their underlying test input generation and selection strategies, with regard to fault detection effectiveness. In addition, the approaches are compared in terms of required effort in detecting faults as part of efficiency evaluation.

Results

The experimental results and comparison provided insights into the effectiveness and efficiency of automated AOP testing with their respective strengths and weaknesses. Results showed that EAT is more effective than the other automated AOP testing approaches but not significant for all approaches. EAT was found to be significantly better than Wrasp at 95% confidence level (i.e. p < 0.05), but not significantly better than Aspectra or Raspect. Concerning the test effort efficiency, Wrasp was significantly (p < 0.05) efficient with requiring the lowest amount of test effort compared to the other approaches. Whereas, EAT showed to be not very efficient by recording the highest amount of test effort.

Conclusion

This implies that EAT can currently be the most effective automated AOP testing approach but perhaps less efficient. More generally, search-based testing (as underlying strategy of EAT approach) might achieve better effectiveness but at the cost of greater test effort compared to random testing (as underlying strategy of other approaches).     

Machine learning based adaptive watermark decoding in view of anticipated attack

We present an innovative scheme of blindly extracting message bits when a watermarked image is distorted. In this scheme, we have exploited the capabilities of machine learning (ML) approaches for nonlinearly classifying the embedded bits. The proposed technique adaptively modifies the decoding strategy in view of the anticipated attack. The extraction of bits is considered as a binary classification problem. Conventionally, a hard decoder is used with the assumption that the underlying distribution of the discrete cosine transform coefficients do not change appreciably. However, in case of attacks related to real world applications of watermarking, such as JPEG compression in case of shared medical image warehouses, these coefficients are heavily altered. The sufficient statistics corresponding to the maximum likelihood based decoding process, which are considered as features in the proposed scheme, overlap at the receiving end, and a simple hard decoder fails to classify them properly. In contrast, our proposed ML decoding model has attained highest accuracy on the test data. Experimental results show that through its training phase, our proposed decoding scheme is able to cope with the alterations in features introduced by a new attack. Consequently, it achieves promising improvement in terms of bit correct ratio in comparison to the existing decoding scheme.     

Scaling Virtual Worlds with a Physical Metaphor

Online virtual worlds have long been an anticipated medium for digital communications. They provide a compelling substrate for shared, networked environments where people can communicate, shop, socialize, collaborate, and learn. However, today's systems fall short of their imagined potential. The Meru project is designing and implementing an architecture for virtual worlds of the future. Our key insight is that a virtual model of the real world is a comfortable metaphor which addresses a wide range of issues including security, scalability, and federation. This symmetry between real and virtual worlds also permits a natural interaction between the two.     

Interactions betweenKalmia and black spruce: Isolation and identification of allelopathic compounds

Aqueous extracts of fresh leaves and organic soil of northern sheep laurel (Kalmia angustifolia var.angustifolia) were found to be inhibitory to the growth of black spruce (Picea mariana) germinants. Primary root growth of black spruce was more affected by the extracts than was shoot growth. The growth inhibition caused by the leaf extract was most pronounced under acidic conditions (pH 3–4). The aqueous extract ofKalmia leaves contained ferulic, vanillic, syringic, gentisic,m-coumaric,p-coumaric,o-hydroxyphenylacetic, andp-hydroxybenzoic acids as well as some other unknown compounds. These compounds were isolated from the aqueous extract ofKalmia leaves by ethyl acetate extraction and identified using thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC). Bioassay indicated that the overall toxicity of the phenolic compounds to black spruce appeared to increase in the order ofo-hydroxyphenylacetic,p-hydroxybenzoic, vanillic,p-coumaric, gentisic, syringic, ferulic, andm-coumaric acids.     

A Reliable Link-Layer Protocol for Robust and Scalable Intervehicle Communications

Current link-layer protocols for safety-related intervehicle communication (IVC) networks suffer from significant scalability and security challenges. Carrier sense multiple-access approaches produce excessive transmission collisions at high vehicle densities and are vulnerable to a variety of denial of service (DoS) attacks. Explicit time slot allocation approaches tend to be limited by the need for a fixed infrastructure, a high number of control messages, or poor bandwidth utilization, particularly in low-density traffic. This paper presents a novel adaptation of the explicit time slot allocation protocols for IVC networks. The protocol adaptive space-division multiplexing (ASDM) requires no control messages, provides protection against a range of DoS attacks, significantly improves bandwidth utilization, and automatically adjusts the time slot allocation in response to changes in vehicle densities. This paper demonstrates the need for and the effectiveness of this new protocol. The exposures of the current proposals to attacks on availability and integrity, as well as the improvements effected by ASDM, are analytically evaluated. Furthermore, through simulation studies, ASDM's ability to provide message delivery guarantees is contrasted with the inability of the current IVC proposals to do the same