Intention-oriented programming support for runtime adaptive autonomic cloud-based applications

The continuing high rate of advances in information and communication systems technology creates many new commercial opportunities but also engenders a range of new technical challenges around maximising systems’ dependability, availability, adaptability, and auditability. These challenges are under active research, with notable progress made in the support for dependable software design and management. Runtime support, however, is still in its infancy and requires further research. This paper focuses on a requirements model for the runtime execution and control of an intention-oriented Cloud-Based Application. Thus, a novel requirements modelling process referred to as Provision, Assurance and Auditing, and an associated framework are defined and developed where a given system’s non/functional requirements are modelled in terms of intentions and encoded in a standard open mark-up language. An autonomic intention-oriented programming model, using the Neptune language, then handles its deployment and execution.     

Efficient 3D medical image segmentation algorithm over a secured multimedia network

Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.

     

Remote health monitoring of elderly through wearable sensors

Multimedia Tools and Applications - Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for...     

An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier

The Journal of Supercomputing - Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and...     

Damage detection in glass/epoxy composite structure using 8–12 GHz X-band

In this study, an electromagnetic wave 8–12 GHz X band microwave was utilized to detect various types of damage in a woven fabric composite structure. Damages, such as cracks, delamination, bubbles, and voids, were synthesized artificially in accordance to ASTM D2734. Variable thicknesses of composite structure were also analyzed, and the influence of defect on the transmitted signal was investigated. Network analyzer (ENA5701C) in X-band was utilized for this investigation. Detection was based on the changes in the electromagnetic properties, such as permittivity and permeability, and the reflection and transmission to microwaves were based on standard samples. These were implemented for each case of defect selected for this study. Experimental tests revealed that damage in the transparent glass/epoxy composite can be recognized clearly. Particularly, sample thickness was detected when the microwave was applied. This testing method can be considered an inline operation and non-destructive testing for such composites, especially during fabrication.     

A secure fog-based platform for SCADA-based IoT critical infrastructure

The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low-latency and enhanced security for mission-critical data. However, fog security and privacy for SCADA-based IoT critical infrastructures remains an under-researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog-based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. These results can pave the way toward the development of a more secure and trusted SCADA-based IoT critical infrastructure, which is essential to counter cyber threats against next-generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.     

Catalysis of neural activation functions: Adaptive feed-forward training for big data applications

Deep Learning in the field of Big Data has become essential for the analysis and perception of trends. Activation functions play a crucial role in the outcome of these deep learning frameworks. The existing activation functions are hugely focused on data translation from one neural layer to another. Although they have been proven useful and have given consistent results, they are static and mostly non-parametric. In this paper, we propose a new function for modified training of neural networks that is more flexible and adaptable to the data. The proposed catalysis function works over Rectified Linear Unit (ReLU), sigmoid, tanh and all other activation functions to provide adaptive feed-forward training. The function uses vector components of the activation function to provide variational flow of input. The performance of this algorithm is tested on Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR10) datasets against the conventional activation functions. Visual Geometry Group (VGG) blocks and Residual Neural Network (ResNet) architectures are used for experimentation. The proposed function has shown significant improvements in comparison to the traditional functions with a 75 ± 2.5% acuuracy across activation functions. The adaptive nature of training has drastically decreased the probability of under-fitting. The parameterization has helped increase the data learning capacity of models. On performing sensitivity analysis, the catalysis activation show slight or no changes on varying initialization parameters.

     

Effectiveness of filter gabions against slope failure due to heavy rainfall

Slope failures due to heavy rainfall events are phenomena that can cause serious damage to social infrastructures and the loss of lives. Based on previous studies, natural slope failures are generally shallow and originate at the slope toe where infiltrated rainwater has accumulated and saturated it. Hence, it is extremely important to prevent these initial failures from inducing entire slope failures. In the present study, firstly, 1 g model tests, called G series tests, were conducted. In the tests, a gabion filled with filter materials was placed at the slope toe of each model for reinforcement and to drain the accumulated rainwater from the slope toe. Filter gabions have been found to shrink the failure regions and to significantly extend the time until slope failures occur. The failure mechanism in the G series tests was almost similar to that in cases without filter gabions if focus was placed on the slope above the filter gabions. However, the drainage effect was small. Secondly, P series tests, in which a filter gabion with a pipe was introduced for each model, were conducted. The results of these tests indicated that the displacements significantly decreased as the diameter of the pipe and the depth of the pipe’s insertion to the surface layer increased. Water did not discharge through the pipe until the pore water pressure around the pipe reached positive values. The failures always started when a phreatic surface appeared on the slope surface. Thus, it is very important to prevent a phreatic surface from forming on the slope surface. The adequate arrangement of a filter gabion with a drainage pipe may increase the potential for slope stability.     

Evidence for UV-B-induced DNA degradation in Euglena gracilis mediated by activation of metal-dependent nucleases

It is demonstrated that in vivo irradiation with artificial UV-B for several hours significantly reduces the amount of large DNA extractable from immobilized Euglena in comparison with non-irradiated controls. This UV-B effect can be eliminated by a drastic reduction of the divalent ion concentration in the extracellular medium, i.e. the substitution of the culture medium by Tris-buffered agarose. Moreover, in vitro degradation of large DNA is demonstrated for crude protein extracts isolated from non-irradiated or UV-B-irradiated Euglena. The nuclease activity is shown for both crude protein extracts and purified nucleases; in both cases, two protein bands possessing nuclease activity are obtained with apparent molecular masses of 26 and 40 kDa and their activity is inhibited by specific nuclease inhibitors, i.e. aurintricarboxylic acid and ATP, applied at a concentration as low as 10(-8) M. Moreover, in vitro, nuclease activity clearly depends on the pH, with an optimum around pH 4.5, and on the ion composition of the extracellular medium. A strong stimulating effect is shown for Ca2+ with an optimum around 10(-4) M; this effect is potentiated by Zn2+ and Mn2+, but strongly counteracted by Mg2+ and the calmodulin inhibitors trifluoperazine and N- (6-aminohexyl)-5-chloro-1-naphthalenesulphonamide (W5). These results favour the concept which explains the lethal UV-B effect on Euglena as arising from a change in the general metabolic state of the cell and an activation of a DNA-degrading system, i.e. activation of metal-dependent nucleases (U.K. Tirlapur, D.-P. H?der and R. Scheuerlein, UV-B mediated damage in the photosynthetic flagellate, Euglena gracilis, studied by image analysis, Beitr. Biol. Pflanzen, 67 (1992) 305-317).