∗\ast-g-frames in tensor products of hilbert C∗C^{\ast}-modules

In this paper, we study $\ast$-g-frames in tensor products of Hilbert $C^{\ast}$-modules. We show that a tensor product of two $\ast$-g-frames is a $\ast$-g-frames, and we get some result

MystifY : A Proactive Moving-Target Defense for a Resilient SDN Controller in Software Defined CPS

The recent devastating mission Cyber–Physical System (CPS) attacks, failures, and the desperate need to scale and to dynamically adapt to changes, revolutionized traditional CPS to what we name as Software Defined CPS (SD-CPS). SD-CPS embraces the concept of Software Defined (SD) everything where CPS infrastructure is more elastic, dynamically adaptable and online-programmable. However, in SD-CPS, the threat became more immanent, as the long-been physically-protected assets are now programmatically accessible to cyber attackers. In SD-CPSs, a network failure hinders the entire functionality of the system. In this paper, we present MystifY, a spatiotemporal runtime diversification for Moving-Target Defense (MTD) to secure the SD-CPS infrastructure. In this paper, we relied on Smart Grid networks as crucial SD-CPS application to evaluate our presented solution. MystifY’s MTD relies on a set of pillars to ensure the SDN controller resiliency against failures and attacks. The 1st pillar is a grid-aware algorithm that optimally allocates the most suitable controller–deployment location in large-scale grids. The 2nd pillar is a special diversifier that dynamically relocates the controller between heterogeneously configured hosts to avoid host-based attacks. The 3rd pillar is a temporal diversifier that dynamically detours controller–workload between multiple controllers to enhance their reliability and to detect and avoid controller intrusions. Our experimental results showed the efficiency and effectiveness of the presented approach

A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

Mode I stress intensity factor with various crack types

Presence of cracks in mechanical components needs much attention, where the stress field is affected by cracks and the propagation of cracks may be occurred causing the damage. The objective of this paper is to present an investigation of crack type effect on crack severity in a finite plate. Three cases of cracked plate with three different types of cracks are assumed in this work, i.e., single edge crack, center crack and double edge crack. 2D numerical models of cases of cracked plate are established in finite element analysis (FEA), ANSYS software by adopting PLANE 183 element. Values of FEA mode I stress intensity factor SIF and Von-Mises stress at crack apex are determined for cases of cracked plate under tensile stress with different values. To identify the crack severity, the comparison of FEA results for different cracked cases is made. The comparison showed that, single edge cracked plate (SECP) has the maximum values of mode I SIF and Von-Mises stress at crack apex, i.e. the greatest crack severity is considered. Also, values of FEA Von-Mises stress at crack apex for center cracked plate (CCP) are moderate and for double edge cracked plate (DECP) are the minimum. Besides, in case of high crack lengths, it is found that, FEA results of mode I SIF in case of (CCP) are higher than those of in case of (DECP). Consequently, crack severity is considered as moderate in case of (CCP) and the minimum in case of (DECP). Empirical formulas are used to approximately estimate mode I SIF for all the case studies of cracked plate in this study and the results are compared to those of FEA. A good agreement between analytical and FEA results has been showed by this comparison

Damage severity for cracked simply supported beams

This paper investigated the static and dynamic behaviors of isotropic cracked simply supported beam using finite element analysis (FEA), ANSYS software. Modal and harmonic vibration analysis of intact and damaged beam were performed in order to extract mode shapes of bending vibration, natural frequencies and obtain frequency response diagram. Static finite element analysis of undamaged and damaged simply supported beam was carried out to determine zero frequency deflection, then stiffness of intact and cracked beam was computed using conventional formula. Crack damage severity of damaged beam was calculated and it is noticed that as crack position is increased from left hand support of beam up to central point and crack depth is increased, then crack damage severity increases. The effect of mode shape pattern is investigated and it is found that the amount of decreasing of natural frequency is proportional to the normalized mode shape at position of crack. The exhibited correlation between results for damaged beam revealed that crack damage severity is proportional to zero frequency deflection and inversely proportional to first mode frequency

COVID-19 Detection from Chest X-ray Images using CNNs Models: Further Evidence from Deep Transfer Learning

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using Deep Learning. Methods: We collect a relatively large COVID-19 dataset comparing with previous studies that contain 309 real COVID-19 chest x-ray images. We also prepare 2,000 chest x-ray images of pneumonia cases and 1,000 images of healthy chest cases. Deep Transfer Learning is used to detect abnormalities in our image dataset. We fine-tune three, pre-trained convolutional neural networks (CNNs) models on a training dataset: DenseNet 121, NASNetLarge, and NASNetMobile. Results: The evaluation of our models on a test dataset show that these models achieve an average sensitivity rate of approximately 99.45 % and an average specificity rate of approximately 99.5 %. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using Deep Transfer Learning. However, the clinical diagnosis of COVID-19 disease is always necessary