University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning

Abstract: Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. We use dynamic analysis to craft dynamic behavior profiles as feature vectors. Furthermore, we develop a new dataset, namely CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories: Adware, Banking, SMS, Riskware, and Benign. Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. The experimental results show that the model can classify Android apps with respect to malware category with F 1 -Score of 97.84 percent and a false positive rate of 2.76 percent, considerably higher than LP. These results demonstrate the robustness of our model despite the small number of labeled instances.

The scaffold microenvironment for stem cell based bone tissue engineering

Abstract: Bone tissue engineering uses the principles and methods of engineering and life sciences to study bone structure, function and growth mechanism for the purposes of repairing, maintaining and improving damaged bone tissue. Scaffolds not only provide structural support for stem cells in cell adhesion and proliferation and bone formation, but also serve as a microenvironment for guiding stem cell differentiation and tissue regeneration and for controlling tissue structure. This review presents the research status of the scaffold microenvironment for bone-related stem cells based on bone tissue engineering. Scaffold materials and the stem cell microenvironment are described in this review, and the existing shortcomings are also simply mentioned.

The Preparation of Hierarchical Flowerlike NiO/Reduced Graphene Oxide Composites for High Performance Supercapacitor Applications

Abstract: Reduced graphene oxide (rGO) and NiO composites were prepared with an environmentally friendly method, in which hydrogen gas was employed as the reducing agent to convert reduced graphene oxides. Our study indicates that the success of this new approach is because NiO not only is an additive of the composites but also acts as a catalyst to facilitate the reduction. Characterization with scanning electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and X-ray powder diffraction illustrates that the as-prepared rGO/NiO composites have a three-dimensional flowerlike hierarchical structure, which prevents graphene from taking face to face aggregation and therefore greatly improves the stability of the composite materials. A hybrid capacitor electrode made of the NiO/rGO composites shows great performance, in which the maximum specific capacitance is close to 428 F g–1 at a discharge current density of 0.38 A g–1 in a 6.0 M KOH electrolyte.

Modelling the Quaternary evolution of shore platforms and erosional continental shelves

Abstract: A mathematical model was used to investigate the effect of glacially induced fluctuations in sea level on the evolution of wave-cut shore platforms and erosional continental shelves during the Quaternary. The model used two deep-water wave sets, which were used to calculate breaker height and depth, and the force of the waves at the waterline, according to the width and bottom roughness of the surf zone and the gradient of the submarine slope. The model also incorporated an erosional threshold related to the strength of the rocks, the number of hours each year in which the water level is at each intertidal elevation and the amount and persistence of the debris at the cliff foot. Most runs were made using a sea level model that consisted of 26 glacial cycles from 2 million to 0·9 million years ago, and nine, of approximately twice the amplitude and wavelength, in the last 0·9 million years. The model emphasized the dynamic association between the contemporary intertidal platform and the continental shelf. Both surfaces trend towards a state of static equilibrium under oscillating sea level conditions, when attenuated waves are unable to continue eroding the rock. If there has not been enough time to reduce the gradient of the shallower portions of the continental shelf, however, intertidal shore platforms can be in a temporary, though possibly long-lasting, state of dynamic equilibrium. The model suggests that most platforms are, at least in part, inherited from one, or in many cases more, interglacial stages when sea level was similar to today's. Copyright © 2001 John Wiley & Sons, Ltd.