An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers.The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time

Context-aware multifaceted trust framework for evaluating trustworthiness of cloud providers

With the rapidly increasing number of cloud-based services, selecting a service provider is becoming more and more difficult. Among the many factors to be considered, trust in a given service and in a service provider is often critical. Appraisal of trust is a complex process, information about the offered service's quality needs to be collected from a number of sources, while user requirements may place different emphasis on the various quality indicators. Several frameworks have been proposed to facilitate service provider selection, however, only very few of them are built on existing cloud standards, and adaptability to different contexts is still a challenge. This paper proposes a new trust framework, called Context-Aware Multifaceted Trust Framework (CAMFT), to assist in evaluating trust in cloud service providers. CAMTF is flexible and context aware: it considers trust factors, users and services. When making recommendations, CAMFT employs a combination of mathematical methods that depend on the type of trust factors, and it takes both service characteristics and user perspective into account. A case study is also presented to demonstrate CAMFT's applicability to practical cases

An Empirical Study of Cross-Platform Mobile Development in Industry

The purpose of this study is to report on the industry’s perspectives and opinions on cross-platform mobile development, with an emphasis on the popularity, adoption, and arising issues related to the use of technical development frameworks and tools. We designed and conducted an online survey questionnaire, for which 101 participants were recruited from various developer-oriented online forums and websites. A total of five questions are reported in this study, of which two employed a Likert scale instrument, while three were based on multiple choice. In terms of technical frameworks, we find that PhoneGap, the Ionic Framework, and React Native were the most popular in use, both in hobby projects and in professional settings. The participants report an awareness of trade-offs when embracing cross-platform technologies and consider penalties in performance and user experience to be expected. This is also in line with what is reported in academic research. We find patterns in the reported perceived issues which match both older and newer research, thus rendering the findings a point of departure for further endevours

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region

Lexicographical minimization of routing hops in hop-constrained node survivable networks

In this paper, we address a hop-constrained node survivable traffic engineering problem in the context of packet switched networks with source based routing. Consider a telecommunications network with given link capacities that was dimensioned for a set of commodities, with estimated demand values, such that each commodity demand is routed through a set of node disjoint service and backup paths, all with at most H hops. When the network is put in operation, the real demand values might be different from the initial estimated ones. So, we aim to determine a set of hop-constrained service and backup paths for each commodity, with known demand values, such that the whole set of paths does not violate the link capacities. The traffic engineering goal is related with the hop minimization of only the service paths. We aim to minimize the number of routing hops in a lexicographical sense, i.e., minimize the number of service paths with the worst number of hops; then, among all such solutions, minimize the number of service paths with the second worst number of hops; and so on. We address two traffic engineering variants: in the first variant, all service paths of each commodity are accounted for in the objective function while in the second variant only the worst service path of each commodity is accounted for in the objective function. We first present and discuss three classes of Integer Linear Programming hop-indexed models- disaggregated, mixed and aggregated — for both variants. Then, we prove that, although the three classes are not equivalent, they provide the same Linear Programming relaxation bounds for each variant. Finally, we present computational results showing that, as a consequence, the more compact aggregated models are more efficient in obtaining the optimal integer solutions