Zayed University

About: Zayed University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Web service & Computer science. The organization has 1030 authors who have published 3346 publications receiving 42546 citations.

Isms and schisms: A meta-analysis of the prejudice-discrimination relationship across racism, sexism, and ageism

Abstract: Summary Racism, sexism, and ageism persist in modern day organizations and may translate into workplace discrimination, which can undermine organizational effectiveness. We provide the first meta-analysis comparing the relationships between these three types of prejudice (racism, sexism, and ageism) and three types of workplace discrimination (selection, performance evaluation, and opposition to diversity-supportive policies). Across outcomes, racism was associated with workplace discrimination, whereas sexism was not. Ageism was associated with discriminatory selection and opposition to organizational policies supporting older workers; however, ageism was not related to discriminatory performance evaluation. Consistent with prior research and theory, Implicit Association Test measures were related to subtle discrimination (opposition to diversity-supportive policies) but not deliberate discrimination (selection and performance evaluation). Finally, prejudice was more strongly associated with discrimination against real as compared with hypothetical targets. Implications for organizational researchers and practitioners are discussed. Copyright © 2017 John Wiley & Sons, Ltd.

A Secure Fog-based Platform for SCADA-based IoT Critical Infrastructure

Abstract: 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 datacenters 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 IoTcritical 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 (SeFoP) and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. These results can pave the way towards the development of more secured 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 SeFoP, which is around 2.8 seconds when adding 5 virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs compared to Multi-Level user Access Control (MLAC) platform.

Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning

Abstract: In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. FSL tasks have been predominantly solved by leveraging the ideas from gradient-based meta-learning and metric learning approaches. However, recent works have demonstrated the significance of powerful feature representations with a simple embedding network that can outperform existing sophisticated FSL algorithms. In this work, we build on this insight and propose a novel training mechanism that simultaneously enforces equivariance and invariance to a general set of geometric transformations. Equivariance or invariance has been employed standalone in the previous works; however, to the best of our knowledge, they have not been used jointly. Simultaneous optimization for both of these contrasting objectives allows the model to jointly learn features that are not only independent of the input transformation but also the features that encode the structure of geometric transformations. These complementary sets of features help generalize well to novel classes with only a few data samples. We achieve additional improvements by incorporating a novel self-supervised distillation objective. Our extensive experimentation shows that even without knowledge distillation our proposed method can outperform current state-of-the-art FSL methods on five popular benchmark datasets.

Design Guidelines for Blockchain-Assisted 5G-UAV Networks

Abstract: Fifth generation (5G) wireless networks are designed to meet various end-user quality of service (QoS) requirements through high data rates (typically of gigabits per second) and low latencies. Coupled with fog and mobile edge computing, 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale artificial-intelligence-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low- and high-density locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of unmanned aerial vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (drones as a service) and routing to and from end users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide a wide range of complex authenticated service and data availability. Particular attention is paid to comparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as federated learning.

A Comprehensive Survey of the Tactile Internet: State-of-the-Art and Research Directions

Abstract: The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communications and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions.