A Study on Rapid Adoption of Zero Trust Network Architectures by Global Organizations Due to COVID-19 Pandemic

2021 ◽  
pp. 26-33
Author(s):  
Aniket Deshpande
Keyword(s):  
Network Architectures ◽  
Trust Network ◽  
Global Organizations

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

The Language of Global Success

2019 ◽  
Author(s):  
Tsedal Neeley
Keyword(s):  
Cultural Differences ◽  
High Tech ◽  
Lingua Franca ◽  
Global Organizations ◽  
Cross Border ◽  
The World ◽  
Global Language ◽  
Language Strategies ◽  
Native Tongue

For nearly three decades, English has been the lingua franca of cross-border business, yet studies on global language strategies have been scarce. Providing a rare behind-the-scenes look at the high-tech giant Rakuten in the five years following its English mandate, this book explores how language shapes the ways in which employees in global organizations communicate and negotiate linguistic and cultural differences. Drawing on 650 interviews conducted across Rakuten's locations around the world, the book argues that an organization's lingua franca is the catalyst by which all employees become some kind of “expat”—detached from their native tongue or culture. Demonstrating that language can serve as the conduit for an unfamiliar culture, often in unexpected ways, the book uncovers how all organizations might integrate language effectively to tap into the promise of globalization.

Enabling Things To Talk in Heterogeneous Self-Organizing Networks—An Exhaustive Critique For IOT

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 728-750
Author(s):  
Naeem Z Azeemi ◽  
Saira Khan ◽  
Sharmini Enoch ◽  
Riktesh Srivastava ◽  
Omar al Basheer ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Adaptive Modulation ◽  
Sustainable Growth ◽  
Adaptive Modulation And Coding ◽  
Network Architectures ◽  
Multiple Antenna ◽  
Multiple Access Schemes ◽  
Natural Outcome ◽  
Air Interfaces ◽  
The Internet Of Things

The superstructure network in the Internet of Things (IoT) is an emerging network targeted to enable an ecosystem of smart applications and services. It connectsphysical resources and peopletogether with software, hence contribute to sustainable growth, provided it combines and guarantees trustand security for people and businesses.  In this work we presented smart city viewpoint opt-in to the Firth Generation (5G) mobile networks. Both a framework and deployment are explored rigorously to assist and predicting robustness of IoT technologies and applications as a natural outcome of the Third Generation Partnership Project (3GPP) in general and LTE in particular. These technologies are compared on the basis of Air Interfaces and their Specifications i.e. Adaptive Modulation and Coding, Multiple Access Schemes and Multiple Antenna Techniques along with the evolution and comparison of the Network Architectures.

The Decline of the Liberal Global Order and the Revival of Nationalism

2018 ◽  
Author(s):  
Yale H. Ferguson ◽  
Richard W. Mansbach
Keyword(s):  
United States ◽  
The United States ◽  
Global Politics ◽  
Global Order ◽  
Donald Trump ◽  
National Interests ◽  
Global Organizations ◽  
Obama Administration ◽  
Us Hegemony ◽  
National Populism

This chapter addresses the erosion of the postwar liberal global order and the accompanying disorder in global politics. It describes the perceptions of declining US hegemony during the Obama administration of American decline and the return of geopolitical and economic rivalries that are undermining the liberal order. The election of President Donald Trump in 2016 in the United States was the most significant manifestation of national populism that has emerged in recent years in Europe and elsewhere. The profile of supporters of national populism are much the same globally. They oppose so-called elites and immigrants (especially minorities) whom they blame for the loss of manufacturing jobs. After defining national populism, the chapter describes how it fosters isolationism and malignant nationalism and focuses on national interests rather than global cooperation. Such policies threaten the movement of goods and people, multinational global organizations, and the postwar order in which globalization thrives.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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