By assuring the thorough consideration of network #security throughout the design, development, deployment, and sustainment phases of a network’s lifecycle, #SDN emerges as a powerful resource enabling the future #IoE.
The emergence of #wireless device networks and the #IoT places a new burden on networks to not only perform reliably, with guaranteed quality, in real-time, but also to do so dynamically. #SDN
In a world of rapid technological change, both the miniaturization and the growing distribution of inexpensive sensing/computing technologies presents a worrisome condition whereby technologies disseminate user information with greater stealth, in greater volume, often without the permission or the awareness of the user. Now, machine learning partners with the user to advance intuitive mobile permission-ing based on expected user preferences across a variety of contexts.
A comprehensive history of
the programmable network concept lends itself to a larger dialogue regarding the potential application of #SDN technologies to more-diverse range of problems and use-cases.
Over the past decade, researchers are lending an increasing amount of attention to the problems arising from the misuse of statistical methods to a variety of applications. An examination of the widespread mis-application of statistics across academic disciplines provides valuable information for the emerging class of data analysts. By examining these common hurdles, data analysts not only perfect their analytic craft through conscious and appropriate application of statistical techniques across a variety of problems, they contribute to an increased discernment among non-technical consumers of the oft-misinterpreted statistical insights. Especially given the abundance of statistical technologies, this increased discernment is a vital measure to reducing the various harms that emanate from misapplied methods and misinterpreted outcomes. The Problem of Statistics? Across applications of surgical research, psychotherapy research, and even litigation, the elementary application of statistical techniques falls prey to a ubiquitous […]
Emerging over the past century, the theory of strategic games has found increasing relevance across the fields of economics, political science, biology, psychology, computer science, operations research and decision analytics. This article provides a brief summary of the main concepts of game theory within their historical context. Using this foundation, we then explore game theory’s most relevant applications and their implications within the fields of data science and decision analytics.
Examining these terms and strengthening their distinctions helps to standardize a common lexicon across domains. This lexicon is an essential enabler of the growing discourse, research, discovery and application of data analysis techniques for solving real-world problems.
This concept model recommends a systems-level, top-down approach to #defense #supplychain management, which supports a holistic view of the joint services as an integrated #logistics chain.
The goal of DARPA’s LADS program is to develop new cybersecurity capability by exploring the intersection of the analog and digital domains enabling defense IoT. This article presents a literature review of the publicly-accessible research produced during Phase I of the LADS program.
As database technologies continue to specialize, and as Web 3.0 offers paradigmatic disruptions to how we conceive and model data, the question evolves: where is the installation of each technology most appropriate?
Numerous DARPA programs extend #IoT applications and technologies through #cybersecurity research and advanced #algorithms.
Enabled by BI, data might now not only be called efficiently, but also combined effectively across systems to produce new innovative advantage. However, quality analytics depends upon quality data and a framework of data governance.