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Peter J. Denning, Editor in ChiefThe digitally connected world has become a large, swirling sea of information stripped of context. We help our readers make sense of it, find meaning in it, learn what to trust, and speculate on our future.

Peter J. Denning,
Editor-in-Chief

 

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LATEST ARTICLES

Symposium

When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium)

by Tegjyot Singh Sethi, Mehmed Kantardzic

While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.

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Communication Corner

First write like you speak, then write like you write

by Philip Yaffe

Each "Communication Corner" essay is self-contained; however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Became a Swan," then read each succeeding essay.

In this installment, Philip Yaffe introduces a two-step plan to create well-written text that will not only impress the reader, but also engage the reader to digest and comprehend new ideas or concepts with ease.

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Symposium

Developing an Open Source 'Big Data' Cognitive Computing Platform: Big Data (Ubiquity symposium)

March 2018
by Michael Kowolenko, Mladen A. Vouk

The ability to leverage diverse data types requires a robust and dynamic approach to systems design. The needs of a data scientist are as varied as the questions being explored. Compute systems have focused on the management and analysis of structured data as the driving force of analytics in business. As open source platforms have evolved, the ability to apply compute to unstructured information has exposed an array of platforms and tools available to the business and technical community. We have developed a platform that meets the needs of the analytics user requirements of both structured and unstructured data. This analytics workbench is based on acquisition, transformation, and analysis using open source tools such as Nutch, Tika, Elastic, Python, PostgreSQL, and Django to implement a cognitive compute environment that can handle widely diverse data, and can leverage the ever-expanding capabilities of infrastructure in order to provide intelligence augmentation.

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Symposium

High Performance Synthetic Information Environments
An integrating architecture in the age of pervasive data and computing: Big Data (Ubiquity symposium)

March 2018
by Christopher L. Barrett, Jeffrey Johnson, Madhav Marathe

The complexities of social and technological policy domains, such as the economy, the environment, and public health, present challenges that require a new approach to modeling and decision-making. The information required for effective policy and decision making in these complex domains is massive in scale, fine-grained in resolution, and distributed over many data sources. Thus, one of the key challenges in building systems to support policy informatics is information integration. Synthetic information environments (SIEs) present a methodological and technological solution that goes beyond the traditional approaches of systems theory, agent-based simulation, and model federation. An SIE is a multi-theory, multi-actor, multi-perspective system that supports continual data uptake, state assessment, decision analysis, and action assignment based on large-scale high-performance computing infrastructures. An SIE allows rapid course-of-action analysis to bound variances in outcomes of policy interventions, which in turn allows the short time-scale planning required in response to emergencies such as epidemic outbreaks.

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