Assessment of the Information Sciences Directorate at the Army Research Office (2019) / Chapter Skim
Currently Skimming:
2 Computing Science Division
2 Computing Science Division
Pages 11-17
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 11... ...
-sponsored investigators at the University of California, Santa Barbara, have created an automated evasive malware detection method based on real hardware systems that can enhance warfighter capability in cyberdefense. A common shortcoming of current virtual machine-based malware 1 Army Research Laboratory, "Army Research Office: Information Sciences," http://www.arl.army.mil/www /default.cfm?
|
From page 12... ...
The malware analysis system was recently transitioned to the Cyber Systems Division of the Air Force Life Cycle Management Center at Joint Base San Antonio-Lackland in San Antonio, Texas, for field test and usage. Algorithms of feature extraction for explosive hazard detection for countering improvised explosive devices have been developed by at the University of Missouri with ARO funding.
|
From page 13... ...
In the field test, DeepRadio successfully learned the behavior of a dynamic jammer that does not transmit continuously and is hard to detect, using a deep neural network model and providing an over 85 percent success rate of detecting potential jammers. Additional Considerations The projects, PIs, focus, relevance, and results of the Information Assurance Program were exceptional.
|
From page 14... ...
There could be good future opportunities for this research area related to manned/unmanned teaming, robotic and autonomous systems, and robotic swarms. Significant Accomplishments New techniques using compressive sensing algorithms for data reconstruction have been transitioned from Rice University to Conoco-Phillips, a major energy company in the application of seismic sensing for oil exploration.
|
From page 15... ...
The work described on message-passing libraries for big data and on scheduling for graphics processing units was reasonable but very short term; it is likely that similar work is being performed at a much larger scale by major commercial concerns. However, considering the titles of some of the other projects funded under the program but not reviewed, such as the work on approximate computing, and the backgrounds of their PIs, it is likely that some of the architecture work funded in the recent past did have a longer time horizon with a deeper microarchitecture focus, and that work may have held out the potential for developing more fundamental results.
|
From page 16... ...
Appropriately performed, program adjustment, if any, would remain within the bounds of the stated program thrusts: advanced learning theory, methodology, and techniques; and adaptive, robust, and pervasive intelligent systems. OVERALL ASSESSMENT OF THE COMPUTING SCIENCE DIVISION Overall Scientific Quality and Degree of Innovation Overall, the scientific strategy and selection of projects were of high quality.
|
From page 17... ...
The mapping of project accomplishments to programs' strategic plans was not always clear, and consistent, meaningful metrics for assessing progress were generally lacking. The appendix of this report lists a broad set of metrics that ARO could consider for assessment of its programs.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.