2017-2018 Assessment of the Army Research Laboratory (2019) / Chapter Skim
Currently Skimming:
4 Information Sciences
4 Information Sciences
Pages 85-122
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From page 85... ...
ARL research in the Information Sciences Campaign is focused on developing and enhancing science and technology (S&T) capabilities that allow for the timely acquisition and use of high-quality information and knowledge at the tactical edge, for both strategic operations planning and mission deployment.
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From page 86... ...
System intelligence and intelligent systems research at ARL seeks to both understand and exploit interactions between information and intelligent systems, such as software agents or robots. The research portfolio includes a range of topics, from core machine learning, vision, and natural language organization and understanding to integration of information and decision making.
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From page 87... ...
, radar sensing and signal processing, image and video analytics, sensor and data fusion, and machine learning. The nonimaging research presentations focused on electric and magnetic field sensing and on acoustic classification.
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From page 88... ...
Sensor Fusion These research projects included multimodal image fusion and understanding, combined text and video analytics, and multimodal fusion for detection and estimation. The project on multimodal image fusion and understanding applies algorithms to fuse point-cloud and other image data for use in scene situational awareness.
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From page 89... ...
However, this was not uniformly so, and it is advisable that all researchers develop a strong understanding of Army relevance and uniqueness of their work, as well as an ability to communicate this in an effective manner. Such understanding would contribute to developing research problem statements better aligned to needs, and consequently higher impact of successful research endeavors.
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From page 90... ...
Image and Video The research in real-time video analytics is at an early stage, and the object recognition aspects were not completely clear. The research might benefit from a crisper and clear problem statement and methodology approach, and from a stronger, more integrated consideration of object recognition.
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From page 91... ...
Some potentially interesting trends were identified, but the data are sparse, and the result may be context specific. The underlying effort to use cognitive modeling to assist in tactical intelligence data fusion has great midterm potential, and the linkage to experts in cognitive modeling is laudable.
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From page 92... ...
The underlying effort to use cognitive modeling to assist in tactical intelligence data fusion has great midterm potential, and the linkage to experts in cognitive modeling is to be applauded and supported. Reasoning under Uncertainty via Subjective Logic Bayesian Networks Inference is a challenging task in the presence of noisy, sparse, and untrustworthy data.
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From page 93... ...
Challenges and Opportunities It is evident that artificial intelligence and machine learning have been assigned priority as a crucial area, and that ARL is organizing its research portfolio, particularly in SIIS, to address key gaps relevant to the Army. There is specific emphasis in areas including learning with sparse and noisy data, learning in adversarial settings, unsupervised learning, vision, decision making under uncertainty, and natural language processing.
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From page 94... ...
One way to further enhance this awareness might be to create and share relevant data sets. An opportunity exists for SIIS researchers to look for interesting science with applications of machine learning in domains where the Army may have a great amount of labeled data, and perhaps even structured data, such as logistics or medical records.
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From page 95... ...
, and the Institute for Human and Machine Cognition. Social Computing: New Directions for Army Relevance This is a new area of research and is focused on applications in decision making, in establishing context, and in understanding or interaction.
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From page 96... ...
Opinion Formation and Shifting This project seeks to understand how people passively interact with social media, and will contribute to a theory of information propagation through media-enhanced social networks. The researcher is very talented and has good ideas.
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From page 97... ...
This field is extensively researched outside ARL, with a decade of research addressing issues that need to be incorporated into current HII formulations. Social media presents both an opportunity and a challenge, and for HII to be a meaningful contributor, there is a need to define a uniquely Army mission, identify a transition partner, and understand the legal authorities that constrain Army activity in this space.
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From page 98... ...
This would support building toward strongly Army-relevant technology, and support the testing and evaluation of basic work. For every project, the researchers could articulate how this supports the deployed dismounted soldier, and be able to pinpoint the specific Army relevance, the Army issue, and the research they can do that is not being done elsewhere.
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From page 99... ...
This project could benefit from the combination of mentoring in the broader scientific community, and advice from potential end users and colleagues to help identify the specific Army focus and transition path. While the initial results indicate success in working with physiological data, the likelihood of it working with more cognitive or social factors is in question.
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From page 100... ...
Without this mapping, the overall guidelines developed are likely to be too generic. Social Computing: New Directions for Army Relevance This scope is somewhat narrow, and reflective of a strong computer science focus.
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From page 101... ...
They would also benefit by gaining familiarity with the broad literature on information diffusion, misinformation, and fake news. Key factors not considered are the psychology of opinion formation, social influence, constructuralism, network topology and its impact on diffusion, network externalities, information biases, and confirmation biases.
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From page 102... ...
Hill, 2017, Automated aerosol Raman spectrometer for semi-continuous sampling of atmospheric aerosol, Journal of Quantitative Spectroscopy & Radiative Transfer 188:103-117.
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From page 103... ...
ARL collaboration on this project with the University of Southern California Institute for Creative Technologies is noteworthy. The project has clear Army relevance, providing soldiers with the ability to plan and rehearse dangerous missions in complex terrain and urban areas.
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From page 104... ...
at WSMR will enable unprecedented continuous examination of atmospheric phenomena crucial to our understanding of atmospheric flows over complex terrain at high horizontal resolution. In addition to fixed instrumentation, it also a mobile component (lidar)
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From page 105... ...
The challenges and opportunities to BED programs are interrelated. First, BED's thrust areas touch nearly every S&T campaign outlined in the Army Research Laboratory S&T Campaigns 2015-2035.
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From page 106... ...
Atmospheric Boundary Layer Environment Model Lattice Boltzmann Method Future work includes improvements in turbulence model coupling as well as radiation model and surface thermal model coupling, continued validation using laboratory and field observations, and initialization with larger-scale model data and lidar observations. Visualizing Terrain in Augmented Reality The researcher was directed to a study published by Hembree et al.
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From page 107... ...
in a geographic region. The use of social media data is a good first step, and more sophisticated data sets and social media models are available in the research literature; future work could consider the role of adversarial actors as potential corruptors of social media data.
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From page 108... ...
The researchers could also consider patenting their active antenna if they have not already done so. Networking in Resource Constrained Environments This is a large project that began in fiscal year 2010 to develop networked communication devices and network protocols that meet Army needs of energy-efficient communication systems that operate under a wide variety of conditions.
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From page 109... ...
The models and proposed approaches include consideration of operating environments that are relevant to Army missions. A clearer indication of practical implementation of the research in Army operations would be helpful.
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From page 110... ...
Many of the projects in the networks and communication area use data sets to test and improve the research approaches and solutions. Some data is representative of Army mission scenarios, but a significant fraction of the data being employed is based on academic collections or benign social media data.
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From page 111... ...
It was unclear how the current focus on properties of stationary networks would address the goal of understanding dynamic networks. Linking the problem statement to topics that have Army relevance and consider topologies aligned to Army operational scenarios would improve the impact of this program.
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From page 112... ...
This research could be enhanced by considering models in which human opinions are embedded in opinion networks and hierarchies, and in which pro and con opinions simultaneously propagate, and in which there is no ground truth. These modifications would improve the ability to model decision making in Army-relevant scenarios of interest, and thereby improve the Army relevance and potential for transition.
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From page 113... ...
The labeled data sets will represent ground truth to be used for evaluating the effectiveness of vendor tools that profess to automatically label data sets. This project is a good example of applied research in support of Army operational requirements.
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From page 114... ...
It illustrates the successful adaptation of a solution strategy from one problem domain into another. Cyber-CAMO: Mission-Based Cyberdefense and Resilience for Tactical Networks This research project seeks an approach to active defense by replicating a number of nodes in order to avoid single points of failure.
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From page 115... ...
ARL needs to utilize these resources to the largest extent possible in its cybersecurity research programs, as they provide nearly the only ground truth in research related to cyberattacks. It is encouraging to see research specifically focused on providing greater access to that data, both internally and externally.
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From page 116... ...
However, high demand, coupled to scarcity of cybersecurity professionals, will likely challenge ARL to retain and recruit the best and brightest professionals in this field of work. Adversarial Influence of Machine Learning on Active Cyberdefense Adversarial ML is an important area, as recent work in the broad ML community has demonstrated.
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From page 117... ...
Intelligent Rapid Honeynet Generation for Mission-Based Cyberdefense and Resilience Systems The proposed approach presumes that the attacker cannot use a foothold within the network to undermine the inputs to the game-theoretic model, thereby compromising the defender's optimal strategy. As an example, the attacker could gain access to a poorly defended unimportant node (e.g., a minor sensor)
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From page 118... ...
The research generally reflected a good understanding of the problems being considered, appropriate statement of the problem being pursued, knowledge of the appropriate methodologies to address the problem, and a good knowledge of the state of the art and the relevant research pursued elsewhere. In many cases, the researchers are able to articulate Army relevance and unique aspects to Army needs when that was the case.
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From page 119... ...
The projects were highly variable in the extent to which they had identified the relevant computational and social theories. For the nascent research projects, there is the need to develop a stronger Army research focus and to use this focus to narrow the scope of work as a path to realizing meaningful results.
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From page 120... ...
Recommendation: ARL should actively encourage researchers to take on research problems of greater complexity and scope. The research in SIIS is appropriately aligned with the emergent priority accorded to the fields of artificial intelligence and machine learning, and is seeking to address key gaps relevant to the Army.
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From page 121... ...
The networking and communication area continues to address basic and applied research to advance technology related to the understanding of dynamical behavior of networks and the interactions between information and social networks. The research emphasizes development of technologies that apply to unconventional networks that will employ heterogeneous approaches to encoding and transmitting information, with a focus on robustness and survivability in harsh operating environments.
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From page 122... ...
This gives ARL an ability to focus on research that has an immediate bearing and impact. All of the research projects reviewed showed suitable scientific rigor and practice.
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