2017-2018 Assessment of the Army Research Laboratory Interim Report (2018) / Chapter Skim
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4 Information Sciences
4 Information Sciences
Pages 54-77
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From page 54... ...
The primary essential research areas (ERAs) reviewed includes artificial intelligence and machine learning (AI and ML)
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From page 55... ...
Sensor fusion research included multimodal image fusion and understanding, combined text and video analytics, and multimodal fusion for detection and estimation. Machine learning research was used as a tool in several of the S&E projects.
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From page 56... ...
Research is also being conducted to develop algorithms to model and to learn human actions and activities from image sequences or video streams. Sensor Fusion These research projects included multimodal image fusion and understanding, combined text and video analytics, and multimodal fusion for detection and estimation.
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From page 57... ...
was not very clear, and Army relevance and impact was hard to gauge. The acoustic classification research is considered as a collaborative project between the sensing and effecting and battlefield environment groups.
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From page 58... ...
The SIIS research portfolio also includes work related to text analysis, language understanding and dialogue, information integration, and decision making. The approach of collaborating with researchers throughout ARL as well as on the outside to develop a continuum of work, ranging from information analysis (in SIIS)
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From page 59... ...
Some potentially interesting trends were identified, but the data is 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 60... ...
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 61... ...
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 62... ...
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 63... ...
Research into interaction requires expertise in collaboration, team dynamics, social influence and other social science subareas. Interaction is at the center of battlefield dynamics, the expanding use of social networks for communication, and information dissemination, and it enables the dismounted soldier to attain and maintain extensive situation awareness.
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From page 64... ...
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 65... ...
Social media and diffusion of information is of interest to HII, specifically as it relates to the role of social media in information diffusion and propagation of false information. This field is extensively researched outside ARL, with a decade of research addressing issues that need to be incorporated into current HII formulations.
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From page 66... ...
To accelerate progress in the crucial areas of team dynamics, collaboration, and social influence, the ARL could invest in additional social scientists to balance its strengths in engineering and computer science. Specifically, a mathematical sociologist and an experimental social psychologist could help significantly with ongoing and proposed work.
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From page 67... ...
Understanding Theoretical Information Interaction: The Development of a Standard Model Using an Agent-Based Modeling Framework The objective of this research is to address challenges in developing fundamental theories of human information interaction. This project is central to HII, but needs to be better formulated.
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From page 68... ...
Modeling and Analysis of Uncertainty-Based False Information Propagation in Social Networks This work proposes an opinion model based on subjective logic and then uses it to study how to mitigate the impact of false information using counter-narratives. The model is mathematically well specified but operates at a very abstract level.
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From page 69... ...
This needs to be controlled in the experiment. ATMOSPHERIC SCIENCES The research portfolio of the Battlefield Environments Division (BED)
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From page 70... ...
This set of simulations sought to evaluate combinations of PBL and surface physics parameterizations in order to determine which combinations produce the most accurate representations of the PBL in complex terrain. Vortex Filament Method in Microscale Atmospheric Modeling Researchers have pursued three distinct approaches for the atmospheric boundary layer environment (ABLE)
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From page 71... ...
It is worthwhile to note that this project and the VFM have benefited greatly by collaboration with researchers from multiple universities and other ARL directorates. Visualizing Terrain in Augmented Reality This project relates to 3D visualization of terrain and ground cover, and is illustrative of collaboration between BED and other Information Sciences Campaign researchers.
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From page 72... ...
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 73... ...
First, BED's thrust areas touch nearly every S&T campaign outlined in the Army Research Laboratory S&T Campaigns 2015-2035. Although battlefield environment and weather are technically assigned to the sensing and effecting area within the Information Sciences Campaign, the atmosphere and its effects can impact vehicle maneuver, lethality and protection, human sciences, and materials science -- all of which are campaign areas in the ARL S&T plan.
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From page 74... ...
However, the project could benefit from increased collaboration with other BED subject matter experts for an improved integration with the atmospheric components. OVERALL QUALITY OF THE WORK The research portfolio in the Information Sciences Campaign reviewed in this current cycle was expansive, and covered areas spanning sensing and effecting, system intelligence and intelligent systems, human information interaction, and atmospheric sciences.
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From page 75... ...
In work related to atmospheric sciences, the overall scientific quality of the work is very good, and quite comparable (and, in a few cases superior) to research conducted at successful university, government, and industry laboratories.
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From page 76... ...
These types of approaches are commendable and need to be continued by the BED leadership. CONCLUSIONS AND RECOMMENDATIONS The research portfolio in information sciences reviewed in this current cycle was expansive, and covered areas spanning sensing and effecting, system intelligence and intelligent systems, human information interaction, and battlefield effects/atmospheric sciences.
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From page 77... ...
In this context, it would be helpful both to send scientists to leading conferences in the area and to organize internal seminars and workshops in advanced areas such as ABMs, information diffusion and the spread of misinformation, and social media analytics. Last, it is noted that the work in HII is somewhat hampered by not having access to all state-of-the-art tools and software.
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