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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Information Sciences

The Panel on Information Science at the Army Research Laboratory (ARL) was charged with reviewing ARL research in the broad areas of information sciences—network sciences, cyber sciences, sensing and effecting (S&E), system intelligence and intelligent systems (SIIS), human and information interaction (HII), and atmospheric sciences. A 2-year cycle of review has been adopted for this purpose with the focus in 2017 on reviewing activities in SE, SIIS, HII, and atmospheric sciences.

ARL research in Information Sciences 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. Included in this approach are technological advances that support information acquisition, reasoning with such information, and support to decision-making activities including collaborative communications. The research is expected to generate new technology to both manage and effectively use information flows in the battlespace of the future. Research in these areas falling under the broad categories of SE, SIIS, HII, and atmospheric sciences were reviewed in June 7-9, 2017, at Adelphi, Maryland. This chapter provides an evaluation of that work.

Research in sensing and effecting is organized around understanding of sensing capabilities and exploiting information gained through sensing to drive effectors. Research in this area focuses on understanding the sensed phenomena, including the effects of the environment on the phenomena. Both sensing and effecting necessitate detailed understanding of corresponding physical behaviors that generate and utilize data, an ability to extract relevant information from sensors of different modalities, an understanding of the complex and variable environments in which these sensors operate, and an ability to combine information from multiple modalities and other contextual and environmental information. Research in effecting focuses on the understanding of transforming information into physical events, and provides insights into the nature and characteristics of related phenomenology, thereby helping design effectors that provide the desired physical effects. This work includes development of advanced algorithms to improve signal-to-noise ratios for all sensing modalities. The overarching goal of sensing and effecting is to deliver relevant information to decision-makers in a timely manner to facilitate effective action.

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. The primary essential research areas (ERAs) reviewed includes artificial intelligence and machine learning (AI and ML) and human-agent teaming (HAT). An important consideration in this work is to think of information as data in context, and to use this data to develop automated intelligence in perception, reasoning, planning, and collaborative decision making, with applications in cyber virtual environments or decision support systems. Research in this area complements efforts being undertaken in the Sciences for Maneuver Campaign involving intelligent systems concepts applied to vehicles or robotic platforms.

The Army faces significant and growing challenges in the area of human and information interaction. This is an extremely broad area with obvious linkages to human sciences and other information sciences

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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programs. The growing use of unmanned vehicles and robots and the expanding influence of social media in planning and operations are creating a challenging communications environment for the Army of 2040. The need for effective communications between humans and machines, often without the ability to determine whether the communication is from and to a human or a software agent, introduces additional degrees of complexity. The need to operate effectively in this environment is creating a need for a new transdisciplinary science of HII. This is a crucial area for ARL basic research because many of the core issues are deeply intertwined. Mission requirements such as the need for diverse teams to operate under extreme time pressure in unpredictable, resource-constrained battlefield conditions are not a consideration in most academic research. This is an area in which ARL has a strong potential, and an imperative, to lead in research. Of course, as they do this, relevant academic work need to also be monitored and exploited.

The characterization of the battlespace environment and prediction of optimal conditions for engaging an adversary with overwhelming force present significant challenges for the Army of the future. The Army is likely to find itself engaged in a number of challenging environments, from complex terrain to sprawling urban areas. There is a need, therefore, to characterize these diverse environments and develop accurate, relevant, and timely predictions of their future state on spatial and temporal scales useful to Army operations. The related need to collect and process accurate, relevant, and timely environmental characterizations in austere conditions, and translate that data into actionable environmental intelligence for field commanders will also pose new challenges to the computational sciences community. The research being conducted by the Battlefield Environments Division is addressing these challenges, and includes a mix of analytical, computational, and experimental projects. There are clear linkages of the individual research projects to the nine ERAs considered essential to the success of Army operations in the 2030-2050 time frame.

SENSING AND EFFECTING

Accomplishments and Advancements

Research projects in sensing and effecting covered thematic areas of nonimaging sensors (acoustic, electric, magnetic, seismic), 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. Radar sensing included advancements in landmine and improvised explosive device (IED) detection, adaptive radar transmit-waveform selection, and radar imaging in congested spectrum environments. Image and video understanding research included cross-modal face recognition, real-time image object recognition, and human action learning and recognition. 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.

The research portfolio contains a mix of basic and applied research, and some of the projects show clear potential for transition into Army applications and products. Noteworthy programs include electric and magnetic field sensing, research on the next-generation IED and landmine detection platform, computational advances in electric field modeling, cross-modal face recognition, along with the development and dissemination of a cross-modal face recognition data set to the academic research community, and innovative approaches to fuse textual context with image features to improve machine learning of human activity.

More details on some of the projects reviewed are provided in the following.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Nonimaging Sensors

This area of research included projects in modeling of electric and magnetic fields, electric and magnetic field sensing technologies and exploitation algorithms, and acoustic classification algorithms. The research on electric and magnetic field modeling represents high-quality algorithmic development, and seeks to apply computer science expertise to an electromagnetics problem. The research program contains a healthy mix of fundamental work related to understanding of phenomenology, sensor development and assessment, and algorithm and application advancements. The work reviewed was of high quality, and this group provides national leadership in this important area. Mentorship and development of early-career researchers is especially strong, and it is clear that ARL is nurturing the next generation of talent in this area.

Radar Sensing

Projects in this category included research in radar imaging and in adaptive transmit waveform selection in congested radio frequency interference (RFI) environments, and on further development of standoff landmine and IED detection. The research on radar imaging in RFI environments builds on a well-established program at ARL, and has potential to transition into operational use. The research on optimal transmit waveform selection in an RFI environment is in early stages, and is addressing an important problem that will enable radar sensing as the RF spectrum becomes increasingly crowded and complex. The research on a vehicle-mounted radar sensor for landmine and IED detection is advancing the capability in multisensor design and phenomenology, and is addressing important technical challenges needed for transition into operation.

Image and Video

Research projects in this grouping included cross-modal face recognition, real-time image object recognition, and human action learning and recognition. The cross-modal face recognition research is advancing the capability of facial recognition by comparing images collected in one modality with a catalogue of facial images from another modality. Researchers collected and disseminated a data set to the academic research community to facilitate development by others and grow collaborations with ARL. Research on real-time image object recognition is in the early stage, and seeks to develop algorithms that could be deployed on video sensors to automatically detect objects of interest from video streams. 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. The project on multimodal image fusion and understanding applies algorithms to fuse point-cloud and other image data for use in scene situational awareness. Advancements in improving robustness in fusion of data from coarse-grained and fine-grained sensors, such as acoustics and imagery, were presented. Research attempting to improve human activity classification using text and linguistic information is in an early stage of development. The research on cross-modal face recognition is strong and the data collection and dissemination efforts are commendable. The research on fusing point clouds with imagery was of good quality.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Machine Learning

This research included decentralized estimation and learning methods; in addition, machine learning was used as a tool to support several of the projects described in other areas discussed earlier. The distributed learning research was considered to be of high scientific quality. The demonstration was on a “toy” problem. It will be useful to assess the research as it matures to the point where it can be demonstrated on a relevant problem in an experimental test-bed.

Challenges and Opportunities

While the overall quality of the research was considered as strong, it was not uniformly so across all presentations. Some researchers did not demonstrate good understanding of the existing state of the art. As a result, these presentations lacked a clear connection to existing research, and in some cases, it was not clear why existing techniques were inadequate to address the problem at hand. Researchers were generally able to articulate those aspects of their work that made it unique to Army needs and had Army relevance. 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. Applied research programs would benefit from clear transition planning that includes mission fit to a present or envisioned Army need. Basic research programs would benefit from at least some early consideration of this relevance.

The research staff at ARL is well qualified and equipped to take on problems of increased complexity and challenge. To this extent, the research problem statements in some instances were less ambitious than they could be. This was noted more often in cross-disciplinary endeavors. ARL has in its existing workforce, either within the S&T portfolio or in the larger ARL cadre, the research talent and expertise to formulate and pursue challenging research goals. Researchers pursuing such cross-disciplinary projects are encouraged to establish collaborations and mentoring relationships with experts in allied areas early in the problem formulation stage.

The posters, presentations, and oral summaries were generally good in providing sufficient details about assumptions and experiments to assess the quality of the work. Some presentations and posters lacked important detail about assumptions being made, and details about the experimental design setups that led to the reported performance results. It was in some cases difficult to understand what experiment was conducted and what significant observations or conclusions could be drawn from the results. Research impact and understanding would be improved by additional attention to these descriptions in the posters and oral presentations.

Specific opportunities and challenges for some of the projects reviewed are discussed in the following.

Nonimaging Sensors

The relevance of the work to the ARL Key Campaign Initiatives (KCIs) and core campaign enablers (CCEs) 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. Tighter collaboration and integration with the battlefield environment researchers would yield enhanced impact. The performance gains might be improved with a more integrated consideration of propagation physics in the sparsity models.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Radar Sensing

The research on radar algorithms in spectrum-congested environments would benefit from a stronger development of a transition strategy; this would help to focus the research to ensure addressing transition-relevant research issues. Additionally, with the rapidly shrinking radio frequency (RF) spectrum available to radar, the research would benefit by considering transmit waveform adaptation to effect power notching as an approach to avoid interference with communication bands. The cognitive radar research has opportunity for stronger impact if a more ambitious problem statement were adopted. In particular, more general waveforms that use more of the available spectrum could be considered. This research could consider sensing the available spectrum on both transmit and receive, and leveraging the cognitive radar research conducted by Defense Advanced Research Projects (DARPA) and other Department of Defense (DOD) service laboratories.

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. In addition, a comparison to semi-supervised learning using convolutional neural networks could improve the impact of the research. The research on human action recognition would benefit from a clearer presentation of the mathematical construct, experiment design, and experimental results.

Sensor Fusion

The research on cross-modal face recognition would benefit from consideration of larger subject database sizes, and also from the inclusion of battlefield environment atmospheric effects (such as dust or smoke) on recognition performance. As regards the research on fusing point clouds with imagery, other research groups, both within ARL and within the Night Vision Laboratory, are considering related problems, and researchers are encouraged to understand and leverage other similar research in this area. The research on improved multimodal fusion would benefit from a clearer articulation of the problem statement, and by framing the research approach more clearly in the context of existing research literature on the topic. It was difficult to assess how the proposed approach yielded the stated performance gains.

SYSTEM INTELLIGENCE AND INTELLIGENT SYSTEMS

Accomplishments and Advancements

SIIS research has produced key results in areas relevant to Army needs, including the understanding and analysis of complex environments and streaming data, navigation, exploration, and mapping of the physical world. The work on unsupervised learning of semantic labels in streaming data, and the synergies between visual analysis and efficient exploration of environments is noteworthy. It has identified gaps in the state of the art and is being disseminated in leading venues.

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) to decision support (in HII), will yield good dividends. The research on integrating event ontologies to increase the coverage of events provides near- to mid-term utility. In the longer-term, approaches that combine curated resources and more data-driven approaches as well as integrating text

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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and video could provide added benefits. Research on natural language dialogue to support human-agent interaction, and agent intervention for decision and information integration, is important in supporting efficient communication and information analysis. Much of this work is focused on novel methods to collect data to support the design of such systems, and results were preliminary but promising.

The sample of research presentations and posters reviewed in this cycle are in broad subject areas of information understanding, information fusion, and computational intelligence. A summary of their assessment follows.

Ontology for Event Understanding

A critical issue for analysts and decision makers is to rapidly and accurately identify events of concern in documents. An important component of such an information extraction data pipeline is an ontology, and the ongoing work explores representations that could be used to encode and integrate previous ontologies to obtain better coverage. The research seeks to evaluate how the integration affects information and decision tasks, and is of good quality.

Knowledge Representation and Modeling for Understanding Information Amalgamation

This work seeks to use knowledge representation to define a computational model of the value of information (VoI). In previous research, the team developed a fuzzy logic-based approach to model a VoI metric across various information characteristics (e.g., reliability of source and credibility of content) and mission contexts. The current research extends this by investigating how analysts combine complementary and contradictory information. 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.

Toward Natural Dialogue with Robots: BOT Language

This project seeks to advance the state of the art in natural language dialogue processing to support human-agent interaction in the context of a collaborative search-and-navigation setting. A novel “Wizard of Oz” methodology was developed to collect a corpus of naturally occurring variation in language in this setting. Initial results have helped in understanding of dialogue structures in these settings.

Air-Ground Robot Team Surveillance of Complex 3D Environments

The focus of this work is to improve autonomous surveillance via robots, with a human operator helping to focus attention on relevant areas. Starting from the assumption of a known map, the robot chooses a plan that is within a resource budget and maximizes coverage. The human operator can indicate one or more regions of interest, and the robot adjusts its plan to prioritize these areas. This effort has advanced beyond simulation, and the evaluation is being done with real robots in interesting environments. Future work on this project includes the inclusion of additional constraints, including the ability to select between ground or air vehicles.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Parsimonious Online Learning with Kernels

This high-quality work is focused on an important subset of learning techniques that are relevant to Army needs. In particular, the research is focused on the “5 D’s” (dynamic, dirty, deceptive, dinky data), and the approach is predicated on the use of online, parametric, kernel-based techniques. Results included an evaluation for the proposed approach based on simulations, as well as some work toward a target application predicting slip or oversteer based on images of the surface. The researcher was effective in providing context for the work and identifying limitations.

Knowledge Representation and Modeling for Understanding Information Amalgamation

The objective of this work was to use knowledge representation to define a computational model of the VoI. Previous research had focused on the development of a fuzzy logic-based approach to model a VoI metric across various information characteristics (e.g., reliability of source and credibility of content) and mission contexts. The current research extends this by investigating how analysts combine complementary and contradictory information. 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 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. This work introduces subjective belief propagation, a new technique that extends belief propagation to efficiently infer uncertain marginal probabilities over subjective Bayesian networks. This is a promising technique to provide robust inference over uncertain relations.

Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph

This research is focused on reasoning with brittle and incomplete information in knowledge bases, and combines deductive and analogical reasoning and association by mapping information into a vector space, and using the geometry of the space to find “related” paths. Previous work has looked at vector relations in embedded knowledge representations, but none has combined this with deductive reasoning. The ability to combine symbolic and continuous representations is a promising direction.

Unsupervised Semantic Scene Labeling for Streaming Data

The goal of this work is to augment visual perception by automatically extracting semantic groupings of objects found in video. This project was particularly strong, both for Army relevance and in its execution. In particular, the use of unsupervised techniques means that the approach does not require tedious labeling, a difficult task for video. Furthermore, the approach is applicable to long-running video without requiring excessive storage. To restrict the propagation of error introduced by unsupervised learning, the approach incorporates an ensemble gained from training the model on a sliding window of frames with periodic resets. The work compares favorably to previous techniques such as over- and undersegmentation entropy and graph-based techniques. Results of this research have been published in papers presented in top vision conferences.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Autonomous Mobile Information Collection Using Value of Information Enhanced Belief Approach

This research focuses on a framework for planning and optimization in multirobot environments. By using a model based on partially observable Markov decision processes, and by using the VoI to help prioritize, the approach is expected to pick better plans to simultaneously meet mission objectives and maximize information about the environment. The research is at an early stage of development, and a simple prototype simulation has been used in early investigations and described in a paper presented at this year’s International Conference on Robotics and Automation (ICRA), a top-tier conference in robotics.

A Crowd-Sourced Integration of Intervention Thresholds for Decision Agents

This research project seeks to develop an approach for supporting human decision making with automated agents. A new methodology was developed in which a single-player game was developed to help an analyst solve an information-based problem that was deployed on Amazon Mechanical Turk (AMT) with hundreds of participants. The primary outcome seems to be building experience using crowd-sourcing platforms for these experiments, and an initial indication that agents can help support these decision-making tasks.

Robot Demonstration

A robot capable of autonomous navigation, exploration, and mapping was demonstrated. The robot was tasked with mapping a complicated space via light detection and ranging (LIDAR), inertial, and odometry-based sensors. The on-board algorithm was designed to select an optimal pathway to map the interior, and was sufficiently capable to avoid dead-end paths that might impede its task. Additionally, a user could draw a polygon representing a region of interest, and the robot would dynamically adapt its plan to map the desired space. This demonstration was an effective illustration of several ARL technology thrusts integrated in a single robotic platform. It was nice to see an end-to-end demonstration of the Army technologies of high relevance working together. The laboratory space where the demonstration was staged represents an effective resource for researchers from all over ARL, as well as academic open campus partners. In particular, the support for reconfiguring the space, the ability to fly small unmanned aerial vehicles (UAVs), global positioning system (GPS) denial, and calibrated measurement equipment (e.g., the VICON camera system) are all features that make the space valuable for ongoing research.

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. The research projects are appropriately resourced. Bright early-career researchers have been hired, and there appears to be a healthy cross-disciplinary integration. Overall, the facilities and computational resources seem adequate. The facility conducting experiments with robots, UAVs, and humans is especially impressive. This facility will help attract staff and visiting researchers to the laboratory. Engagements with universities seem to be a great source of collaborations and new talent. It will be important to continue to invest in this area, both internally and especially with university and industrial partners, where the pace of development in this area is extremely rapid. ARL plans for establishing the Intelligent Systems Center (ISC) to facilitate open campus collaboration and unification

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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of effort to take intelligent systems from tools to teammates are an indicator to grow and promote partnerships.

There is some concern about researchers being isolated from each other, as well as the outside world. There was some evidence that SIIS researchers within ARL were not always aware of similar or parallel efforts. This is not unique to ARL, and all government laboratories generally can benefit from closer interaction with industry and universities around Army-relevant needs. 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. There may be immediate gains in the application of methods that have been successfully used by industry (e.g., deep neural nets). Engaging with such applications will help keep SIIS researchers current on big data tools and methods developed elsewhere.

Specific opportunities and challenges for some of the projects reviewed are discussed in the following.

Ontology for Event Understanding

In addition to evaluating the impact of extended coverage on an end-to-end pipeline task, an important future direction would be to integrate curated ontologies with more data-driven approaches.

Toward Natural Dialogue with Robots: BOT Language

Moving from a controlled experimental setting to one with operational components would accelerate data collection and research progress. In addition, it might be interesting to consider the integration of language with richer multimodal signals (vision, mapping).

Reasoning under Uncertainty via Subjective Logic Bayesian Networks

Generalizing the work to realistic applications will require a number of advances, such as moving from binary to multinomials, inference over directed acyclic graphs (as opposed to trees) and general graphical models.

A Crowd-Sourced Integration of Intervention Thresholds for Decision Agents

The findings were preliminary, and would benefit from a more detailed statistical analysis of the relationship between decision making and agent guidance, and a broader framing of the decision-making options. The work has been published in defense-based sensing venues, and might benefit from a broader AI or decision-making setting.

HUMAN AND INFORMATION INTERACTION

Accomplishments and Advancements

Human and information interaction (HII) is a new program in the Information Sciences Campaign, and has been in operation for about one year, bringing together researchers from disparate disciplines and technical backgrounds. “Interaction” is what distinguishes HII in the information science and human

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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science research space. It applies to the interaction of groups of individuals and agents with information, as well as individual discovery, creation, and use of information. 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. With a focus on the dismounted soldier, issues of land-based real-time interaction rather than future forecasting are paramount. The objective of HII research at the ARL is to develop models, methods, and understanding of data and information generated by humans and intelligent agents in a complex, multi-genre network environment. It further examines tools to respond to user information needs with due consideration of user variability and mission constraints, and thereby to develop timely and accurate situational understanding.

More details on some of the projects reviewed are provided in the following.

Multimodal State Classification

The research addresses an important problem of human state classification. It focuses on the use of machine learning to classify whether a user is in a threatened or challenged state based on wearable sensor data. The research focus resides in comparing and contrasting multiple modalities and multiple methods of classification, and provides breadth to the research while ensuring broader strategic relevance. The potential of the research is high, and the investigator demonstrated understanding of how this work fits into the broader scientific community.

Exploiting and Mitigating Cognitive Effects of Real-World Luminance Dynamics on Visual Search

This research explores the use of deep neural networks to predict human search behavior under dynamic range stimuli for images and video by illuminating the complex patterns of interaction that impact search. Specifically, the focus is on developing models of visual search behavior in dynamically changing, high-luminance environments such as those available in high-definition resolution imagery and videos. The work is in collaboration with the Human Research and Engineering Directorate at ARL.

Assessing Value of Information for Graph Understanding

The focus of the work is in developing metrics to measure the fitness of statistical graphs that better inform the design of Army displays, and to develop automated techniques for generating task-dependent statistical graphs. There exist some human-factors guidelines for generating graphs and charts, and the use of these guidelines has been shown to yield improved performance. The work has clear alignment with Army needs and represents collaborative work between ARL, the Naval Research Laboratory (NRL), 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. The scope of research tools and technologies is quite broad, and will require input from many disciplines. The program is in a formulation stage, seeking to establish appropriate research directions in social computing relevant to Army needs, and to coordinate disparate efforts in the field across ARL. Machine learning and graph theoretic analysis have been identified as key technologies to pursue in this context.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Intelligent Information Management for the Battlefield

This user-focused research is of high quality, has a clear articulation of the associated scientific and technical challenges, has clear connections to Army needs, and presents a transition path and partner. The researchers demonstrated strong understanding of what work is being done in the broader scientific field and how their work fits into that framework. As such, the researchers know what community to address and where to publish.

The work is creative, and the data and methods are appropriate to the objective. The researcher was able to gather and make clear utilization of end-user feedback. A strength of this work is the use of live experiments to gather data, shape the research objectives, and test the overall approach. The demonstrations and live experiments provide a test-bed and framework for VoI research that lays the groundwork for other related projects in the HII area. This work has the potential to develop context, user-adaptive information management, and processing techniques that will support the dismounted soldier in tactical environments.

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. The collaboration within ARL is good. Using a set of experiments, the investigator is seeking to derive empirical thresholds for opinion formation and shifting given diverse types of media. This is clearly of Army relevance. The investigator has solid scientific credentials, is good at designing experiments, and would be well served by mentors from the area of sociology or social psychology of opinion formation.

Internet of Battlefield Things

The complex cyber physical environment that will characterize the battlefield of the future has defined the Internet of Battlefield Things (IoBT). The vast amount of information available from networked things in IoBT offers endless possibilities of using analytics to support military planning and operations. This will require humans to interact with intelligent agents in a variety of ways. The research project seeks to understand the dynamics of human-agent interaction, and to build models of such interaction so as to simulate different battlefield scenarios to develop optimized strategies for dynamic resource and task allocation across human and agents in IoBT. Toward this end, the approach is to formulate an optimization problem based on a cross-layer (Com/Net, actuator, human layers) mathematical model for optimal resource allocation. This project has clear Army applications and seeks to collaborate with researchers in the NRL. Developing constraints reflective of human knowledge and perception will certainly enrich the optimization problems but the approach for doing so was not presented.

Challenges and Opportunities

The HII agenda at ARL is very broad, and without a dramatically narrowed focus, it will be difficult to make sustained progress that will transition to Army-relevant applications. A key challenge, therefore, is to leverage the deep understanding of Army needs and requirements to narrow the focus and to reorient new projects. In a number of the projects, the research questions are too broad, and are not designed to lead to actionable and transitional results in the near term. In cases where research is linked to Army needs, the specific Army issue is not clearly articulated in a way that drives the research question and the

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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methodology. The approaches used are often not well matched to the available data or to the research question. A strong scientific mentoring program is needed to ensure that promising projects realize their potential.

In response to suggestions from the 2015-2016 ARLTAB assessment, ARL has recruited two psychologists into the HII program and is pursuing the recruitment of an economist. Although this initiative is to be lauded, it is important to recognize that talent with necessary scientific expertise in the areas of social, team, and group behavior and communication is needed to support the HII projects. Data scientists and information scientists have been hired, further strengthening the computer science focus. The research staff distribution continues to require additional balancing to bring greater understanding of social theoretical underpinnings of the research.

HII has begun to identify synergies with other parts of the ARL, and may benefit from creating a “social network model” of ARL, identifying those individuals whose work is synergistic with their own. There is untapped potential to coordinate across areas concerned with the social side of human behavior, and the interaction with other agents and information. The creation of a challenge problem could be a way for focusing HII efforts. Although a challenge problem has not been identified, HII researchers are engaged and active in discussing what one might be. Mentoring, particularly matching researchers to others with scientific experience in the methods or theories of relevance, needs to be a priority for ARL.

From the work that was reviewed, it appears that there are three areas of research around which multiple projects can be organized, and where closer ties among projects would be a force multiplier. These areas are VoI, social media and diffusion, and agent-based modeling. There is a strong focus on VoI, and this is the most mature of the concentration areas. Projects related to this area could develop and adopt a common use case, share data, and develop common formulations of VoI. The HII research needs to expand to recognize the impact of context. Whether the information is of a socio-cultural origin or technology related, the VoI is contextual. Taking context into account, such as the physical climate and social environment, is a major challenge and opportunity. A focus in this arena has the potential for elevating HII as leaders in the SBP-BRiMS1 community.

The other two areas are broadly part of social computing. Social computing brings in research from over 20 scientific disciplines, most of which are not represented in HII at ARL. Despite the commendable step of organizing a workshop, the HII group is still at a beginning point on the learning curve in this area. Social computing is an area that is growing exponentially. The HII group is beginning to embrace a small sector of this community, and could task itself with monitoring the larger social computing field as it develops. To stay actively engaged, the group could identify Army specific problems that it needs to pursue, and invest in the personnel qualified to pursue the work.

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. 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.

The projects making use of agent-based modeling have strong potential but need to be more tightly focused on Army needs. The value of agent-based models (ABMs) is that they support prediction and forecasting. Since HII is more focused on near-term analytics rather than forecasting, the aspect of ABM that supports forecasting is not needed. Another value of ABMs is that they support theory development, and can be used to fine-tune field experiments. This aspect of ABMs has strong potential for the HII program. The ABM expertise within HII needs to be enhanced to achieve greater progress.

It is important to underscore some disconnects that may detract from the impact of HII research. First, there is a disjuncture between work being done, the stated goals and objectives, and the perceived time-to-

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1 SBP-BRiMS stands for Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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get-to-that-objective. As an example, HII goals include operation in a complex multi-genre context and understanding information content. Much of the research, however, considered only simple or single-genre contexts and ignored content. Another example relates to the time frame allocated for a research task. Social dynamics is meant to be a “distant goal”—that is, one that will take more than 15 years. However, some HII researchers working in that area anticipate success in 1-3 years. This would be acceptable if the ongoing work was research needed to enable the longer term goal; that does not, however, appear to be the case. Shifting to the research program organization, it is to be noted that the HII program overview mentioned beliefs multiple times, but the specific work that is most closely associated is focused on trust and VoI. Trust and VoI are not the same as beliefs, particularly as they have been operationalized. Either the goals need to include trust or the research could be shifted to beliefs.

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. A systematic effort to survey this expanding area to identify specific Army-relevant subtopics and research results, and the groups working in this area, could also be a priority.

The HII group could develop one or two challenge problems. To support the challenge problem(s), the HII could consider a unifying integrated test and evaluation platform that is relevant to that challenge problem. 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.

As noted earlier, the scope of the research program in HII could be further narrowed and refined for maximal impact. One possible area for focus is a framework that defines how a joint human-machine system selects a level of data resolution for effective interaction. A key issue in this task is to understand what role the human operator needs to play in this selection and what is best relegated to automation. There is no theory or framework to address this problem in a complex dynamic environment with teams of humans and autonomous systems. A good theoretical understanding of this problem will be important, because in the long run, the Army will need to transfer data at different levels of resolution for cultural information, social network information, and general social informatics. HII has the opportunity to establish leadership in core scholarship in this arena.

A second area to consider is a concerted focus on problems involving teams of three or more interacting agents (e.g., two humans and one artificial). Currently, several HII researchers are focusing on problems related to one human and one artificial agent. Fundamental questions to be answered in larger team interactions include what enables high performance, how much information overlap is needed, and what kind of transactive memory and knowledge memory do they need to develop. These questions assume increased importance as the team size is increased, or as team members come from different services or from different countries. Additional scenarios need to be considered where information cannot be shared with all team members, and where there is impact of these omissions on performance. Another fundamental question to be addressed is how the mode of communication—video, image, voice, and so on—impact team performance.

A third area for consideration deals with the issue of the commander’s intent, and how intent can be transmitted so it does not get distorted. It would be important to understand what factors cause distortion, and how coordination and interaction among soldiers help resolve ambiguities in their understanding of the intent. Much research in HII could be refocused to address issues directly related to commander’s intent. A platform could be built for testing and evaluating models and conducting experiments vis-à-vis commander’s intent in a multi-genre complex information environment.

Specific opportunities and challenges for some of the projects reviewed are discussed in the following.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Multimodal State Classification

The research could be strengthened, particularly with respect to methodology. There was lack of a clear articulation of connection to an Army mission, and the associated lack of a clear transition path and transition partner. 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. The project could benefit from using much larger training sets.

Exploiting and Mitigating Cognitive Effects of Real-World Luminance Dynamics on Visual Search

Initial results demonstrate feature interaction but no link of this to in-the-field utility was demonstrated. It was unclear if there was sufficient data for model development and testing, especially in the context of using deep neural networks. Another goal of the project is to develop a high-definition resolution model of visual saliency, and it would be useful for the researchers to review the literature on how saliency has been addressed in previous work.

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. The project is relatively new, and both the domain and the methodology are new to the investigators. While the investigators are creative and energetic, the goals of the project are overly ambitious, and the team has limited expertise in agent-based modeling. A project of this scope and the development of a credible agent based model in this space could take years and would employ vast quantities of data for model instantiation and testing.

There are many extant theories that are considered to be theories of human information interactions. These include social influence theory, constructuralism, information bias, and so on. The presenter had knowledge of a few of the behavioral theories such as confirmation bias. The approach being followed is to develop an agent-based model where complex interactions can be evaluated. However, there are multiple existing agent-based models that have been used to reason about human information interaction. It is important for researchers to learn about these models, and that the model being developed be docked against these existing models. A simple model in NetLogo is being developed that is great for learning but will not scale well for large simulations. This model does not control for structural effects such as the networks connection information, the social networks connecting people, the communication networks over which people interact, and the knowledge networks of who knows what, or has access to what information. The authors may consider employing a meta-network approach to look at high-dimensional networks. The work could also benefit from the use of agent-based modeling tools that allow for network analytics. Furthermore, the project could be strengthened by building on the measure of VoI developed elsewhere in HII and using that as one of the dependent variables in the simulation. That would both increase Army relevance and create synergies with other work in this area.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Assessing Value of Information for Graph Understanding

The project has a well laid out plan but is limited in that it does not have a comprehensive theory, or empirical mapping of the relation between graph type, decision problem, and data. Without this mapping, the overall guidelines developed are likely to be too generic.

Social Computing: New Directions for Army Relevance

The scope is somewhat narrow, and reflective of a strong computer science focus. Social computing is a well-established area, involving many scientists and multiple conferences devoted to the subject. The investigators present a description of social computing as dealing with social media but demonstrated limited understanding of the breadth of work in that field, and a grasp of the key challenges.

Value of Information-Based Mission Needs Matching

The objective of this research project is to evaluate a set of metrics that could be used to assess value of information within a simulation environment. This is a relatively small project that uses existing metrics with clear Army relevance. The project could be expanded to other metrics and the simulator expanded to other types of conditions. This, and indeed all the VoI projects, would be strengthened by integrating all the simulation and test-bed environments, creating a comprehensive set of VoI metrics, and utilizing consistent approaches to measuring and calculating them. Tighter integration across all the VoI projects would be a force multiplier. The simulator and the metrics are really best for evaluating physical systems, and as currently developed, will not work for sociocultural information. Indeed, such sociocultural information is difficult for all the VoI projects. Furthermore, the current simulator is limited in the kinds of missions that it can accurately reflect. The authors might consider developing a taxonomy of mission types so as to accurately gauge where the current simulator is relevant, and where new simulators are required. It is proposed that the work be extended to metric aggregation. This is a worthy goal, and the researchers could build better awareness of aggregation techniques cited in the literature.

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. The problem is important, and the researchers need to get a better understanding of other ongoing research related to fake news and rumor propagation. The work is directed primarily at spread of false information through social networks, but the model does not take into account known network effects. Further, the emphasis is on thinking of networks as graphs. The vast literature on information movement, fake news opinion dynamics, and social change in networks that has come out of the social sciences is very relevant, and needs be considered. The researchers also need to dock their model against leading models in this area that are based on social influence. 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. This is a highly interdisciplinary problem and requires expertise beyond the discipline of engineering. The researcher is quite talented and would benefit from going to the more social science conferences in this area—such as the INSNA meeting or SBP-BRiMS.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Opinion Formation and Shifting

The principal weakness of the project is a lack of consideration of a broad range of existing work on opinion formation and shifting. Specifically, this work is classifying people based on number of messages by type of media, with no attention to content, saliency of content, relation of content provider to the person being influenced, and so on. Prior work has demonstrated that it is not solely the number of messages that matter, but rather factors such as the affective relation between the sender and receiver, the content of the message, the affective nature of the message, the inclusion of image/video/numbers, the role of the sender in the receiver’s network, the strength and direction of the receiver’s current opinion. Another issue arises from the claim of obtaining results using “content laden message examples,” but content is explicitly removed in the experiment. This is clearly inconsistent. Use of AMT for subjects is also problematic. It is not clear that the subjects were sufficiently tested for linguistic skills commensurate with the project and for the learner type. Individuals have different ways of learning, and some are more prone to learning from video and images than others. This needs to be controlled in the experiment.

ATMOSPHERIC SCIENCES

The research portfolio of the Battlefield Environments Division (BED) can essentially be divided into two thrust areas that include improving environmental understanding of the planetary boundary layer (PBL) and processes that operate on small spatial and temporal scales, and on developing appropriate environmental intelligence tools for deployed soldiers to use in austere, complex operating environments. The review in this cycle followed on the heels of an assessment held at White Sands Missile Range (WSMR) in June 2016. Thus, this interim report spans a period that is one year instead of the two-year cycle of reviews that was adopted in 2013-2014.

Accomplishments and Advancements

BED scientists have accomplished a great deal, and these achievements will be highlighted throughout this section. Additionally, BED leadership provided specific, point-by-point responses to the findings and recommendations from the 2015-2016 ARLTAB report, and greatly assisted the panel in its work.

The research projects reviewed by the panel during this cycle included detection and characterization of chemical aerosols, acoustic and infrasound sensing, development and fielding of a meteorological sensor array at WSMR, and advances in small-scale atmospheric model development, verification, and validation. In addition, updates were provided to the panel on past large-scale projects. These are described shortly.

It is important to note that additional projects were also discussed with the panel. Significant progress has been made in the past year with the automated aerosol Raman spectrometer project. The work is commendable, and a recent paper2 is particularly useful in showing the utility of the project as well as discussing the benefits of the BED work over other aerosol characterization systems. Additionally, work that was completed through the Small Business Innovation Research (SBIR) program to improve drone performance is also noteworthy. This innovative leveraging of the SBIR program has been successful in developing enhanced drones that incorporate environmental turbulence data, thereby improving drone movement. The SBIR work that has been undertaken by the BED has multiple civilian and DOD

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2 D.C. Doughty and S.C. Hill, 2017, Automated aerosol Raman spectrometer for semi-continuous sampling of atmospheric aerosol, Journal of Quantitative Spectroscopy & Radiative Transfer 188:103-117.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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applications and, most importantly, has led to the development of additional, fundamental (6.1) scientific research questions.

More details on some of the projects reviewed are provided in the following.

Design of Experiments for Verification and Assessment of Fine-Scale Atmospheric Forecasts

The presentation was essentially an update of the team’s activity over the last 12 months. Good progress has been made regarding the development of an appropriate design of experiments (DoE) matrix for initial testing. The matrix, developed for 40 weather research and forecasting (WRF) model simulations, was recently completed and is ready for execution. The significant challenge of developing a DoE approach for atmospheric numerical weather prediction (NWP) models, an approach that has never been attempted, had been noted by ARLTAB earlier. The ARL team has done an admirable job of surveying the scientific community to evaluate the project’s feasibility and establish a unique niche for this work. Much work remains to be done, particularly with model verification. At present, the team proposes to employ root-mean-square error analysis of the 40 model runs, with the intention of performing more detailed verification using subdomains, and possibly leveraging the National Center for Atmospheric Research (NCAR) method for object-based diagnostic evaluation (MODE), which is currently being evaluated at ARL.

Multiscale Atmospheric Modeling for Tactical Army Nowcasting

The researchers have continued the performance evaluation of the WRF model for the May 2013 Moore, Oklahoma, tornado case. The model was tested in 2016 with a number of different data assimilation schemes, including a hybrid of four-dimensional (4D) data assimilation and three-dimensional (3D) variational data assimilation that is both computationally efficient and fairly accurate. Researchers have been running a very-large-eddy-simulation (VLES) version of WRF over the Jornada Experimental Range (JER) region north of WSMR, with a 300 m horizontal grid spacing and 130 vertical levels. Preliminary results from the VLES WRF show great promise in the realistic reproduction of detailed PBL vertical motion patterns over the JER region. Results from a version of WRF run on a 450 m horizontal grid over the Dugway Proving Grounds were also presented. 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) model for predicting mean wind, temperature, moisture, and turbulence over urban and complex terrain in near real time. These included computational fluid dynamics, the vortex filament method (VFM), and the lattice Boltzmann method (LBM). There are two attractive characteristics for the VFM that include its potential use as an engineering tool to help one understand the nature of complex terrain and urban-scale atmospheric flows, and the validity of the VFM approach in high Reynolds number scenarios. Preliminary results feature the ability of VFM to reproduce thermal bubble rise by comparing simulations to theoretical results from Shapiro and Kanak (2002),3 and demonstration of isotropic turbulence in a 3D periodic box.

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3 A. Shapiro and K.M. Kanak, 2002, Vortex formation in ellipsoidal thermal bubbles, Journal of Atmospheric Sciences 59(14):2253-2269.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Atmospheric Boundary Layer Environment Model Lattice Boltzmann Method

The LBM approach solves particle kinetic equations in lattice directions, and is well suited for modeling turbulent flow with complex boundaries. It is a more applied method than the VFM discussed previously, and has thus far satisfactorily reproduced theoretical results for stably stratified flow over a mountain ridge, convective flow forced by a heated ground surface, and flows around buildings. 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. The terrain and ground cover data were collected from UAVs and could be visualized using either 3D glasses with a projection screen or a holo-lens wearable visor. Both methods were demonstrated in the poster presentation. 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. This technology also has potential applications in a training environment.

Infrasound Research and Applications

This more-than-15-year applied research (6.2) project is enhancing techniques for detecting blast signals using infrasound waves—typically at frequencies below 20 Hz. These waves can travel for hundreds of km horizontally and vertically, making them ideal for standoff detection of events such as missile launches. The approach uses time-of-arrival (ToA) differences of sound, and incorporates these differences with atmospheric properties to improve the models to estimate ToA. This work is critical to the proper identification of activities and their locations. The research incorporates real-world measurements of infrasound ToA from rocket launches from Wallops Island, Virginia, under varying conditions; uses models with the appropriate parameters to simulate the actual systems; and then compares the actual measurements with the modeling results. This project therefore incorporates an excellent work plan to validate the utility of the models. One of the unique aspects of this project is the relationship between ARL and the recipient of the technology, the Army’s 501st Military Intelligence Brigade in the Republic of Korea. While the technique in its current state is not ready for field use by the brigade’s soldiers, ARL is currently involved with the brigade in testing and evaluating the methods. A clear transition plan to different parts of the Army is in place, and includes, among other aspects, collaborations with international partners (e.g., Republic of Korea). The work is strong, and improvements to the mathematical modeling (to improve the time-delay estimates) are possible through collaboration and connection with researchers in the sensing & effecting group.

Acoustic Classification Algorithms for Complex Propagation Channels

This project uses the modeling of sparse data sets to model acoustic events with the intention of developing event classifiers that can be used by soldiers in the field. Work was conducted at the basic research (6.1) level and is in the final year of 6.1 funding; it will transition to applied research (6.2). The work technically represents a joint battlefield environment effects/sensing and effecting project and its science is solid.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Detection and Characterization of Chemical Aerosol Using Single-Particle Laser Trap Raman Spectrometer

This project has successfully demonstrated a laser-based technique for isolating, detecting, and identifying the chemical compositions of micron-size particles of multiple phases with unprecedented speed and accuracy. The researchers’ method to capture and suspend the particle vastly reduces the analysis time to the order of seconds, and allows a sampling frequency that is much superior to other methods that are currently in use. The work is exceptional and novel, and has the ability to revolutionize the aerosol science field as well as all industries and technologies that rely on aerosol science. The unique feature of being able not only to measure composition but also to track changes in the size of the particle paves the way for new investigations and applications, and is therefore transformational to the aerosol field. As one example, the work may assist in developing basic information on the aging of particles, thereby helping to determine their origin. This project has the potential to spawn additional applications outside DOD, and once it has been adapted to an operational environment, provide soldiers in the field with an additional level of protection from chemical weapons. Several papers on this work have been published, are in the review process, or are being planned for submission to high-quality peer-reviewed journals.

Meteorological Sensor Array

The deployment of the meteorological sensor array (MSA) 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) and instrumented unmanned aircraft system (UAS). As a means of laying the groundwork for this ambitious project, the ARL team has been very engaged with universities, industry, and international agencies in field observation experiments such as mountain terrain atmospheric modeling and observations (MATERHORN), and an experiment that is just concluding in Perdigão, Portugal. The deployment of the MSA has four phases. Phase I is the experimental test-bed of 5 towers located near the WSMR Las Cruces gate. These towers have been operating as needed for the past 2 years. Phase II involves the installation of 36 meteorological sensor towers at the Jornada Experimental Range (JER) adjacent to WSMR, and a memorandum of understanding between ARL and the U.S. Department of Agriculture (USDA), which operates the National Wind Erosion Research Network in the JER, has been established. Phase II instrumentation deployment in USDA’s JER data collection network is more than 50 percent complete. There are also plans for an acoustic array and instrumented UAS over this area. Phase III will involve a dense instrumentation network installation on the west slope leading up and over San Andres Peak at 2510 m elevation to complement the ARL/USDA network over JER. This capability, when fielded, will provide detailed atmospheric information over this extremely mountainous terrain, with resulting data certain to benefit the Army’s ability to operate in such challenging environments. In Phase IV, ARL hopes to gain approval for installation and operation of instruments in the High Energy Laser Systems Test Facility domain, which is part of WSMR. To date, nearly a dozen agencies and universities have either provided equipment or are interested in collaborating with ARL on this project. For instance, NRL is contributing soil moisture sensors and rain gauges to the MSA for verifying satellite-based moisture measurements in a desert environment.

Opportunities and Challenges

Current staffing in the BED is 53, and includes 45 government personnel and additional contract personnel including two Ph.D. senior researchers, two postdoctoral fellows, two computational support staff, and two administrative support personnel. These numbers reflect a sharp decline in BED contract

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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personnel over the previous year, in both research and support staff. The engagement of postdoctoral fellows has greatly benefited the quality of research within BED, and the decline in their number is viewed with concern. The engagement of postdoctoral fellows has been shown to be effective in the past, and needs to be continued, especially if promising candidates can be converted to permanent staff. A new ARL initiative is the establishment of nine essential research areas (ERAs) that emphasize interdisciplinary efforts considered essential to the success of Army operations in the 2030-2050 time frame. BED researchers have been quick to show the linkages between their individual projects and the appropriate ERA. BED can be integrated within additional multiple ERAs.

The opportunities and challenges 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. 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. A related issue is the continuing challenge that BED faces to ensure that their unique expertise is being leveraged by ARL research projects that may have an environmental sensitivity. Both of these represent opportunities for additional research collaboration that can enhance the overall strength of the ARL portfolio, and strengthen its position for transitioning useful technology to the field. On the other hand, these interactions are also challenging because many project scientists outside BED are not aware of the expertise residing in the Directorate, and may not even understand that atmospheric effects and impacts need to be incorporated into their research. This latter point was illustrated in several projects from the system intelligence and intelligent systems and sensing and effecting areas, where the researchers either were not aware of the need to incorporate environmental sensitivities into their research methods or felt that environment was unimportant or could be mitigated by other strategies.

BED has continued to leverage all available resources in advancing the research plans. In addition to postdoctoral fellows, the Division has made extensive use of other avenues for assistance on their projects. For example, students from Navajo Technical University are at ARL as summer visitors. Another instrument that BED has utilized is the cooperative research and development agreement (CRADA). Planned future CRADAs with NCAR and Vaisala Corporation will help all three BED branches work more efficiently—with the Vaisala CRADA being particularly important as the MSA is fielded. However, there are some inefficiencies regarding the length of time to get CRADAs established. In an environment with limited resources, this presents a challenge to maintaining research at the leading edge. BED continues to be challenged by steadily declining resources, but there is a strong scientific imperative to incorporate BED expertise in multiple projects across the ARL portfolio. If full collaboration potential with other related laboratory projects can be realized, fundamental science will be advanced in highly significant and potentially groundbreaking ways.

Specific opportunities and challenges for some of the projects reviewed are discussed in the following.

Multiscale Atmospheric Modeling for Tactical Army Nowcasting

Challenges here include a more complete understanding of objective verification statistics for radar reflectivity, where the current equitable threat scores are somewhat counterintuitive. The preparation for eventual use of data from the meteorological sensor array (MSA) being fielded at WSMR was also discussed. The researcher commented about the potential of leveraging findings from the Design of Experiments project in future research once that project has produced usable results.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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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. (1997)4 that incorporated cloud scene simulation model output into a mission planning and rehearsal simulation tool called PowerScene. This early work was limited by hardware computational capabilities, with the simulation tool losing its real-time frame rate as a result of assimilating the weather model information. Significant advances in computational power render the approach more tractable, with potential for realistic weather incorporation into contemporary simulation tools.

Acoustic Classification Algorithms for Complex Propagation Channels

The departure of a key BED researcher on the project has resulted in the modeling effort not sufficiently incorporating atmospheric effects in the physics of acoustic propagation. The current approach is to mitigate or overcome the effects of the atmosphere through deployment of sensors in multiple locations. 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. The projects reviewed range from those advancing fundamental science to those focused on enabling technologies and applications. The ongoing projects demonstrated relevance to the future missions of the Army, and were generally of good technical quality. There are additional opportunities to further drive scientific innovation through enhanced integration and collaboration across campaigns.

ARL has focused on increasing the number of Ph.D. scientists on the research staff in critical areas of expertise and this has had a measurable positive impact on the overall quality of technical work. The nature of the research in information sciences dictates a workforce with significant technical diversity, including strengths in the social and mathematical sciences. This diversity and added strength in the social sciences is also critical to the HII initiative. Among the ARL researchers, there was generally a good awareness of external research and connections to professional organizations and external research communities; research results are appearing in respected conference proceedings and in archival journals. There is room for even broader dissemination of these results to a larger scientific community. As noted in earlier reviews, the mission-oriented thrust helps differentiate the ongoing research from efforts pursued elsewhere, and creates opportunities for impactful technical contributions. The impact of the work can be further enhanced by clear articulation of unique, cutting-edge research questions.

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4 L. Hembree, S. Brand, W.C. Mayse, M. Cianciolo, and B. Soderberg, 1997, Incorporation of a cloud simulation into a flight mission rehearsal system: prototype demonstration, Bulletin of the American Meteorological Society 78:815-822.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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The work in sensing and effecting was assessed to be generally of high scientific research quality, but not uniformly so. The research portfolio represents an appropriate balance of theoretical and experimental work, and many of the more mature programs, but not all, show a transition into practice or use by other areas. 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. The researchers are academically well qualified to carry out the research problems that they are pursuing. Some of the work is being published in top venues, but researchers could be given additional guidance and encouragement to present their work at leading conferences, and then pursue publication in top archival journals. The increase in postdoctoral researchers and early-career researchers is a positive trend. The quality of the early-career ARL researchers in the S&E area is impressive. The ARL process of using co-ops and internships to recruit a strong cadre of emerging researchers appears to be yielding positive results. The facilities are adequate to the needs of the researchers and of the research, including access to computational facilities and to laboratory instrumentation.

The overall research program in SIIS is aligned with the current and future needs of the Army. It is clear that the researchers within SIIS are generally conversant with modern techniques and approaches from the rapid advances in machine-learning research. SIIS researchers were forthcoming and articulate in their presentations and responses to questions. Thorough materials were provided well in advance of the meeting, newer publications were provided, and the research team was available for clarification after its initial presentations. The presentations of the different projects were of somewhat variable quality. In part, this was due to the limited scope of some of the projects, or that the project was in an early stage and did not have a comprehensive set of results. In some instances, the presentations simply failed to highlight key questions, methods, and interim or final results. For some of the more experimental work, it was not clear whether the researchers were building an engineering artifact to develop initial insights and validate approaches, or whether they had a more scientific paradigm that started with a hypothesis that could be crisply stated, and that was the basis for the design of the experiment. In many instances, the research results are being disseminated in top conferences or journals, but not uniformly so. While not every piece of good work will be published in the most highly regarded venues, the site of publication is a good indication of high-quality work and provides excellent visibility for the researcher and ARL. Furthermore, publishing in high-level journals will attract the top academia researchers (and their students) to problems most relevant to the ARL mission.

The research focus in the area of HII is relatively new, and includes many projects that are at an early stage of inception. As a result, the technical quality of the work is mixed. 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. The more mature work has strong Army relevance but tends to focus on single human-technology interaction. While important for demonstration purposes, the impact of the research could be enhanced by reformulating the problems to include scenarios involving Army teams interacting with information and technology. The researchers are clearly skilled and show enthusiasm and the desire to grow and to learn. Early-career researchers could use more mentoring, and all researchers would benefit from better ties to the broader scientific community. The field is highly interdisciplinary, and research productivity and impact would be enhanced if the HII team was better balanced to include additional social scientists. The work being done by HII researchers is being disseminated at relevant conferences, including multiple papers at leading venues such as the SBP-BRiMS conference.

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. Researchers are very familiar with the underlying science and cognizant of research being done elsewhere; in many cases, they have been either dialoguing or

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×

collaborating with researchers outside ARL. In all cases, the researchers are aware of the potential challenges associated with their projects. BED uses the use-inspired research5 approach, resulting in researchers from different disciplines being brought into their projects. In one case, the dialogue led to the recent staff additions of a mathematician from another ARL division and a National Aeronautics and Space Administration (NASA)-supported scientist, which have greatly benefited the vortex filament and lattice Boltzmann projects. In several other instances, collaboration and consultation with researchers outside atmospheric sciences have enhanced project quality and applicability to the Army’s mission. 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. The projects reviewed range from those advancing fundamental science to those focused on enabling technologies and applications. The ongoing projects demonstrated relevance to the future missions of the Army, and were generally of good technical quality. There are additional opportunities to further drive scientific innovation through enhanced integration and collaboration across campaigns.

The work in S&E was assessed to be generally of high scientific quality, but not uniformly so. There was a balance between theoretical and experimental work, and in many of the more mature programs, there was evidence of transition into practice or use by other areas. The research generally reflected a good understanding of the problems being considered. The researchers are academically well qualified to carry out the research problems that they are pursuing, and some of the work is being published in top venues; more, however, can be done in terms of mentoring and encouragement to seek the most highly rated venues for disseminating their results. The quality of the early-career ARL researchers in the S&E area is impressive, and the capacity to successfully address much more challenging research problems now exists within ARL. There is currently a mismatch between the quality of the researchers and the degree of difficulty of problems that are being pursued.

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. Several early-career researchers have been recruited and have the appropriate background and academic training to pursue the research goals that have been identified. The research projects are appropriately resourced, and both the facilities and computational resources are adequate for the proposed work. There is a healthy collaboration among researchers, both within ARL and with the external community. Continued investments in this regard are encouraged, as in some instances it appeared that SIIS researchers within ARL were not always aware of similar or parallel efforts elsewhere.

Recommendation: ARL should bring about greater understanding of available technical expertise in SIIS-related areas across ARL, and build synergy across campaign thrusts to leverage this technical talent.

___________________

5 A commentary on use-inspired basic research, “Thinking Beyond the ‘Quadrant,’” by Dr. F. Fleming Crim, NSF Assistant Director for Mathematical and Physical Sciences, https://www.nsf.gov/mps/perspectives/quadrant_august2014.jsp (accessed August 10, 2017).

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×

HII is a small program that seeks to address an enormous area, with a wide range of potential applications. HII would be well served by developing more within-group synergy and concentrating on those aspects that are highly relevant to Army missions and the deployed dismounted soldier. Given the nascent nature of the program, it is advisable to develop a strong mentoring program, perhaps using a combination of external and internal mentors. There is also a need for developing a broader set of external collaborators, including mathematical sociologists and social psychologists in the area of teaming. 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. This concern could be alleviated by making tools such as Repast and ORA and various image processing software available to researchers.

Recommendation: The HII research program is in a formative state, and ARL should develop a strong mentoring program for the research team involving external and internal mentors. Also, ARL should establish a broader set of external collaborations to accelerate progress of the research agenda.

With respect to ongoing research in BED, the importance of the natural environment needs to be featured more prominently in strategic planning documents in order to advance the fundamental science while encouraging collaboration and dialogue between researchers whose projects have an environmental sensitivity, and subject matter experts (SMEs) from BED. The exclusion of environmental effects, whether inadvertent or intentional, could possibly jeopardize the successful fielding of promising new technologies to the Army and the broader society. Environmental phenomena are crosscutting and impact nearly every Army operation.

Recommendation: The environmental impact on Army operations should be given crosscutting, prominent visibility in planning documents such as the S&T Campaign plan and the essential research areas (ERAs) that are being formalized by ARL senior leadership. ARL should evaluate the potential for environmental integration at the initiation of a new project, to enable teams to incorporate multiple components into their approach at the inception of the design process, and potentially enable faster basic science and applied technology development.

Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
×
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Suggested Citation:"4 Information Sciences." National Academies of Sciences, Engineering, and Medicine. 2018. 2017-2018 Assessment of the Army Research Laboratory: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/25011.
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The National Academies of Sciences, Engineering, and Medicine's Army Research Laboratory Technical Assessment Board (ARLTAB) provides biennial assessments of the scientific and technical quality of the research, development, and analysis programs at the Army Research Laboratory (ARL), focusing on ballistics sciences, human sciences, information sciences, materials sciences, and mechanical sciences. This interim report summarizes the findings of the ARLTAB for the first year of this biennial assessment; the current report addresses approximately half the portfolio for each campaign; the remainder will be assessed in 2018.

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