army research office multidisciplinary university research
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Army Research Office Multidisciplinary University Research Initiative
Unified Research on Network-based Hard/Soft Information Fusion
Third Annual Review Period August 1, 2011 thru July 31, 2012
Drs. Rakesh Nagi (PI), Moises Sudit (co-PI), James Llinas (Emeritus PI)
Center for Multisource Information Fusion
State University of New York at Buffalo
Buffalo, New York, USA
{nagi, sudit, llinas}@buffalo.edu
Dr. John Lavery, ARO PM
Army Research Office
Research Triangle Park, NC 27709 [email protected]
MURI Overview Agenda
1. Why is this an important area of research? 2. What is MURI Project trying to accomplish (scientific
objective and technical approach)? 3. Who are the MURI team members? 4. What are the Challenges in Hard/Soft Information
Fusion? 5. Project Scientific Issues and Progress to Date:
Underlying scientific principles, prior state-of-the art advances that favored a multidisciplinary approach, scientific barriers, potential scientific advances.
6. Summary Architecture 7. Scientific breakthroughs in this research
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1. Importance of the Problem In a few words…
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“The attacks of September 11th 2001 killed 2,996 people. Despite the subsequent declaration of a war on terror, over the past ten years thousands more have been killed by terrorists of all hues.”
http://www.economist.com/blogs/dailychart/2011/09/global-terrorism-deaths
2. Scientific Objective and Technical Approach
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NEW
INPUTS
NEW
DECISION
PARADIGMS
LIMITED
DEDUCTIVE
KNOWLEDGE
CONTEXTUALLY
RICH
Some Distinctions in Hard and Soft Observational Data
Totally distinct from Hard Sensors Philosophy: Relations not directly
observable—require reasoning over properties of entities*
* Brower, J., (2001) "Relations without Polyadic Properties: Albert the Great on the Nature and Ontological Status of Relations." Archiv für Geschichte der Philosophie 83: 225–57.
Humans can also judge intangibles --emotional state
DDAATTAA CCHHAARRAACCTTEERRIISSTTIICC HHAARRDD DDAATTAA SSOOFFTT DDAATTAA IIMMPPLLIICCAATTIIOONNSS FFOORR FFUUSSIIOONN
PPRROOCCEESSSSIINNGG
OBSERVATIONAL SAMPLING RATE
HIGH LOW --“Out-of-sequence” processing --agile Temporal Reasoning
SEMANTIC CONTENT ATTRIBUTES FOR SINGULAR ENTITIES
HIGHLY VARIABLE; INCLUDES: -JUDGED RELATIONS -JUDGED INTANGIBLES
--Auto semantic labeling for Hard Data --Rich domain Ontology --Data Association at varying levels of abstraction
ACCURACY, PRECISION RELATIVELY HIGH, GOOD REPEATABILITY (PRECISION)
COGNITIVE AND LINGUISTIC CONSTRAINTS YIELD LOWER ACCURACY AND PRECISION
Requires new strategies and techniques for Common Referencing and Data Association
--Calibrated, --Physics-based, --Known errors,
--Known parametric
Performance --Attribute-
Centric
-Weakly Calibrated, -Neural-based, -Weakly Known
errors, -Weakly Known
parametric Performance
-Attribute-and Relation Centric
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3. Project Team
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Penn State University Dr. David Hall, Principal Investigator
US Army Research Office Dr. John Lavery, Program Manager
State University of New York at Buffalo Drs. Rakesh Nagi , James Llinas, Moises Sudit, Principal Investigators
Tennessee State University Dr. Amir Shirkhodaie, Principal Investigator
Iona College Dr. Ronald Yager, Principal Investigator
3. The Team in the Large
• University at Buffalo – 10 Graduate students; 1 PhD awarded, 2 Master’s awarded; 2
Undergraduate students
• Penn State University – 7 Graduate students; 2 Staff; 4 Master’s awarded; 1 PhD
expected 2012
• Tennessee State University – 4 Graduate students; 1 Post-doc; 3 Undergraduate students
• IONA College – Faculty only
• All-Hands Count: About 40 people
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4. Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) – Data-level Fusion (Raw Fusion) – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty
• Uncertainty representation and transformation
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty
• Contextual Information – Observational gap-filling, interpretation
• Association of Hard and Soft Information – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) – Asynchronous (out of sequence) information arrival
• Processing of Hard or Soft Information – How does the one influence the parameters, biases or algorithms for the processing of the other
• Lack of Datasets for Technology Development and Verification • Traditional Challenges in Hard Fusion • High-level Fusion (Situation Understanding) • Concepts of Employment (and Transition) 9
FUSION
DISCIPLINES
Computational Linguistics
Probability-Possibility Theory
Human Factors Engineering
Computer Science
Operations Research/Opt.
Information Fusion Systems Engineering
Electrical Engineering
Design of Experiments V& V
Technology Management
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Synthetic Dataset for Hard+Soft SYNCOIN (PSU)
9/22/2012 11
Soft Messages • Developed SYNCOIN, including interlaced scenarios,
600 text messages and synthetic hard data
Hard Sensor Data Collect
Combined Hard and Soft • Realistic COIN situations • Associable (entities, locations, etc.) • Complementary • Insightful and non-trivial •Non-linear (intertwined, out-of-sequence data) • Confounding elements (background noise) • Linguistic and human observational uncertainty
Ground Truthed for T&E
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Tractor: Soft Data Understanding (UB)
9/22/2012 13
Tractor Processing Pipeline •GATE -> Conversion to syntactic propositional
graph -> CBIR -> Syntactic/Semantic mapping
Soft Message Understanding • Tractor converts Soft Data into Semantic
descriptions (of entities, events, relationships) called semantic propositional graphs
• Improves co-referencing in GATE • 65 Rules for Syntactic to Semantic
mapping • Context-Based Information Retrieval Adds relevant geographical information; ontology; mereology
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Tractor: Soft Data Understanding
Transitions
- Jim Hendler (for Army Network Science CTA project)
- Mark A. Thomas, USA CIV (US)
- Gabor (Gabe) Schmera, SPAWAR
409,414 Wikipedia documents ~264,000,000 words ~1,700,000 unique entities Tractor processed @~ 0.03 secs/word
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Objectives: • Support development of hard/soft information fusion • Develop methods for the aggregation of uncertain information • Provide formalisms for the representation and modeling of soft information
DoD Benefit: • Better use of available information
Scientific/Technical Approach
• Fuzzy Set Theory
• Monotonic Set Measure
• Dempster Shafer Theory
• Mathematical theory of aggregation
• Computing with Words
Accomplishments • Set measure model of fusion
• Role of expert instructions
• Nature of Soft information
• Modeling different uncertainty modes
Challenges • Mixed uncertainty mode fusion • Complexity of Soft information
Computing with WordsComputing with Words
Representation
(Translation)
Fusion
Inference Reasoning
Retranslation
Soft Information
Hard
Information
Fusion
Instructio
Uncertainty Representation and Transformation
(IONA College)
Uncertainty Representation and Transformation
(IONA College)
• The Fuzzy measure has the capability of modeling in a unified framework many different types of knowledge about the value of a variable
• Fuzzy measures closed under aggregation operations are needed for Multi-Source Information Fusion
• The measures of assurance and opportunity generalize some fundamental concepts used in uncertainty modeling
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0.7
0.8
0.9
1
30 35 40 45
Age Uncertainty Function
Uncertainty Function for Observed Age of 42 Years by
Observer Trained in Age Estimation
• Observation: person, age 42 years old
– Observer Characteristic : trained in age estimation
– Uncertainty Function: from MURI Human Source Characterization efforts
• Bias: overestimate age by 4.07 years
• Variance: error is normally distributed with standard deviation of 1.65 years
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Human Source Characterization (UB)
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Objectives •Humans make observations (e.g. age
estimation of a target, number of targets) •How accurate are humans? •What is the influence or training? •How do environmental conditions (e.g.,
lighting) influence their estimate? •How to characterize uncertainty and eliminate
bias?
Scientific/Technical Approach • Literature based review of existing
research in psychology, decision-making, perception, psychophysics
• Focus on qualifying variables impact on observational error
• FM study for COIN relevant observational categories
Context Aware Data Fusion
Transition Target: Uncertainty Alignment Engine
ARL APG Infrastructure Program
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Objectives:
• Align observations with consideration for observational uncertainties
– Assess observational biases/variances
• Represent observation uncertainties in unified framework – Transform observations where required to common representation
• Automate process for uncertainty alignment
• Enable human observation database maintenance
DOD Program Benefit:
• Uncertainty alignment enables accurate uncertainty propagation through situation assessment process
Initial Prototype: Technology Readiness Levels (TRL): • Uncertainty alignment (UA) database format
developed
• Database implementation tested for prototype observation categories
• Prototype models relied on empirical studies
• UA logic implemented within existing dirty graph matching code
• UA database editor developed
Architecture
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Sensor Selection Based on Physical and Human Targets
Figure 6: Summary of Selected PSU Hard Sensors
Selection Criteria • Sensors that are representative of
tactically deployed sensors • Sensors that provide informational
“value added” to the inference process on our selected targets •A suite that can be utilized in real
demonstrations and campus-based experiments •At least one sensor that allows
innovations for the hard sensor processing flow
Selected Sensors • LIDAR • Short-Wavelength Infra-red (SWIR) • Long Wavelength Infra-red (LWIR) •Visual Video •Acoustic Sensors
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Hard Sensor Fusion (PSU)
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2D/3D Video
3D Flash Lidar
Genlock Multiple Hypothesis Particle Filter Color Tracker
Multiple Hypothesis Lidar Range Segmentation Tracker
Fusi
on
En
gin
e
3D
2D/3D
• The objective of the implementation is to incorporate range data from the lidar which will be used to guide the color tracker in cases where track is lost due to occlusion.
3-D
Po
int
Clo
ud
N
ois
e P
roce
ssin
g Ortho-
rectify 3-D image to 2D image Flash LIDAR
Visual (2-D)
Video
Pixel Level
Fusion • Color Intensity Mapping
Range-Intensity Fusion
PCA Shape Decomposition • Point Cloud Classification • 3D Descriptors
X I/D Att.
2-D
Imag
e P
roce
ssin
g
Feature Extraction • Ransac • HOG • SIFT • Hough -
Visual (2-D)
•Pattern recognition • Jones & Rehg’s Gaussian mixture models D
ata
Ass
oci
atio
n v
ia m
ult
i-ca
me
ra h
om
ogr
aphy
&
geo
met
ry-b
ased
met
ho
ds
Target Tracking • K. Filter • P. Filter • LOB
X
Target ID • Neural
nets • Bayes
I/D
Rep
ort
(st
ate
vect
or)
leve
l A
sso
ciat
ion
/Co
rrel
atio
n
Rep
ort
(st
ate
vect
or)
leve
l tar
get
trac
kin
g
TML
Tran
sfo
rm
Hard Sensor Processing Flow
• SWIR •VNIR
•LWIR
Pixel-Level Fusion Point-Cloud range estimates mapped to 2-D camera components
Year 1 Year 2 Year 3+
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An Integrated Approach Hard (PSU+TSU) and Soft (UB)
Fusion
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Acoustic Signature Analysis Based on STFT Spectrogram
(PSU)
Short-Time Fourier Transform (STFT)
Discrete Wavelet Transform (DWT)
Kernel-Based Gaussian Mixture Model (K-GMM)
Alkilani, A., Shirkhodaie, A., “Vibro-Acoustic Analysis of Contented Containers,” in review for publication in SPIE 2012 Defense and Security Conference, April 2012, Orlando, FL.
Classification of Handled
Objects By Their Sound (PSU)
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Conceptual Spaces Framework: Symbolic vs. Numerical
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R-Tree for Indexing Regions
Sample of Regions in Iraq
0.2-0.5 ms per query
Bombing
Bombing
Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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Data Association (UB)
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Objectives •Associating or co-referencing entities
from multiple soft, hard, or soft and hard sources
Novelty •Principled Mathematical optimization
approach considering full graph relationships •Cloud Computing Solution
Taxonomy of Association Problems
Highlights and Technology Transition
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Transitions
- I2WD A2SF program
- AFOSR (Intel Client)
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Fundamental Hard-Soft Fusion Challenges
• Hard-Soft Fusion Architecture (what/when to fuse?) [T1] – Feature-level Fusion (Measurement or Early Fusion) – Estimate-level Fusion (Late Fusion)
• Nature of Soft Information – Symbolically expressed (symbolic ambiguity) – Human observations have bias and (seldom characterized) uncertainty [T5]
• Uncertainty representation and transformation [T4]
– Results of cognitive fusion are unavailable (feature to symbol) – Natural language processing difficulty [T2]
• Contextual Information – Observational gap-filling, interpretation [T3]
• Association of Hard and Soft Information [T8] – Symbolic versus Numerical (Baghdad vs. 33°20’ N, 44°26’ E) [T7] – Asynchronous (out of sequence) information arrival [T6]
• Processing of Hard or Soft Information [T10] – How does the one influence the parameters, biases or algorithms for the processing of the
other
• Lack of Datasets for Technology Development and Verification [T11] • Traditional Challenges in Hard Fusion [T12] • High-level Fusion (Situation Understanding) [T9] • Concepts of Employment (and Transition) [T13]
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High-Level Fusion: Situation Understanding, Analytics (UB)
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Objectives • New Fusion Models for
Complex Relational Data
• Uncertainty representation in graphs and graph matching
• Incremental real-time SA
• Simultaneous analysis of multiple situations of interest (AND-OR graphs)
• User display, provenance/ pedigree reach back.
Similarity Scoring and Uncertainty
Stochastic Graph Matching Results Display
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Transitions
- I2WD A2SF program
- AFOSR (Intel Client) - ARL
Supporting Sensmaking: Network Visualization (UB)
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Objectives •Humans make observations (e.g. age
estimation of a target, number of targets) •How accurate are humans? •What is the influence or training? •How do environmental conditions (e.g.,
lighting) influence their estimate? •How to characterize uncertainty and eliminate
bias?
Enhanced Network Visualization
Agenda
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1. Why is this an important area of research? 2. What is MURI Project trying to accomplish (scientific
objective and technical approach)? 3. Who are the MURI team members? 4. What are the Challenges in Hard/Soft Information
Fusion? 5. Project Scientific Issues and Progress to Date:
Underlying scientific principles, prior state-of-the art advances that favored a multidisciplinary approach, scientific barriers, potential scientific advances.
6. Summary Architecture 7. Scientific breakthroughs in this research
Agenda
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1. Why is this an important area of research? 2. What is MURI Project trying to accomplish (scientific
objective and technical approach)? 3. Who are the MURI team members? 4. What are the Challenges in Hard/Soft Information
Fusion? 5. Project Scientific Issues and Progress to Date:
Underlying scientific principles, prior state-of-the art advances that favored a multidisciplinary approach, scientific barriers, potential scientific advances.
6. Summary Architecture 7. Scientific breakthroughs in this research
Outcomes & Breakthroughs
1. Created the first known Hard and Soft "SYNCOIN" dataset for fusion technology and analytics development.
This counter insurgency dataset has about 600 natural language messages with interleaved vignettes for the development and testing of natural language processing methods, and hard sensor associable counterparts for developing semantic extraction and association technologies. This unique data sets is inspired by a Counter-Insurgency (COIN) scenario in Bagdad and contains synthetic soft (human report) data, synthetic hard (physical sensor) data, and real hard data collected using human-in-the-loop vignettes collected at a special facility in central Pennsylvania. The data are augmented by extensive “ground truth” information including, scene setter descriptions, identification and location of all events and activities, social network information, database schema, reference maps, and word cloud diagrams.
2. First ever comprehensive characterization of human observations that are context sensitive. Results published in a submitted paper for the Information Fusion journal, and transition to ARL pending through the ARL infrastructure initiative. Characterizing the human observer is an essential “human” source characterization and common referencing step for hard-soft fusion.
3. An novel approach to Natural Language Processing through "Tractor" and context overlay so that no information is lost and all semantically meaningful information is extracted. Relevant contextual information (as a human would have when reading text) is added so that machine reasoning is feasible. This is a goal, but it is an important discriminator from NLP engines that are simply interested in "information retrieval" of entities and relationships.
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Outcomes & Breakthroughs
4. Best in class graph association engine. First Map-Reduce implementation for distributed computing. The event association engine, which includes location normalization has been compared to others in the literature. Paper under revision for ACM trans on Info Sys. Association results provide better Precision, Recall and F-measure than other approaches in the literature. Results to be published [Note here that since there is no agreed corpus, a direct comparison with other authors is sometime not accurate.] Journal paper revised for Naval Research Logistics establishes a Lagrangian approach to Graph Association. Results within 5% of the optimal are produced.
5. Graph Matching: Most comprehensive suite of graph matching engines to include uncertainty, AND-OR template graphs (which are suitable to model PIRs), and incremental graph information.
6. 2D to 3D image fusion. We have developed advanced algorithms for the fusion of hard sensor data that include Lidar, SWIR, LWIR, visual video and acoustic sensors. The algorithms perform fusion at the data level – mapping 2-D image data to 3-D Lidar data, creating a “smart pixel” 3-D point cloud that allows multi-spectral feature extraction, hierarchical object classification (based on object size, shape and spectral characteristics), and very accurate target location. New algorithms for multiple hypothesis tracking provide the capability to track dynamic objects in complex environments including tracking of multiple people through crowds including obscurations.
7. Creation and demonstration of a distributed cyber infrastructure for fusion. We have explored the design and implementation of an infrastructure to support distributed information fusion. The toolbox of techniques include; communication methods and protocols, extensions of Service Oriented Architecture (SOA) and Message Oriented Middleware (MOM) paradigms, optimized information flow and tasking, complex event processing, and utilization of community standard data representations. Demonstrations of this infrastructure have been created using tools such as GeoSuite.
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