Denis Garagic

Sustaining Plants and People: Traditional Q’eqchi’ Maya Botanical Knowledge and Interactive Spatial Modeling in Prioritizing Conservation of Medicinal Plants for Culturally Relative Holistic Health Promotion

Ethnobotanical surveys were conducted to locate culturally important, regionally scarce, and disappearing medicinal plants via a novel participatory methodology which involves healer-expert knowledge in interactive spatial modeling to prioritize conservation efforts and thus facilitate health promotion via medicinal plant resource sustained availability. These surveys, conducted in the Maya Mountains, Belize, generate ethnobotanical, ecological, and geospatial data on species which are used by Q’eqchi’ Maya healers in practice. Several of these mountainous species are regionally scarce and the healers are expressing difficulties in finding them for use in promotion of community health and wellness. Based on healers’ input, zones of highest probability for locating regionally scarce, disappearing, and culturally important plants in their ecosystem niches can be facilitated by interactive modeling. In the present study, this is begun by choosing three representative species to train an interactive predictive model. Model accuracy was then assessed statistically by testing for independence between predicted occurrence and actual occurrence of medicinal plants. A high level of accuracy was achieved using a small set of exemplar data. This work demonstrates the potential of combining ethnobotany and botanical spatial information with indigenous ecosystems concepts and Q’eqchi’ Maya healing knowledge via predictive modeling. Through this approach, we may identify regions where species are located and accordingly promote for prioritization and application of in situ and ex situ conservation strategies to protect them. This represents a significant step toward facilitating sustained culturally relative health promotion as well as overall enhanced ecological integrity to the region and the earth.

Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring

A novel adaptive mixture-based neural network is presented for exploiting track data to learn normal patterns of motion behavior and detect deviations from normalcy. We have extended our prior approach by introducing multidimensional probability density components to represent class density using an adaptive mixture of such components. The number of components in the adaptive mixture algorithm, as well as the values of the parameters of the density components, is estimated from the data. The network utilizes a recursive version of the Expectation Maximization (EM) algorithm to minimize the Kullback-Leibler information metric by means of stochastic approximation combined with a rule for creation of new components. Learning occurs incrementally in order to allow the system to take advantage of increasing amounts of data without having to take the system offline periodically to update models. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. In addition, the adaptive mixtures neural network classifies streaming track data as normal or deviant. These capabilities contribute to higher-level fusion situational awareness and assessment objectives by enabling a shift of operator focus from sensor monitoring and activity detection to assessment and response. Our overall motion pattern learning approach learns behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce massive amounts of track data to a rich set of information regarding operator field of regard that supports rapid decision-making and timely response initiation.

Adaptive spatial scale for cognitively-inspired motion pattern learning & analysis algorithms for higher-level fusion and automated scene understanding

To date, our neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior have operated at fixed spatial scales. Although these models continuously adapted to incoming track data through incremental learning in order to adjust to evolving situations, the fundamental spatial scale of the learned models did not change over time. This constraint necessitates a trade-off between model maturation rate and deviation detection or behavior prediction performance. This paper describes updates to our approach that enable data-driven model scale adaptation. Anomaly detection is based on coarse resolution models during early learning stages and progressively switches to finer resolution models as sufficient data are received. This approach increases speed of model maturation with small amounts of data, while improving model fidelity and anomaly detection sensitivity as increasing amounts of data are received. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to assessment and response. Our learning algorithms learn behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding their field of regard that supports decision-making and timely response initiation.

Anomaly Detection & Behavior Prediction: Higher-Level Fusion Based on Computational Neuroscientific Principles

Higher-level fusion aims to enhance situational awareness and assessment (Endsley, 1995). Enhancing the understanding analysts/operators derive from fused information is a key objective. Modern systems are capable of fusing information from multiple sensors, often using inhomogeneous modalities, into a single, coherent kinematic track picture. Although this provides a self-consistent representation of considerable data, having hundreds, or possibly thousands, of moving elements depicted on a display does not make for ease of comprehension (even with the best possible human-computer interface design). Automated assistance for operators that supports ready identification of those elements most worthy of their attention is one approach for effectively leveraging lower-level fusion products. A straightforward, commonly employed method is to use rule-based motion analysis techniques. Pre-defined activity patterns can be detected and identified to operators. Detectable patterns range from simple trip-wire crossing or zone penetration to more sophisticated multi-element interactions, such as rendezvous. Despite having a degree of utility, rule-based methods do not provide a complete solution. The complexity of real-world situations arises from the myriad combinations of conditions and contexts that make development of thorough, all-encompassing sets of rules impossible. Furthermore, it is also often the case that the events of interest and/or the conditions and contexts in which they are noteworthy can change at rates for which it is impractical to extend or modify large rule corpora. Also, pre-defined rules cannot assist operators interested in being able to determine whether any unusual activity is occurring in the track picture they are monitoring. Timely identification and assessment of anomalous activity within an area of interest is an increasingly important capability—one that falls under the enhanced situational awareness objective of higher-level fusion. A precursor of being able to automatically notify operators about the presence of anomalous activity is the capability to detect deviations from normal behavior. To do this, a model of normal behavior is required. It is impractical to consider a rule-based approach for achieving such a task, so an adaptive method is required: that is, a capability to learn what is normal in a scene is required. This normalcy representation can then be used to assess new data in order to determine their degree of normalcy and provide notification when any O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Automated activity pattern learning and monitoring provide decision support to supervisors of busy environments

Neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior are presented. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to behavior assessment and response decision-making. Our learning algorithms construct models of normal activity patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding the current status of active entities within an operators field of regard. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. Deviations from normalcy result in notification reports that can be published directly to operator displays. Deviation tolerance levels are user settable during system operation to tune alerting sensitivity. Operator responses to anomaly alerts can be fed back into the algorithms to further enhance and refine learned models. These algorithms have been successfully demonstrated to learn vessel behaviors across the maritime domain and to learn vehicle and dismount behavior in land-based settings.

Long-range dismount activity classification: LODAC

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non‐parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self‐tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti‐access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross‐modal signal generation) and discovers the critical cross‐modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non‐parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Interactive spatial evolutionary computation based predictive modeling of rare plant species occurrences

In this paper, a habitat suitability model for rare plant species is developed by combining spatial predictive modeling techniques and an evolutionary computation method known as Interactive Evolution (IE). The interactive spatial evolutionary computation based predictive modeling enables an analyst to combine advanced mathematical geospatial and pattern recognition modeling techniques, conceptual knowledge, available empirical data, and expert-interactive dynamic data visualization techniques to predict species distributions. This methodology represents a step toward identifying regions where species are likely located to base argument for appropriate stratification in applying culturally relative conservation strategies to protect them and promote overall enhanced ecological integrity to the region. The model accuracy was assessed statistically by testing for independence between predicted occurrence and actual occurrence using cross-validation tests.