United States

The Power Projection Using Process Technologies Tool (PPUPTT) enables data-driven tactics, techniques, and procedures (TTP) for enduring 21st Century Competition. Developed originally for exploiting fleeting opportunities in space ecosystems at scale, PPUPTT aligns with the Space Force’s “Combat Power Projection&#39;&#39; strategic capability. Importantly, however, PPUPTT may be adapted for use within any ecosystem of interest. Governments and military organizations need to transition from linear, kinetic approaches to a continual competition mindset. Better, faster data-driven decisions advantage one player over another. PPUPTT uses explainable process technologies which predict complex systems next-state, and identify fleeting opportunities to maneuver and increase adversary costs of operations. Process technologies are core to PPUPTT’s architecture. Developed originally in a business context for organizations to model the processes which run the business, PPUPTT uses process mining to model complex systems as temporal, unfolding processes. Using process models outputs, PPUPTT derives time series information entropy measures and depicts entropy with intuitive, engaging dashboards fueled by explainable artificial intelligence--making it easy for those without extensive data science training to use the software, understand the analyses, and communicate recommendations. Information entropy, which is linked to Ashby’s Law of Requisite Variety, is a measure of the state of possibilities within complex systems. Changes in information entropy reflect changes in the distribution (variety) of activities, which uncover planning and execution cycles inferences, cognitive state comparisons among agents, and ecosystem stimulus-response dynamics. Creative TTPs built using these measures improve the ability for blue to pursue goals by increasing blue variety of TTPs while simultaneously tarnishing red’s variety of activities. It does this by monitoring ecosystems of interest continually and providing operators with actionable insights tailored to their specific needs. Finally, PPUPTT speeds decision making and automates rote analyses, which frees up time for human sense making. PPUPTT operationalizes an aspect of complexity science by synthesizing research from noteworthy thinkers. For example, Shannon’s Information Theory and simple entropy calculation is used to identify surprising confluences of events which can be used for persistent monitoring, alerting, and decision making based upon recent and historical activity. Information entropy describes the variety of activities or state of possibilities within complex systems. Inputs and outputs are in open data standards which facilitates collaboration with adjacent systems. PPUPTT enables maneuver warfare grounded in complexity science, can be operationalized quickly, and will increase in value as more statistical insights are discovered and TTPs developed.

There is a pressing need to exploit fleeting opportunities continually--real time anticipatory ecosystem entropy offers a lens for these kinds of exploitations. We want to measure ecosystem entropy continually to make better, faster data-driven decisions which advantage one player over another. Combining entropy measures with process models, we seek to understand how dissipative structures interact with one another. Continual sliding window entropy measurements identify exploitation opportunities when a player’s ecosystem is especially chaotic or orderly. Process models provide turnkey simulation parameters which predict ecosystem next-states and identify exploitation actions. This concept may be applied to space, social media, critical infrastructure, urban, supply chain, macro-economic, natural, and geo-political ecosystems, as well. Efforts to date have neither examined ecosystems in a temporal process mining context using categorical data event logs nor analyzed interactions between ecosystem players in order to anticipate outcomes and improve decision making. A highly adaptable, domain agnostic information maneuver capability is possible. It will be possible to drive entropy purposefully by anticipating ecosystem changes or inflection points and make decisions which favor one party over the other.

NAML 2023

Power Projection Using Process Technologies Tool (PPUPTT)

law of requisite variety

systems warfare

operations research

process mining

complex systems

entropy

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



The Power Projection Using Process Technologies Tool (PPUPTT) enables data-driven tactics, techniques, and procedures (TTP) for enduring 21st Century Competition. Developed originally for exploiting fleeting opportunities in space ecosystems at scale, PPUPTT aligns with the Space Force’s “Combat Power Projection'' strategic capability. Importantly, however, PPUPTT may be adapted for use within any ecosystem of interest. Governments and military organizations need to transition from linear, kinetic approaches to a continual competition mindset. Better, faster data-driven decisions advantage one player over another. PPUPTT uses explainable process technologies which predict complex systems next-state, and identify fleeting opportunities to maneuver and increase adversary costs of operations. Process technologies are core to PPUPTT’s architecture. Developed originally in a business context for organizations to model the processes which run the business, PPUPTT uses process mining to model complex systems as temporal, unfolding processes. Using process models outputs, PPUPTT derives time series information entropy measures and depicts entropy with intuitive, engaging dashboards fueled by explainable artificial intelligence--making it easy for those without extensive data science training to use the software, understand the analyses, and communicate recommendations. Information entropy, which is linked to Ashby’s Law of Requisite Variety, is a measure of the state of possibilities within complex systems. Changes in information entropy reflect changes in the distribution (variety) of activities, which uncover planning and execution cycles inferences, cognitive state comparisons among agents, and ecosystem stimulus-response dynamics. Creative TTPs built using these measures improve the ability for blue to pursue goals by increasing blue variety of TTPs while simultaneously tarnishing red’s variety of activities. It does this by monitoring ecosystems of interest continually and providing operators with actionable insights tailored to their specific needs. Finally, PPUPTT speeds decision making and automates rote analyses, which frees up time for human sense making. PPUPTT operationalizes an aspect of complexity science by synthesizing research from noteworthy thinkers. For example, Shannon’s Information Theory and simple entropy calculation is used to identify surprising confluences of events which can be used for persistent monitoring, alerting, and decision making based upon recent and historical activity. Information entropy describes the variety of activities or state of possibilities within complex systems. Inputs and outputs are in open data standards which facilitates collaboration with adjacent systems. PPUPTT enables maneuver warfare grounded in complexity science, can be operationalized quickly, and will increase in value as more statistical insights are discovered and TTPs developed.

There is a pressing need to exploit fleeting opportunities continually--real time anticipatory ecosystem entropy offers a lens for these kinds of exploitations. We want to measure ecosystem entropy continually to make better, faster data-driven decisions which advantage one player over another. Combining entropy measures with process models, we seek to understand how dissipative structures interact with one another. Continual sliding window entropy measurements identify exploitation opportunities when a player’s ecosystem is especially chaotic or orderly. Process models provide turnkey simulation parameters which predict ecosystem next-states and identify exploitation actions. This concept may be applied to space, social media, critical infrastructure, urban, supply chain, macro-economic, natural, and geo-political ecosystems, as well. Efforts to date have neither examined ecosystems in a temporal process mining context using categorical data event logs nor analyzed interactions between ecosystem players in order to anticipate outcomes and improve decision making. A highly adaptable, domain agnostic information maneuver capability is possible. It will be possible to drive entropy purposefully by anticipating ecosystem changes or inflection points and make decisions which favor one party over the other.

poster

### Welcome to the 7th annual workshop on Naval Applications of Machine Learning!

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

<button name="button" onclick="window.open('https://sites.google.com/go.spawar.navy.mil/naml/','blank')">**NAML 2023 Website**</button>

<br><br><br>

The 7th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Modeling for safety risk often involves infrequent events with sparse data, and there is simultaneously a low tolerance for both type 1 and type 2 errors. Frequent type 1 errors will cause a numbness to develop with respect to any warnings generated by the model, while frequent type 2 errors are a symptom of chronic underestimation of risk. Managing type 1 and type 2 errors in a balanced way is the objective of ensemble modeling. This method can help reduce type 1 errors so that a more sensitive model can be produced, which also in turn remediates the chronic risk underestimation. Ensemble modeling can leverage multiple techniques that tend to agree on the true positive and true negative results, while tending to have a diverse response with respect to false positive and false negative results. When these results are unified under a top layer model, each lower layer model's classification results are interpreted in context with all lower layer model results. The outcome of this process is a more robust model which amplifies the true positive and negative results that are common between ensemble components while deemphasizing the results that the components disagree on. This paper will review some generally accepted ensemble modeling strategies and provide a practical walkthrough of a layered ensemble process using Naval safety data and the R programming language, along with an evaluation of the limits associated with the improvement that can be expected from this technique.

Ensemble Modeling: Using Advanced Learning Techniques for Naval Risk Analysis and Prediction

What we want to aim for is a navy where the autonomous systems are allowed to do what machines do best. And leave humans for things that they do best, like making decisions and doing analysis and things like that. We may argue the fact over how, how good we are humans are at that. But, but we'll just focus on what do machines do best.

Keynote Talk: Rear Admiral Douglas Small

Automating Safety of Navigation with Machine Learning Technologies

Our project focuses on using machine learning and sensor data to predict quantities such as the refractive-index structure constant (Cn2), scattering, and extinction. Meteorological sensor data including temperature, relative humidity, wind speed, and surface pressure was input as predictors into RegressionLearner in MATLAB, with Cn2 and RSSI being the response variables. We provide a comparison of all the available machine learning algorithms. Principal Component Analysis (PCA) was included to determine the number of variables that explain the most variance. This presentation covers the initial results for our optical turbulence prediction trained on datasets from our collaborators at the Hellenic Naval Academy. 

Initial Results for Optical Turbulence Prediction Using a Bagged Decision Tree Model Trained on Geographically Diverse Datasets

First-principles computer models or simulations are often used as forward models for training reinforcement learning (RL) agents because training on real-world systems is too expensive, slow, or otherwise impractical. Even the highest-fidelity models can, however, fail to account for all physical variables and these modeling errors can lead to poor performance when the simulation-trained agent is exposed to the real-world. A potential solution is to use data from the physical system to directly train a deep neural network as an approximation (surrogate) of the system’s underlying state-transition function. Given difficulties inherent in acquiring sufficient real-world training data, one advantageous approach is to first train a neural network surrogate using a first-principles model and then use transfer learning and a smaller set of real-world data to fine-tune the surrogate’s approximations. An RL agent can then be quickly and cheaply trained using the well-trained surrogate and can exhibit better real-world performance compared to an agent trained on a first-principles model alone. We propose a unique algorithm for generating training data appropriate for this type of surrogate, explain several important considerations in posing the learning objective function, describe the training techniques used to achieve outstanding performance for several surrogate models before and after transfer learning, and present results for an RL agent trained using a surrogate as the forward model. More specifically, we consider the problem of training RL agents to control an Unmanned Underwater Vehicle (UUV) in an environment characterized by current flow fields. We include discussion of how our novel data generation algorithm can be extended to work for a UUV navigating such an environment and explain how an egocentric state-space definition can improve the performance of an RL agent trained to control the UUV.

Reinforcement Learning for UUV Control Using Stable and Accurate Neural Network Approximations of Motion Dynamics

"Models which predict optical turbulence (as measured by Cn2) from macro-meteorological data are expensive to develop and often perform poorly when deployed in new environments.
Hybrid models combine these existing models with local measurement and a Gradient Boosted Decision Tree (GBDT) architecture to improve predictive power.
This architecture achieved approximately a 29% improvement over the baseline using only one day of measurement, and 68% using 180 days of observation.
"

Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements

Internet has become a ubiquitous influence in today’s age; however, internet activities have become vulnerable and detrimental due to malicious actions if not secured properly. For example, large scale data breaches such as those indicted at Yahoo and Equifax, essentially exploited the weaknesses of the traditional username/password authentication mechanism. Therefore, it has become imperative for us to find additional ways to protect our data, which also needs to be low-cost and unobtrusive. Existing authentication mechanisms can be categorized into what you know (knowledge-based), what you have (possession of physical things) and what you are (biometrics). Biometrics-based authentication can be further categorized into physiological and behavioral biometrics. Physiological biometrics are based on physiological characteristics of a person such as fingerprints, iris, facial recognition and palmprints, which can cumbersome due to the cost and usability. On the other hand, behavioral biometrics use the behavior of a person interacting with a device such as mouse dynamics (i.e., different mouse movements on a graphical user interface (GUI)), widget interactions (i.e., mouse cursor hovering over different icons or buttons on a GUI), keystroke dynamics, swipe, motion, walking or gait, all of which are inexpensive and unobtrusive techniques that can be leveraged to strengthen existing authentication without interfering with day-to-day computer operations. The experimental work of this proposal includes two novel contributions: 1) a survey of the state-of-the-art literature on mouse dynamics and widget interactions and 2) designing and validating fusion of mouse dynamics and widget interactions’ modalities on a realistic and popular website (i.e., PayPal and Facebook) utilizing our novel datasets. We performed literature survey on mouse dynamics with widget interactions along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), fusion and performance, and limitations. Secondly, we experimented with trajectory driven window mapping to fuse mouse dynamics and widget interactions modalities using a total of 10 users for PayPal with an average of 12 minutes of data per user and a total of 15 users for Facebook with an average of 2 hours of data per user. Our mean EER rates with a score-level fusion of mouse dynamics and widget interactions are 7.64\% (SVM-rbf) and 3.25\% (GBM), for PayPal, and 5.49\% (SVM-rbf) and 2.54\% (GBM), for Facebook. We further improve the EER rates by combining decision scores from multiple consecutive trajectories, which yields a 0\% mean EER after 11 decision scores across all the users for both PayPal and Facebook.

User Authentication by Fusion of Mouse Dynamics and Widget Interactions: Two Experiments with PayPal and Facebook

Estimating Sound Speed Profiles Using Vessel Source Signals of Opportunity

RippleGo is an AI-based voyage planner for the Inland Water Transportation industry.  In this talk, two AI-based technologies will be discussed and how each of the technologies improve safety and efficiency along the waterways.  One is a river level prediction model that forecasts river levels and the other is a travel time prediction model that forecasts time taken to go from a starting point to destination along the inland waterways.  Combining the 2 predictive technologies and other marine data sets, TRABUS developed a software-as-a-service application that enables operators in the inland waterways to plan and execute their voyages, safely and efficiently.  The talk will provide an overview of some of the core AI-driven capabilities of RippleGo.

RippleGo - First AI-Based Voyage Planning and Predictive Analytics Software for the US Inland Waterways

Coupled dynamical systems, especially those comprised of hysteretic elements, possess remarkable behavioral similarities to some aspects of neural activity in living systems. While some features of this similarity have been explored, the results have not yet been leveraged to improve machine learning techniques or artificial neural networks. In this work, a dynamical system of coupled overdamped bistable elements subject to a noise floor assists in information transfer. With the noise allowing the system elements to switch randomly between their stable states, the system response is quantified via a steady state probability distribution. A simple five element realization of this network is used to produce the analog equivalent of an XOR gate, an “XOR transform”, through the proper choice of coupling coefficients and controllable external biases. The demonstration of the XOR transform indicates the ability of the system to synthesize more general functions, a necessary prerequisite for machine learning and computing applications. Finally, a silicon implementation is proposed and simulated using a verified process model that could be fabricated to generate a working analog computing system.

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Ensemble Modeling: Using Advanced Learning Techniques for Naval Risk Analysis and Prediction

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