United States

We constructed a scenario using a previously developed framework to educate junior developers on the importance of incorporating statistical analysis in the development of autonomy behaviors. We added rudimentary perception and obstacle avoidance to the previously developed framework, adding variable parameters for the range the autonomous agent could perceive its environment, the braking response curve, and the maximum speed through which the agent could traverse its enivronment. By simulating the agent avoiding obstacles through an environment it has no a priori knowledge of and analyzing the frequencies with which the agent either collided or nearly collided with obstacles, we were able to identify patterns in the design parameters. For example, One-way ANOVA testing showed a statistically significant difference in collision avoidance success rate between the different braking curves across identical sensor ranges and maximum speeds (p=7.88e-24). Additionally, thresholds for agent survivability were identifiable by analyzing the sensor ranges and maximum speeds allowed in simulations. When combined with other core development best practices such as unit testing and version control, we believe we can better define intended behaviors and consequently improve the reliability of the behavior by conducting relevant statistical analysis on the design parameters of behaviors.

NAML 2023

Learning How To Learn: Emphasizing Iterative Design in Good Autonomous Framework Development

workforce development

autonomy

simulation

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

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

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>

To access the site please register [**here**](https://events.r20.constantcontact.com/register/eventReg?oeidk=a07ejmzkc5g71584a82&oseq=&c=&ch=). 

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.


The objective of this research project is to improve the warfighter’s maritime domain situational awareness by designing and developing a prototype payload for deployment on an unmanned or dismounted operator’s platform in support of various missions. The payload would have the ability to detect, track, and identify Signals of Interest (SOIs) by utilizing Convolutional Neural Networks (CNN) and Digital Signal Processing (DSP) techniques. The 1st year of this effort entailed developing a benchtop prototype capable of performing real-time SOI classification. The components of the prototype consisted of a Software Defined Radio (SDR) to stream the raw digitized In-phase and Quadrature (I/Q) components of the signal and a System on a Chip (SoC) development kit to perform real-time DSP and SOI classification via the CNN. The SOIs were classified based on modulation type, wireless protocol, and emitter type. Two network architectures were developed using a standard image approach and a complex value I/Q approach to evaluate the benefit of I/Q input format. DSP techniques such as power normalization, down sampling, up/down converting, and time averaging were implemented to elicit better generalization and accuracy of the trained CNN. This research project effort has been selected to continue into a second year. Future work will focus on Low Probability of Intercept (LPI) signal as the SOI and applying machine learning to both detection and classification of low and negative Signal to Noise Ratio (SNR) signals.

Spectrum Awareness Persistence Payload

Synthetic data generation is crucial to performative and predictive autonomy technology research. The efficient generation of robust synthetic data requires 3D Computer-Aided Design (CAD) models that correctly represent the target but sufficiently small as to promote agile computation. "Watertight" CAD models - models with no gaps or surface flaws - are necessary for computer simulations, generation of synthetic datasets for machine learning, and accurate synthetic data analysis. This is joint work with Dr. Igor Ternovskiy (AFRL/RYAT) and Ms. Elizabeth Sudkamp (AFRL/RYAP).

A Facet to Watertight CAD Converter

Naval Aviation Support Equipment (SE) PMAs lack an automated methodology to produce a failure mode and system identification. An automated capability would facilitate a consistent, repeatable, and time savings process across the sixteen SE PMA’s portfolio of equipment. The NAVAIR methodology of recording equipment maintenance entails completing a Maintenance Action Form (MAF). Deficiencies in the MAF process allow maintainers to enter overly generic systems and root causes for the maintenance action. However, the MAF includes underutilized fleet narratives that describe the equipment discrepancy and the corrective maintenance action. This research investigates whether an unsupervised machine-learning algorithm can process fleet text narratives and identify SE Failure Modes and system affected. 

The research proceeded by employing Natural Language Processing (NLP) to the MAF unstructured text data. The researchers acquired data comprising of several pieces of equipment (contained in separate files) of NAVAIR equipment that contains a variety of failure modes and systems. The unstructured maintenance text was preprocessed utilizing lemmatization, lowercasing characters, removing punctuations and stop words, replacing acronyms/abbreviations, and stripping white space. The researchers plan to apply a Latent Dirichlet Allocation (LDA) topic algorithm to the processed unstructured text iterated across a sequence of topic quantity hyper-parameters. A harmonic mean function can be used to identify the optimal number of hyper-parameter topics. The researchers will apply the LDA algorithm with the optimal number of hyper-parameter topics against the dataset. 

The objective of this project consists of determining whether NLP techniques can identify SE Failure Modes and System affected using unstructured text fields found in the MAF. The project is currently in progress and should result in an automated solution to extract accurate labels from the text data. This solution will allow the stakeholders to identify root causes of Support Equipment failure and apply the necessary resources to rectify the failure mode quickly and more efficiently.

Unsupervised Support Equipment (SE) Failure Mode Identification

We demonstrate an end-to-end neural network approach for acoustic source detection and classification on a linear sensor array. We show that the neural network learns optimal spatial and frequency filters as it performs beamforming-based signal enhancement and finite impulse response (FIR) filtering to isolate source-specific energy signatures. The trained network shows robust performance as a function of signal-to-noise ratio.

An End-to-end Neural Network for Acoustic Source Detection and Classification on a Linear Sensor Array

This study investigates the ability of deep learning methods to identify salience of radio frequency (RF) data. We primarily study this through sensitivity and are Unfortunately, RF data exists in the presence of noise and nuisance signals. We define noise as any perturbation on the data which obscures signal, while nuisance is a signal within the data we are not interested in.

Exploring Salience Metrics of Radio Frequency Data

Current and future methods depend on more data and computational power to ensure mine counter measure(MCM) maritime dominance. Using Synthetic Aperture Sonar(SAS) Side-Scan data, we show how we are tackling the challenge of limited training data.

ATR for UUV Side-Scan and Synthetic Aperture Sonars

In this work, we leverage recent understanding to analyze the utility and practicality of cross-experiment transfer learning. We implement a practical approach where a pre-trained global model is applied on the fly to a new subject in an entirely new experiment using different EEG hardware. We are interested in quantifying the extent to which existing data from similar experiments may be useful to learn features of interest. Ultimately, this work aims to reduce the burden of required data per subject for learning common MI features to help design a system that is practically feasible and convenient. 

Cross-Experiment Transfer Learning for Real-time Brain Computer Interfaces 

Case-based reasoning (CBR) systems provide an interpretable reasoning process, supporting user trust; their decisions can be justified and explained in terms of the retrieved prior cases on which the decisions were based. A key factor that influences the trustworthiness of CBR systems is the quality of their retrieval, which depends on the features aggregated in cases to enable case comparison. Developing a feature vocabulary by traditional knowledge acquisition can be expensive, but the success of machine learning with deep neural networks suggests the potential to harness network learning to automatically generate features/indices for CBR retrieval. Initial studies have demonstrated that this approach can be effective, but related topics such as how such features may augment incomplete knowledge-engineered feature sets, how the highest-quality features may be extracted, and which network models may be most conducive to CBR feature extraction are poorly understood. This poster describes work addressing these questions, in which a CBR classifier is used as the primary system to support trust in model decisions through interpretability and a deep learning model is used to generate high-quality features to increase the accuracy of the CBR system.

On Extracting Network Features for Case-Based Reasoning

Artificial Intelligence (AI) decision aids are becoming increasingly utilized across domains, including for manpower and personnel decisions, because they offer effective and efficient mechanisms for collaboratively supporting human decision-makers. However, despite the computational, data-driven benefits of leveraging AI agents, prior studies highlight that bias may introduce glaring anomalies that may greatly hinder the decision-making process of such collaborative endeavors. Further, as AI systems become commonplace, humans often become over-reliant and complacent about AI decisions, allowing such biases to go undetected and unmitigated. Despite abundant data-driven and algorithmic bias mitigation solutions, several prior investigations have indicated that these technical solutions alone are insufficient and demonstrate the need for human intervention. While humans play a critical role in bias detection, research on affordances to boost bias detection and mitigation in human-AI collaborative contexts remains underexplored. To address this gap, this presentation discusses an investigation of the potential of three different cognitive forcing functions (CFFs), 30-second wait, update, and on-demand, which were chosen for the demonstrated power of CFFs in the medical context to boost analytical and deliberative thinking. We apply these to explore the potential of CFFs to enhance user interaction and interpretation of AI to support the bias detection capabilities of human users. Given the widespread use of AI tools to screen applicants for hiring and relevance to manpower decisions in Naval settings, we frame our investigation within a human resources context. Using a within-subjects factorial survey, we asked participants (N=152) to take on the role of a human resources assistant and work with an AI decision aid to evaluate candidates. The results indicate that these CFFs significantly influenced performance measures, including assessing the fairness of the AI’s decisions. We also found that these CFFs all had significant effects on increasing physical and perceptual interaction and interpretation measures aimed at facilitating greater bias detection capabilities in the process. While similar outcomes were observed across all the CFFs, we observed a greater impact on recognizing bias and enhanced trust associated with the update CFF. These empirical findings expand existing scholarship by playing a critical role in understanding how affordances that enhance bias detection and mitigation can be envisioned to boost the overall efficacy and efficiency of human-AI collaborative ventures, which can have widespread application across multiple different contexts in the Navy that leverage AI-driven decision-making. Such visions can be crucial for conceptualizing intentional designs to increase interaction and interpretation of AI decisions that help counter AI complacency, thereby boosting bias mitigation capabilities in human-AI collaboration.

Promoting Human Interpretation and Interaction to Mitigate Bias in Artificial Intelligence Assisted Decision Aids Through Intentional Interface Designs

Many different complex physics-based algorithms can be used to predict the quality of channels in radio frequency (RF) communications with varying levels of fidelity across many parameters. This project aims to use a generative adversarial network to accomplish the same prediction task. RadioML and GNURadio were used to generate datasets composed of short signal sections of IQ data for various modulations and SNRs. Two different conditional GANs, pix2pix and Wasserstein, were trained on paired and unpaired RF datasets containing several modulation schemes and channel effects in order to investigate the different GAN’s potential to learn the complex channel effects that can occur during RF transmission. After training on the synthetic data, the output of the conditional GANs qualitatively resembled the training data. As in the image domain, metrics for GAN outputs are difficult to quantify, and we are working on appropriate measures to rank GAN performance. This is a first step toward training a GAN that can qualitatively and quantitatively reproduce the transformation between the transmitted RF data and the received RF signal. An HF dataset labelled with the HF channel metadata (e.g. time of day, sun spots, etc.), the conditional parameters for the GAN, is being acquired for future training. While the HF data collection effort continues we are addressing issues of short, discrete IQ samples by investigating different GAN architectures that can enable the generation of longer sequences.

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Learning How To Learn: Emphasizing Iterative Design in Good Autonomous Framework Development

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