United States

NIWC PAC Code 584 in Philadelphia has achieved success in developing tools in the NLP fields of predictive AI and generative AI. For generative AI, we developed a NIWC PAC custom GPT Chatbot that is now answering questions about documents provided by our Public Affairs Officer. The NISE-funded chatbot is a collaborative effort between Code 584 and the PAO’s office and will provide Distro A outputs in the first version, and then a customized chatbot for CUI scientific and engineering material in the second version. These will be the first GPT chatbots deployed as digital assistants for every Center employee and will serve as prototypes for future work, including efforts for the NIWC PAC vision of Making Kennedy Real.

The GPT chatbot is powered by an open-source large language model and uses machine-learning algorithms to analyze and understand the context of a conversation and then retrieve relevant information from NIWC PAC’s Technical Library, a digital archive of over 1,400 technical reports, technical documents, journal articles, and conference papers. The chatbot introduces enhanced problem-solving and accelerated innovation within the research and development process. These new solutions offer an opportunity to improve workflow while also disrupting old ways of doing business and generating new ways to think and act creatively, allowing us all to generate novel ideas and uncover new insights, accelerating the pace of innovation within our organization.

For predictive AI, Google’s open-source Bidirectional Encoder Representations from Transformers (BERT) model remains a workhorse as a foundational NLP model for the division. BERT has skyrocketed since Code 584 began using it in 2020, and over 150 research studies have improved on the BERT large language model. 

In the first predictive AI success story, the BERT-based Technology Transition Classifier (TTC) continues to serve our NIWC PAC Business Portfolio Managers in quarterly data calls. The TTC is an internal application that uses NLP to find technology transitions within NIWC PAC weekly highlight submissions. The TTC is a binary classifier, meaning it predicts if a weekly highlight either involves a technology transition or does not involve a technology transition. The TTC provides classification and a confidence level for each prediction. Each quarter, the application provides an analysis on all NIWC PAC Hub highlights to the BPMs with 96% accuracy.

Our second success in predictive AI is the Controlled Unclassified Information Categorization Automation Tool. The CUICAT is an NLP application that automatically detects and categorizes CUI in Microsoft Office documents and .pdfs. The tool can identify and label five categories of CUI: privacy, health, procurement, legal, and non-CUI; and was a top finisher in NAVSEA’s nationwide Challenge.gov competition. The CUICAT is now funded as a pilot project where we are expanding the CUI categories and targeting deployment on Microsoft Azure.

Lastly, NIWC PAC Code 584 recognizes that an often-overlooked aspect in AI development is how to get prototypes to the fleet. The Navy&#39;s adoption of AI should have a well-defined pathway to ensure the sustainment. To that end, Code 584 is working with the PEO for Digital and Enterprise Services who has a long history of driving IT transformation and is ideal to manage acquisition and longevity of an AI program. Last year, the PEO established a “Pilot-to-Production” (P2P) process to introduce AI tools to the Navy under a portfolio of AI pilot projects. The P2P acquisition pipeline has a three-year horizon from concept to IOC, but pilots can launch from any one of the three horizons depending on technical maturity and other factors. The process uses industry standard DevSecOps principles to instill rigor at every phase of the lifecycle. We believe PEO Digital’s P2P roadmap is well-suited for seeding prototypes and to sustain AI capabilities for the Fleet well into the future.

NAML 2024

NIWC PAC Code 584 Machine Learning &amp; Natural Language Processing: Review of Accomplishments &amp; Future Direction

technology transition classifier

cui categorizer

peo digital

technical library

niwc pac

llms

classifiers

chatbot

bert

### 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>

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NAML 2023

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.


NIWC PAC Code 584 in Philadelphia has achieved success in developing tools in the NLP fields of predictive AI and generative AI. For generative AI, we developed a NIWC PAC custom GPT Chatbot that is now answering questions about documents provided by our Public Affairs Officer. The NISE-funded chatbot is a collaborative effort between Code 584 and the PAO’s office and will provide Distro A outputs in the first version, and then a customized chatbot for CUI scientific and engineering material in the second version. These will be the first GPT chatbots deployed as digital assistants for every Center employee and will serve as prototypes for future work, including efforts for the NIWC PAC vision of Making Kennedy Real.

The GPT chatbot is powered by an open-source large language model and uses machine-learning algorithms to analyze and understand the context of a conversation and then retrieve relevant information from NIWC PAC’s Technical Library, a digital archive of over 1,400 technical reports, technical documents, journal articles, and conference papers. The chatbot introduces enhanced problem-solving and accelerated innovation within the research and development process. These new solutions offer an opportunity to improve workflow while also disrupting old ways of doing business and generating new ways to think and act creatively, allowing us all to generate novel ideas and uncover new insights, accelerating the pace of innovation within our organization.

For predictive AI, Google’s open-source Bidirectional Encoder Representations from Transformers (BERT) model remains a workhorse as a foundational NLP model for the division. BERT has skyrocketed since Code 584 began using it in 2020, and over 150 research studies have improved on the BERT large language model. 

In the first predictive AI success story, the BERT-based Technology Transition Classifier (TTC) continues to serve our NIWC PAC Business Portfolio Managers in quarterly data calls. The TTC is an internal application that uses NLP to find technology transitions within NIWC PAC weekly highlight submissions. The TTC is a binary classifier, meaning it predicts if a weekly highlight either involves a technology transition or does not involve a technology transition. The TTC provides classification and a confidence level for each prediction. Each quarter, the application provides an analysis on all NIWC PAC Hub highlights to the BPMs with 96% accuracy.

Our second success in predictive AI is the Controlled Unclassified Information Categorization Automation Tool. The CUICAT is an NLP application that automatically detects and categorizes CUI in Microsoft Office documents and .pdfs. The tool can identify and label five categories of CUI: privacy, health, procurement, legal, and non-CUI; and was a top finisher in NAVSEA’s nationwide Challenge.gov competition. The CUICAT is now funded as a pilot project where we are expanding the CUI categories and targeting deployment on Microsoft Azure.

Lastly, NIWC PAC Code 584 recognizes that an often-overlooked aspect in AI development is how to get prototypes to the fleet. The Navy's adoption of AI should have a well-defined pathway to ensure the sustainment. To that end, Code 584 is working with the PEO for Digital and Enterprise Services who has a long history of driving IT transformation and is ideal to manage acquisition and longevity of an AI program. Last year, the PEO established a “Pilot-to-Production” (P2P) process to introduce AI tools to the Navy under a portfolio of AI pilot projects. The P2P acquisition pipeline has a three-year horizon from concept to IOC, but pilots can launch from any one of the three horizons depending on technical maturity and other factors. The process uses industry standard DevSecOps principles to instill rigor at every phase of the lifecycle. We believe PEO Digital’s P2P roadmap is well-suited for seeding prototypes and to sustain AI capabilities for the Fleet well into the future.

NIWC PAC Code 584 Machine Learning & Natural Language Processing: Review of Accomplishments & Future Direction

technical paper

### Welcome to the 8th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. 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>

**[More about NAML 2024](https://sites.google.com/go.spawar.navy.mil/naml/)**
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Please log in to explore this workshop.

The 8th 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.


COSMOS is modern web application that enables automation/orchestration between COTS platforms that provides customers an ability to self-onboard, build and secure GovCloud Amazon Web Services (AWS) Impact Level (IL)-4/5 workloads within minutes. COSMOS provides an environment to achieve flexible and agile speed to capability in the face of rapidly evolving threats and missions, enabling mission owners to make the superior decisions necessary to win in battle.

COSMOS provides 200,000% faster onboarding than any other GovCloud environment; a Cloud Native Access Point architecture that provides accessibility from anywhere, any device; high availability standards; inheritable security through federated DoD M365 tenet identities; continuous monitoring for cloud resources; and on-demand Risk Management Framework (RMF) Authority to Operate (ATO) artifact generation to promote a continuous state of cybersecurity awareness and readiness.

COSMOS: Revolutionizing GovCloud & Cybersecurity

Practitioners of AI/ML who are applying their skills to RF problems often intially focus on the signal detection and classification problem. Though this is a rich and interesting problem, this talk seeks to provide an overview of several alternative applications of ML techniques that go beyond signal detection and classification. These include learned signal processing, generative AI for channel modeling, and fully-learned communications waveforms.

RFML Beyond Signal Classification: ML Signal Processing, Generative AI for Channel Modeling, and Learned Comms for 6G

Making Large Language Models (LLMs) Accessible, The need for an accessible and user-friendly platform for working with LLMs. 



CPU is all you need

Typically, effective object detection in images requires thousands or tens of thousands of labeled instances. Crowd-sourced data has been used to train object detectors for commercial purposes, but the DoD cannot use this same approach to train neural networks with supervised learning. Either there is an insufficient number of instances of DOD-relevant data, or curating and labeling that many images is too cost and time prohibitive to be of use. ​

For this reason, few-shot object detection can be used to limit training data to tens or hundreds of instances rather than thousands upon thousands. Specifically, our experiments adapt few-shot object detection to the Naval domain. We set up two experiments where in the first experiment, we use a large, common dataset to train a base network followed by training with a small sub-group of novel classes of data; and, in the second experiment, we test another few-shot network, DE-Vit, which can use few-shot methods to find new objects in an image. ​

There are many methods that can be used to train neural networks with fewer data. These experiments are conducted as a part of the SAVANT project—which uses an ensemble of semi-supervised methods to build upon datasets. It will serve as a test bed for semi-supervised methods while creating some much-needed labeled Naval data.

Adapting Few-Shot Learning To The Naval Domain

Naval aviation assets on aircraft carriers such as aircraft, catapults and arresting gear are tracked through information systems that provide situational awareness to sailors. The current concept of operations requires highly skilled workstation operators to manage everything from aircraft status to flight schedules to keep information up to date. Operators receive information over radio communication channels from flight deck personnel. This process requires the operator to be dedicated to listening to updates and manually entering what they hear into workstations. This project developed an automated speech recognition prototype that can automatically listen into the audio communication pipeline and add information into computer systems.  
A speech recognition pipeline was created to recognize the standard phrases spoken by aircraft carrier flight deck personnel. Outputs can be used to correctly identify the actions that need to be performed to update computer systems automatically. The pipeline includes speech to text Automatic Speech Recognition (ASR) models and speech enhancement followed by post processing and similarity matching techniques to increase accuracy. Recursion was implemented to re-run the pipeline with a larger speech model if the result does not match a standard flight deck phrase. The team made use of Whisper, a transformer-based model with various model sizes ranging from 39 million to 1.56 billion trained parameters. The pipeline was tested in a reverberation chamber using flight deck phrases under various noise levels and model sizes. 
The team is currently working on fine-tuning the model and creating synthetic data. Data generation used by the team include the ability to fabricate semi-realistic speech in 108 varying voices and dialects. The team has tested fine-tuning efforts on a hold-out percentage of the dataset but has plans to continue fine-tuning the whisper model on both real-world and synthetic data to note results on increased model performance. While high-decibel noisy recordings were obtained from a laboratory, the researchers hope to obtain more real-world data and ultimately test the system either in a simulated environment or the flight deck environment. 


Aircraft Carrier Flight Deck Information Updates using Speech Recognition 

The signal landscape is constantly changing as technology evolves. However, much of the work done in Few-shot Learning (FSL) has been done in the image domain. The goal of this study is to evaluate the effectiveness of various FSL methods from the image domain in the signal domain. We were able to transfer FSL methods into the image domain using an open-source signals dataset. Using one-shot learning techniques, Deep Learning models are able to identify signals of unseen modulation types with performance close to baseline classical classification tasks.

Novel Signal Classification using Machine Learning

The study will analyze how to apply USMC data governance advances to JADC2. The long term objective is to advance Project OVERMATCH and JADC2 with respect to improving processes for leveraging data and machine learning to achieve decision advantage.
The immediate study objective is to answer the following questions: 1. How can recent USMC advances in data governance be applied to advance Project OVERMATCH and JADC2? 2. How can formalized data governance processes and automation improve the quality of decisions and/or speed up data-driven C2? 3. What new data sources hold the most value for improving C2 and decision advantage? Navy and Marine Corps officers will be part of the research team. Leveraging faculty expertise and consulting with subject matter experts, students will carry out literature surveys and case studies to determine answers to the study questions and assess the merits of new approaches addressed by the study. This study is part of the Navy/USMC contribution to JADC2 and has potential to impact capability development & integration efforts to related programs of record.

Data Governance in Support of Joint C2

Jupiter - AI Enablement

Deep neural network sensitivity to varied radar waveform properties for radar classification and domain adaptation

You don't need to understand how to fly a plane or design a plane to understand what aviation can do for war. The same thing with artificial intelligence.
This talk walks you through strategic alignment, how the Marine Corps is thinking about implementing AI across the Corps and in support of both Marine Corps operations in the broader naval mission. 

Marine Corps is very good at fighting. But if you look at how we fought for the last 50 years compared to where we could be in the future, we certainly can fight smart. The ability of MC to pull in data, to pull from different sources, from different venues and things, apply analytics, and help us to decision advantage across a range of what we do. Whether that is at the pointy end of the stick, whether that's recruiting, whether that's our internal talent management, whether that's logistics, whether that's supply system,the ability for us to leverage some of these tools is not near infinite, but we have an enormous amount to do and we want to overall be able to fight smart. But right now we're not and this is our path to how we're gonna get to that to take into account this the emerging technology where we think this not only where AI is today, but where we think AI is going over the next few years and then in the beyond into the 2030.

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COSMOS: Revolutionizing GovCloud & Cybersecurity

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NIWC PAC Code 584 Natural Language Processing tools