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Machine Learning Explainability

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Novetta applies machine learning models to help customers make better decisions across a wide range of use cases. Increasingly, decision makers want to understand why a model makes the decisions that it does. Model explanations help our customers gain an understanding of how our models function, while enabling us to debug our models and assess them for bias and fairness. In this blog post, we describe the tools and techniques we use to generate explanations from the predictions of our machine learning models. Interpretable Models Selecting an inherently interpretable “white-box” model is an obvious way to gain insights on how...

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Resolving Dates with NLP

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In newspaper articles, emails, and other texts, it is natural to reference dates in relation to the publication date. For example, you will commonly see phrases like “yesterday” and “two weeks ago.” However, if you read an article a year after it was published, these timeframes are not immediately clear. When analyzing articles using natural language processing (NLP) techniques, relative dates referenced in an article will not be interpreted properly, if at all, since there is no context for the models. Thus, in order to effectively use tools such as event extraction and question-answering engines, relative references need to be...

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Redefining the Disaster Response Landscape with AWS

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by Matt Johannessen Over the last six months, Novetta has had the privilege of collaborating closely with the AWS Snow Family and AWS Disaster Response teams. Working with AWS, we have integrated Novetta’s mission proven C4ISR solution, called Ageon ISR. With emerging IoT and Machine Learning capabilities, Ageon ISR is deployed on the rugged AWS Snowball Edge Compute Optimized device. The device acted as a computing and storage hub at the temporary incident command center at two different massive disaster response events. Novetta used the Snowball Edge device to successfully track the location personnel, vehicles, and equipment involved in the...

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State-of-the-art NLP made easy with AdaptNLP

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by Andrew Chang, Brian Sacash

Natural Language Processing (NLP) has advanced significantly since 2018, when ULMFiT and Google’s release of the BERT language model approached human-level performance on a range of use cases. Since then, several models, such as XLM, GPT-2, XLNet, and ALBERT, have been released in quick succession, each improving on its predecessors. While these state-of-the-art models can solve human-level, language-based tasks on large volumes of unstructured text for certain use cases, getting a handle on what to use, when to use it, and how to use it can be a challenge. At Novetta, we explored what it would take to streamline the...

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IoT & Edge Computing for Disaster Response

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by Matt Johannessen Recently, I had the opportunity to lead a Novetta team tasked with demonstrating how C4ISR, IoT, Machine Learning, and edge computing technologies can improve situational awareness for disaster response operations. Through our partnership, Amazon Web Services invited Novetta to participate in an event called Operation Convergent Response (OCR). OCR is an event hosted annually by Verizon & Nokia at the Guardian Centers facility in Perry, GA. The primary objective of OCR is to bring solution providers and first responder organizations together to demonstrate how emerging technologies can save lives. Unlike traditional technology trade shows, OCR is centered...

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Detecting Semantically Similar Code at Scale with SACS

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Maintaining software security in a project as the volume of new code increases is a pressing problem in big data. Once a vulnerability is identified in a piece of software, it may be essential to locate other similar weaknesses, but conducting this type of search is time consuming and challenging. Currently, tools used to aid in searches like this are only capable of finding exact matches, but similar vulnerabilities may be expressed using different semantics. To address this problem, we developed an approach to efficiently find code with similar functionality in large repositories of executables: Syntax-Agnostic Code Similarity (SACS). SACS...

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NovettaNLP

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BERT’s release in November 2018 marked an inflection point for Natural Language Processing (NLP), moving us closer to human-level performance on a range of use cases. Since then, many transformer-based language models, such as XLM, GPT-2, XLNet, and roBERTa, have been released, with each subsequent model raising the performance ceiling. With the availability of these state-of-the-art pre-trained language models, we can produce NLP models that solve a variety of downstream language-based tasks, enabling our customers to quickly analyze large volumes of unstructured text. The challenge with using these models is that they each differ slightly in their application. For example,...

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A Powerful, Transformers-Based Framework for End-to-End Natural Language Processing Tasks

Congratulations to our 10^27 Award Winners!

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Novetta grants the 10^27 Award to recognize excellence in IRAD execution and delivery. Our latest group of recipients developed important, innovative capabilities in support of customer missions. 10^27 Award recipients: Lara Dedic, Kyle O’Brien, Michelle Rybak, and Michelle Jou   Lara conceived and led development Syntax Agnostic Code Similarity (SACS), a cyber defense tool that allows developers to detect multiple pieces of code that have similar functionality, regardless of how the code was written or compiled. Developed within the Machine Learning Center of Excellence, SACS is being transitioned to support cyber missions. SACS was selected for presentation at the JAIC...

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Adversarial Attacks in Machine Learning: Defenses

Category: Advanced Analytics / Novetta Blog Tag(s):  /  /

by Shauna Revay, PhD, Brian Sacash

Introduction to Adversarial Attack Defense Methods In our previous blog posts, we introduced and experimented with adversarial attacks against deep learning models. Here, we explore ways to mitigate or decrease the effectiveness of some of these attacks with three defense methods: 1. Modifying the feature space of the neural network itself 2. Modifying an input to a network before it is used for inference 3. Modifying the training data to account for adversarial inputs Modifying the Feature Space of a Neural Network In our first experiment, we implemented a technique [1] that modifies the feature space of the weights for...

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Part 3 of 3

Adversarial Attacks in Machine Learning: Experimentation

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by Shauna Revay, PhD, Brian Sacash

As machine learning (ML) becomes more prevalent, bad actors seek to take advantage of our confidence in models’ predictions. An example of this can be found in autonomous vehicle safety, wherein the application of a small sticker to a stop sign causes the vehicles’ ML models to incorrectly classify and interpret the visual cues on the sign [1]. In an exercise to better understand common vulnerabilities in model deployment, we experimented with a handful of prevalent adversarial attacks to understand their viability and demonstrate their potential. Figure 1: A human can easily discern this as a stop sign with stickers...

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Part 2 of 3