Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. As introduced in our first blog post, we built a simple iOS application alongside an AWS pipeline to provide an end-to-end solution to diagram creation. AWS was the obvious solution to build a truly scalable system capable of supporting multiple users creating diagrams at once. We were already using GPU enabled EC2 instances during the development of our object detection model, hence most…
ADSynth Blog Series: Defining the AWS Architecture Diagram
Part 3 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. ADSynth leverages numerous image processing techniques to properly translate a whiteboard sketch into an editable, digital architecture diagram. These techniques are used to ensure that all detected components are connected and formatted correctly. This post describes how we utilized our YOLOv3 object detection model predictions in conjunction with the image of the whiteboard sketch to obtain a clean, presentable graphic ready for insertion…
ADSynth Blog Series: Object Detection Model and Training Data
Part 2 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. We began with the backbone of ADSynth: the object detection model that makes diagram synthesis possible, as well as the data necessary to train the model. Implementation of YOLOv3 Architecture Based on the research we conducted on object detection, the architecture we decided to implement was YOLOv3. YOLOv3 is a deep neural network comprising of 106 layers and almost 63 million parameters. The…
ADSynth Blog Series: Digitizing Whiteboard Diagrams
Part 1 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. This summer, we interned with Novetta’s Machine Learning Center of Excellence (ML COE). As computer science students with a passion for machine learning and problem solving, we were excited to have the opportunity to apply our classroom knowledge in an industry setting. The experience was rewarding, though transferring our theoretical understanding of machine learning to practice presented a steep learning curve (pun intended)….
Deploying Advanced Machine Learning Models at Scale
Novetta is using deep learning to automate advanced analytic pipelines for our customers. These new capabilities identify important pieces of information across large text, image, and video data sets, a challenge faced by many of our Defense and Intelligence Community customers. We developed Argo, a standardized, Docker-based model training and deployment pipeline, to enable rapid evaluation and deployment of state-of-the-art deep learning models. Argo enables models to be deployed as endpoints and, when used with Amazon SageMaker, it forms the foundation for us to deploy, scale, and manage our deep learning models. Simplifying infrastructure setup through Docker and AWS allows…
20% Accuracy Bump in Text Classification with ME-ULMFiT
Introduction In a previous post, we discussed an enhanced method of passing additional data to the ULMFiT1 algorithm for unstructured text classification, which improved accuracy by up to 9%. In follow-on experiments, we have demonstrated that this approach – in which we prepend metadata to the text to provide more information to the model – increased accuracy by up to 20%, in relative terms. This improvement required just a few lines of Python, such that the performance boost comes at essentially no cost. This blog post explains the findings of this more-detailed evaluation, where we have compared how the following…
Training an ML Model on the Show Floor: Rapid Prototyping at AWS PSS
AWS PSS attendees interact with Novetta’s Machine Learning Prototype demo on the show floor. Introduction In our previous blog post, Sharpening the Edge with IoT, ML, and Homomorphic Encryption, we discussed our prototype application with the following functionality: Identify when a simulated license plate (alphanumeric characters on a white card) appeared in a live video stream Extract text from the license plate card using Amazon Rekognition Securely search an encrypted watchlist for this text Return any matches to the user Novetta and Enveil demonstrated this prototype at AWS Public Sector Summit, June 11-12. This blog post is an…
Sharpening the Edge with IoT, ML, and Homomorphic Encryption
An interactive demo at AWS Public Sector Summit • Washington, D.C. June 11-12
Edge compute capabilities are central to many of our customers’ missions, and are only growing in importance. For complex workloads like field forensics or digital media triage, we are seeing an increasing need to use machine learning models to process data where it is being collected. Novetta is pairing our advanced analytics, machine learning, and IoT capabilities with Enveil’s ZeroReveal™ technology through a strategic alliance. Using homomorphic encryption, Enveil’s technology allows sensitive information to be securely processed in unsecure environments, allowing data to be searched or analyzed while it is still encrypted. The data security startup’s core technology was developed…
Enveil and Novetta Partner to Deliver Secure Data Processing at the Tactical Edge
Strategic partnership and technology integration unlocks field-ready capabilities for mission-critical applications in the government market.
WASHINGTON, D.C., June 4, 2019 – Enveil, the pioneering data security company protecting Data in Use, and Novetta, a leader in advanced analytics technology, today announced a strategic partnership and technology integration to enable trusted compute at the tactical edge. The collaboration pairs Enveil’s ZeroReveal™ technology with Novetta’s tailored machine learning models to deliver unmatched capabilities for government customers whose mission requires operating on sensitive data in hostile or untrusted environments. Building on their unique national security heritage and mission experience, Novetta and Enveil are exceptionally positioned to deliver capabilities in support of the nation’s highest priority challenges. As a result…
Deep Learning for Language Identification on Audio Signals
Introduction Speech-to-text transcription is an important tool for mission analytics and many natural language processing tasks. In most cases, a language is manually preset before speech-to-text transcription can be applied. For scenarios where data is being aggregated in large quantities in various languages, automatic language detection capabilities would greatly streamline data processing. Novetta has developed a technique, Language Identification For Audio Spectrograms (LIFAS), that uses spectrograms of raw audio signals as input to a convolutional neural network (CNN) to be used for language identification. This approach requires minimal pre-processing, as raw audio signals are input into the neural network and…
