no code implementations • 15 Feb 2024 • David Noever, Forrest McKee
This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems.
no code implementations • 1 Feb 2024 • Grant Rosario, David Noever
The growing volume of digital images necessitates advanced systems for efficient categorization and retrieval, presenting a significant challenge in database management and information retrieval.
no code implementations • 29 Jan 2024 • Forrest McKee, David Noever
A notable attack limitation stems from its dependency on the background (hidden) layer in grayscale as a rough match to the transparent foreground image that the human eye perceives.
no code implementations • 18 Dec 2023 • Grant Rosario, David Noever
With the growing capabilities of modern object detection networks and datasets to train them, it has gotten more straightforward and, importantly, less laborious to get up and running with a model that is quite adept at detecting any number of various objects.
no code implementations • 17 Dec 2023 • David Noever, Matt Ciolino
GPT-3, even without any fine-tuning or exam preparation, managed to achieve a passing score (over 70% correct) on 39% of the professional certifications.
no code implementations • 23 Nov 2023 • Forrest McKee, David Noever
In this study, we investigate the emerging threat of inaudible acoustic attacks targeting digital voice assistants, a critical concern given their projected prevalence to exceed the global population by 2024.
no code implementations • 18 Nov 2023 • David Noever, Samantha Elizabeth Miller Noever
This study explores the capabilities of multimodal large language models (LLMs) in handling challenging multistep tasks that integrate language and vision, focusing on model steerability, composability, and the application of long-term memory and context understanding.
no code implementations • 20 Aug 2023 • David Noever
In this study, we evaluated the capability of Large Language Models (LLMs), particularly OpenAI's GPT-4, in detecting software vulnerabilities, comparing their performance against traditional static code analyzers like Snyk and Fortify.
no code implementations • 17 Aug 2023 • David Noever, Samantha Elizabeth Miller Noever
Addressing the gap in understanding visual comprehension in Large Language Models (LLMs), we designed a challenge-response study, subjecting Google Bard and GPT-Vision to 64 visual tasks, spanning categories like "Visual Situational Reasoning" and "Next Scene Prediction."
no code implementations • 7 Aug 2023 • David Noever, Sam Hyams
The research explores the steerability of Large Language Models (LLMs), particularly OpenAI's ChatGPT iterations.
no code implementations • 23 Jul 2023 • Forrest McKee, David Noever
The paper applies reinforcement learning to novel Internet of Thing configurations.
no code implementations • 7 May 2023 • David Noever, Matt Ciolino
It compares the performance of two AI models, GPT-3 and Turbo-GPT3. 5, on a benchmark dataset of 1149 professional certifications, emphasizing vocational readiness rather than academic performance.
no code implementations • 25 Apr 2023 • Forrest McKee, David Noever
This study investigates a primary inaudible attack vector on Amazon Alexa voice services using near ultrasound trojans and focuses on characterizing the attack surface and examining the practical implications of issuing inaudible voice commands.
no code implementations • 1 Feb 2023 • Grant Rosario, David Noever
Chatbots have long been capable of answering basic questions and even responding to obscure prompts, but recently their improvements have been far more significant.
no code implementations • 31 Jan 2023 • David Noever, Forrest McKee
Large language models (LLM) such as OpenAI's ChatGPT and GPT-3 offer unique testbeds for exploring the translation challenges of turning literacy into numeracy.
no code implementations • 10 Jan 2023 • Forrest McKee, David Noever
The paper illustrates ten diverse tasks that a conversational agent or large language model might answer appropriately to the effects of command-line attacker.
no code implementations • 5 Jan 2023 • David Noever, Kevin Williams
The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology, including email, internet browsing, robotics, and malicious software.
no code implementations • 1 Jan 2023 • David Noever, Forrest McKee
The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions.
no code implementations • 18 Dec 2022 • Forrest McKee, David Noever
Question-and-answer formats provide a novel experimental platform for investigating cybersecurity questions.
no code implementations • 9 Dec 2022 • David Noever, Matt Ciolino
This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation.
no code implementations • 1 Dec 2022 • Matthew Ciolino, Grant Rosario, David Noever
We show that soft labels can be used to train a model that is almost as accurate as a model trained on the original data.
no code implementations • 23 Sep 2022 • Grant Rosario, David Noever, Matt Ciolino
The challenge of labeling large example datasets for computer vision continues to limit the availability and scope of image repositories.
1 code implementation • 26 Jul 2022 • David Noever, Samuel Hyams
Physics-based simulations typically operate with a combination of complex differentiable equations and many scientific and geometric inputs.
no code implementations • 18 Jul 2022 • Samantha E. Miller Noever, David Noever
Like previous work with chess and Go, these language models offer a novel way to generate plausible game archives, particularly for comparing opening moves across a larger sample than humanly possible to explore.
no code implementations • 28 Feb 2022 • Matthew Ciolino, Dominick Hambrick, David Noever
The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning.
no code implementations • 20 Oct 2021 • Josh Kalin, David Noever, Matthew Ciolino
Machine learning and software development share processes and methodologies for reliably delivering products to customers.
no code implementations • 14 Oct 2021 • Erik Larsen, David Noever, Korey MacVittie
A survey of machine learning techniques trained to detect ransomware is presented.
no code implementations • 24 Sep 2021 • David Noever, Samantha Miller Noever
This research recasts ransomware detection using performance monitoring and statistical machine learning.
no code implementations • 7 Sep 2021 • David Noever, Ryerson Burdick
The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay.
no code implementations • 1 Jul 2021 • Erik Larsen, David Noever, Korey MacVittie, John Lilly
Twenty-three machine learning algorithms were trained then scored to establish baseline comparison metrics and to select an image classification algorithm worthy of embedding into mission-critical satellite imaging systems.
no code implementations • 9 Apr 2021 • David Noever, Samantha E. Miller Noever
The Mars Perseverance rover applies computer vision for navigation and hazard avoidance.
no code implementations • 29 Mar 2021 • Josh Kalin, David Noever, Matthew Ciolino, Dominick Hambrick, Gerry Dozier
Image classification is a common step in image recognition for machine learning in overhead applications.
no code implementations • 3 Mar 2021 • Josh Kalin, David Noever, Matthew Ciolino
Machine learning models present a risk of adversarial attack when deployed in production.
no code implementations • 28 Feb 2021 • David Noever, Samantha E. Miller Noever
As a benchmark using deep learning methods (MobileNetV2), we find an overall 80% accuracy for virus identification by families when beneware is included.
no code implementations • 19 Feb 2021 • Matthew Ciolino, David Noever, Josh Kalin
Natural Language Processing (NLP) relies heavily on training data.
no code implementations • 19 Feb 2021 • Matthew Ciolino, Josh Kalin, David Noever
Production machine learning systems are consistently under attack by adversarial actors.
no code implementations • 8 Feb 2021 • David Noever, Samantha E. Miller Noever
The research presents an overhead view of 10 important objects and follows the general formatting requirements of the most popular machine learning task: digit recognition with MNIST.
no code implementations • 5 Jan 2021 • David Noever, Josh Kalin, Matt Ciolino, Dom Hambrick, Gerry Dozier
Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages.
no code implementations • 7 Sep 2020 • Josh Kalin, Matthew Ciolino, David Noever, Gerry Dozier
With the process in this paper, a structured set of input probes and the output of the model become the training data for a deep classifier.
2 code implementations • 2 Aug 2020 • David Noever, Matt Ciolino, Josh Kalin
This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games.
no code implementations • 7 Jul 2020 • Matthew Ciolino, David Noever, Josh Kalin
This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go.
no code implementations • 19 Jun 2020 • Josh Kalin, David Noever, Gerry Dozier
This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment.
no code implementations • 24 Jan 2020 • David Noever
To probe the largest public-domain email database for indicators of fraud, we apply machine learning and accomplish four investigative tasks.
no code implementations • 3 Jan 2020 • David Noever, Wes Regian, Matt Ciolino, Josh Kalin, Dom Hambrick, Kaye Blankenship
Small satellite constellations provide daily global coverage of the earth's landmass, but image enrichment relies on automating key tasks like change detection or feature searches.
no code implementations • 29 Oct 2019 • Matthew Ciolino, David Noever, Josh Kalin
This suggests a possible improvement to automated target recognition in image classification and object detection.
no code implementations • 30 Jan 2019 • David Noever
We address the relative rarity of threats as a case of low signal-to-noise (< 0. 02% malicious to benign activities), and then train on both under-sampled and over-sampled data which is statistically balanced to identify nefarious actors.
no code implementations • 11 Dec 2018 • David Noever
For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements, and sensor inputs.
no code implementations • 3 Oct 2018 • David Noever
To identify and classify toxic online commentary, the modern tools of data science transform raw text into key features from which either thresholding or learning algorithms can make predictions for monitoring offensive conversations.