no code implementations • 12 Apr 2024 • Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure
Data augmentation is an important facilitator of deep learning applications in the time series domain.
no code implementations • 13 Mar 2024 • Subash Neupane, Shaswata Mitra, Sudip Mittal, Noorbakhsh Amiri Golilarz, Shahram Rahimi, Amin Amirlatifi
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses.
no code implementations • 27 Feb 2024 • Majid Memari, Khaled R. Ahmed, Shahram Rahimi, Noorbakhsh Amiri Golilarz
This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images.
no code implementations • 20 Feb 2024 • Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda, Noorbakhsh Amiri Golilarz, Sudip Mittal, Amin Amirlatifi, Shahram Rahimi
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way.
no code implementations • 18 Jan 2024 • Jesse Ables, Nathaniel Childers, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale
The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
no code implementations • 18 Jan 2024 • Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Sudip Mittal, Aritran Piplai, Manas Gaur, Shahram Rahimi
In this work, we present LOCALINTEL, a novel automated knowledge contextualization system that, upon prompting, retrieves threat reports from the global threat repositories and uses its local knowledge database to contextualize them for a specific organization.
no code implementations • 15 Jan 2024 • Logan Cummins, Alex Sommers, Somayeh Bakhtiari Ramezani, Sudip Mittal, Joseph Jabour, Maria Seale, Shahram Rahimi
This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
no code implementations • 12 Oct 2023 • Subash Neupane, Shaswata Mitra, Ivan A. Fernandez, Swayamjit Saha, Sudip Mittal, Jingdao Chen, Nisha Pillai, Shahram Rahimi
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security.
no code implementations • 22 Jun 2023 • Subash Neupane, Ivan A. Fernandez, Sudip Mittal, Shahram Rahimi
To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense.
no code implementations • 30 Mar 2023 • Jesse Ables, Thomas Kirby, Sudip Mittal, Ioana Banicescu, Shahram Rahimi, William Anderson, Maria Seale
Lastly, we analyze the statistical and visual explanations generated by our architecture, and we give a strategy that users could use to help navigate the set of explanations.
no code implementations • 1 Feb 2023 • Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal, Milan Parmar, Shahram Rahimi
Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs.
no code implementations • 27 Nov 2022 • Kourosh T. Baghaei, Amirreza Payandeh, Pooya Fayyazsanavi, Shahram Rahimi, Zhiqian Chen, Somayeh Bakhtiari Ramezani
Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades.
no code implementations • 25 Oct 2022 • Wilson Patterson, Ivan Fernandez, Subash Neupane, Milan Parmar, Sudip Mittal, Shahram Rahimi
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier.
no code implementations • 14 Sep 2022 • Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal, Shahram Rahimi
A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs).
no code implementations • 16 Aug 2022 • William Anderson, Kaneesha Moore, Jesse Ables, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale
The Human Immune System (HIS) works to protect a body from infection, illness, and disease.
no code implementations • 15 Jul 2022 • Jesse Ables, Thomas Kirby, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale
We leverage SOM's explainability to create both global and local explanations.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
no code implementations • 13 Jul 2022 • Subash Neupane, Jesse Ables, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
Explainable Artificial Intelligence (XAI) Intrusion Detection