Activities and Campus Visit (Participants and Mentors)

Group Meeting and Project Presentation

Student Publications

  • Jacob Morosco and Yuchou Chang, "Visual Inference Using Homology of Human and Machine Vision Systems," the 10th Annual Conference on Advances in Cognitive Systems (ACS), Arlington VA, November 19-22, 2022.
  • Chetan Kumar, James Patrick Donohue, Rohan Gonjari, Neela Rahimi, John McLinden, Yalda Shahriari, and Ming Shao, "Adversary on Multimodal BCI-based Classification," the 11th International IEEE EMBS Conference on Neural Engineering, Baltimore, MD, April 25-27, 2023.
  • Priscila Silva, Mariana Hermosillo Hidalgo, Igor Linkov and Lance Fiondella, "Predictive Resilience Modeling," in Proceedings of the Resilience Week, September 26-29, 2022.
  • James Lu, Todd Morehouse, Jiawei Yuan, and Ruolin Zhou. "Machine-Learning PUF-based Detection of RF Anomalies in a Cluttered RF Environment." In 2021 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1-7. IEEE, 2021.
  • Galvan, Julio, Ashok Raja, Yanyan Li, and Jiawei Yuan. "Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach." In MILCOM 2021-2021 IEEE Military Communications Conference (MILCOM), pp. 589-594. IEEE.
  • Marc Tunnell, Huijin Chung, and Yuchou Chang, “A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals.” Accepted by IEEE International Conference on Robotics and Automation 2022 (ICRA).

     

    Projects Information of Program 2021

    Student Mentor Project Title Project Description
    Andrew Anctil Dr. Ming Shao Enabling Automatic Continual Learning The project “Enabling Automatic Continual Learning” proposes an interesting framework to enable the evolution of machine learning models in a robust and sustainable manner. This project first explores state of the art continual and lifelong learning algorithms. A novel model with the ability to automatically choose the appropriate continual learning methods is then developed to maintain the learning accuracy in the long run.
    Leeban Ali, and Devin M Foxhoven Dr. Gokhan Kul

    Software Performance Estimation through Continuous Code Monitoring

    		 The project “Software Performance Estimation through Continuous Code Monitoring” explores effective ways of monitoring code and its performance changes, a key step to secure the reliability of software. This project develops a code monitoring tool that plugs into the Continuous Integration process. It estimates the performance impact of incoming code changes by evaluating programmer habits, programming language structures used and operations to be performed.
    Morgan Britt-Webb Dr. Ming Shao

    Adversarial Learning on Multimodal Brain Signal Recognition.

    		 The project “Adversarial Learning on Multimodal Brain Signal Recognition” explores the how adversarial machine learning affects multimodal brain signal processing. This has been a concern in cybersecurity where sensitive clinical data may be contaminated. This project investigates electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) brain signal imaging techniques and machine learning algorithms on the recognition of patients with Amyotrophic Lateral Sclerosis (ALS). Adversarial attack algorithms are applied to EEG, fNIRS and their fusion to motivate novel defense mechanisms and improve the robustness of brain signal machine learning models.
    Huijin Chung Dr. Yuchou Chang EEG-Based Emotion Recognition using Transfer Learning and Brain-Visual Representations The project “Software Performance Estimation through Continuous Code Monitoring” explores effective ways of monitoring code and its performance changes, a key step to secure the reliability of software. This project develops a code monitoring tool that plugs into the Continuous Integration process. It estimates the performance impact of incoming code changes by evaluating programmer habits, programming language structures used and operations to be performed.
    Julio Galvan Dr. Jiawei Yuan

    UAV Anomaly Detection using Deep Learning Approach

    		 The project “UAV Anomaly Detection using Deep Learning Approach” investigates how to leverage IMU sensor data and a deep learning approach to detect the abnormal status of UAVs. By developing a new convolutional neural network, we demonstrate the effectiveness of our approach with simulation results. The is of great real-world and research value as it provides a self-collected sensor data-driven UAV anomaly detection dataset as well as an effective deep learning model.

    Mariana Hermosillo Hidalgo

    		 Dr. Lance Fiondella Resilience Modeling: A Quantitative Approach The project “Resilience Modeling: A Quantitative Approach” explores different metrics to quantify the resilience of systems over time. In particular, this project develops quantitative resilience models to characterize performance degradation and recovery. The models enable prediction and the calculation of resilience metrics from the literature.
    Jin Feng Lin Dr. Ruolin Zhou Optimization of CNN model The project “Optimization of CNN model” explores lightweight CNN models developed on FPGA. Compared to the CPU and GPUs, Field-Programmable Gate Array (FPGA) delivers superior performance in deep learning applications where low latency is critical. The project focus on developing a lightweight FPGA-based CNN architecture for RF signal detection and classification.
    James Lu Dr. Ruolin Zhou and Dr. Jiawei Yuan Machine-Learning PUF-based Detection of RF Anomalies in a Cluttered RF Environment The project “Machine-Learning PUF-based Detection of RF Anomalies in a Cluttered RF Environment” explores deep learning in detection of malicious devices. It is expanding the robustness of SDR using CNN to detect potentially malicious devices sending signals to transmitters. This concept is then applied to airplane ADS-B radar signals to reinforce their security.

    Marc Tunnell

    		 Dr. Yuchou Chang A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals The project “A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals” proposes a novel deep infrastructure to improve EEG-BCI classification accuracy. This study proposes the use of Thomson Multitaper PSD estimation in the EEG-BCI classification pipeline as well as a novel architecture based on regularized separable convolutions, designed to efficiently make use of the PSD extracted features. Further, the project evaluates the efficacy of interspersed Gaussian noise as a data augmentation technique and investigates abnormalities in the convergence of widely used optimizers as applied to 'difficult' data.

    Projects Information of Program 2022

    Jacob Morosco,
    Savina Verdugo

    		 Dr. Yuchou Chang

    An Inference Study Using the Microsoft COCO Dataset for Semantic AI

    		 The objective of the project is to explore the inferences that can be drawn from large-scale visual recognition datasets, e.g., the MS COCO dataset. To achieve this, a visual graph of the inferences will be created using an Inference Engine written in Java. By running the dataset through the engine, it will identify the concepts in the dataset that are interrelated and uncover "inferences" from the dataset. The engine will generate a Knowledge Graph, which can be visualized using GraphViz tools, facilitating a comprehensive understanding of the visual concepts and deep analysis of new inference information.

    Ashlee Shuemaker,
    Brendan Thibault,
    Chumba Kiplagat

    		 Dr. Lance Fiondella Agent-based Modeling and Simulation of Nonviolent Action There is an increase in the use of misinformation and disinformation, particularly through social media, which causes internal strife on various issues. The project employed an agent-based model to identify relationships between social media blackouts, and the rate of information dissemination within these conditions. In addition to providing valuable insight into information dissemination, the agent model also proves useful in identifying protest participation, whether that be violent or nonviolent, and identifying how manipulating parameters of the simulation manifest themselves as trends within the protest participation.

    Grace Elena Stewart

    		Dr. Gokhan Kul VulBERT: Backdoor Vulnerability Detection by Feeding Hard-coded Credentials to Bidirectional Encoder Representations from Transformers The project aims to explore and address the fundamental issues in security that hard-coded credentials can be used as backdoors into a software developer's code, allowing hackers and other attackers to gain unapproved administrative access or access to restricted data. This research project investigates the feasibility of applying the state-of-the-art transformer-based language model BERT to detect hard-coded credentials in software.

    Alexandre Broggi

    		 Dr. Gokhan Kul Applying Open Set Algorithms to Machine Learning-based Network Intrusion Detection The project aims to detect novel attacks, a critical challenge for current Network Intrusion Detection Systems (NIDS). To that end, three open-set recognition techniques in machine learning have been explored in an attempt to identify related classes with valuable uncertainty measurements. The project has investigated open-set uncertainty with images that the model was not trained for as a feasibility study. The methodology was then extended CIC-IDS2017, a NIDS dataset for further demonstration.

    James Patrick Donohue

    		 Dr. Ming Shao Adversarial Attacks on Multimodal Graph Neural Networks The project was the first attempt to apply adversarial attack to brain signals models constructed through graph neural network (GNN). Graph perturbations, namely node injection and edge manipulation are observed in wholesale to determine their effectiveness on the following modalities: electroencephalography (EEG), event-related potential (ERP), and functional near-infrared spectroscopy (fNIRS), brain signal imaging techniques used on patients with Amyotrophic Lateral Sclerosis (ALS). Adversarial attack algorithms are applied to EEG, ERPs, and fNIRS to motivate novel defense mechanisms and improve the robustness of brain signal machine learning models.

    Kevin Chen

    		 Dr. Jiawei Yuan Privacy-Preserving Machine Learning as A Service Security and privacy have been a major concern in AI, and machine learning in particular recently. The project applied recent advancements in cryptography, such as fully homomorphic encryption to enable user privacy for machine learning in the cloud. Multiple security models and techniques have been considered. The performance and security of such models are also compared.

    Theresa Ng

    		 Dr. Ruolin Zhou Mimicking GPS Signals GPS signals are weak broadcasted signals and have become affected by in-band interference. GPS spoofers can generate a GPS signal to mislead a GPS receiver through software-defined radio (SDR). The project explores ways of mimicking GPS signals, a fundamental security issue in signal processing. In particular, the project investigates two SDRs, namely, ADALM-PLUTO and USRP X-300. These two radios will transmit a GPS signal to spoof a smartphone. The receiver antenna calculated a GPS location based on the GPS signal transmitted by the phone. The phone was only able to calculate a location when the USRP X-300 transmitted the signal.