39 citations · 130 across the 45 of their papers we have counts for
49 papers
RAD: Rule-Augmented Relational Anomaly Detection
Noah Dahle, Anne Tumlin, Ngoc Tran +2
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattenin…
A Survey of Security Challenges and Solutions for Advanced Air Mobility and eVTOL Aircraft
Mahyar Ghazanfari, Iman Sharifi, Peng Wei +25
This survey reviews the existing and envisioned security vulnerabilities and defense mechanisms relevant to Advanced Air Mobility (AAM) systems, with a focus on electric vertical t…
A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and small Unmanned Aerial Systems (sUAS)
Iman Sharifi, Mahyar Ghazanfari, Abenezer Taye +25
The rapid growth of small Unmanned Aerial Systems (sUAS) for civil and commercial missions has intensified concerns about their resilience to cyber-security threats. Operating with…
Defining and Benchmarking a Data-Centric Design Space for Brain Graph Construction
Qinwen Ge, Roza G. Bayrak, Anwar Said +3
The construction of brain graphs from functional Magnetic Resonance Imaging (fMRI) data plays a crucial role in enabling graph machine learning for neuroimaging. However, current p…
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Ngoc N. Tran, Anwar Said, Waseem Abbas +2
While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when exposed to previously unseen variants…
Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning
Anwar Said, Yifan Wei, Obaid Ullah Ahmad +3
In this article, we utilize the concept of average controllability in graphs, along with a novel rank encoding method, to enhance the performance of Graph Neural Networks (GNNs) in…
Robust Anomaly Detection with Graph Neural Networks using Controllability
Yifan Wei, Anwar Said, Waseem Abbas +1
Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples…
An upper bound on generalized cospectral mates of oriented hraphs using skew-walk matrices
Muhammad Raza, Obaid Ullah Ahmed, Mudassir Shabbir +2
Let be an oriented graph with skew-adjacency matrix . Two oriented graphs and are said to share the same generalized skew spectrum if and have the s…
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir +2
This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducin…
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +8
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban…
PropEnc: A Property Encoder for Graph Neural Networks
Anwar Said, Waseem Abbas, Xenofon Koutsoukos
Graph machine learning, particularly using graph neural networks, heavily relies on node features. However, many real-world systems, such as social and biological networks, lack no…
Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory
Anwar Said, Obaid Ullah Ahmad, Waseem Abbas +2
Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how cer…
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir +2
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introdu…
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
Anwar Said, Mudassir Shabbir, Tyler Derr +2
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the…
A Survey of Graph Unlearning
Anwar Said, Ngoc N. Tran, Yuying Zhao +4
Graph unlearning emerges as a crucial advancement in the pursuit of responsible AI, providing the means to remove sensitive data traces from trained models, thereby upholding the \…
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
Abihith Kothapalli, Mudassir Shabbir, Xenofon Koutsoukos
A dominating set of a graph is a subset of vertices such that every vertex outside the dominating set is…
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
Anwar Said, Roza G. Bayrak, Tyler Derr +4
Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psych…
Sequential Graph Neural Networks for Source Code Vulnerability Identification
Ammar Ahmed, Anwar Said, Mudassir Shabbir +1
Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. Howev…
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
Ho Hin Lee, Quan Liu, Shunxing Bao +7
With the inspiration of vision transformers, the concept of depth-wise convolution revisits to provide a large Effective Receptive Field (ERF) using Large Kernel (LK) sizes for med…
Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids
Himanshu Neema, Harsh Vardhan, Carlos Barreto +1
Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a contin…
Open Set Recognition using Vision Transformer with an Additional Detection Head
Feiyang Cai, Zhenkai Zhang, Jie Liu +1
Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the train…
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning
Dimitrios Boursinos, Xenofon Koutsoukos
Deep neural networks are frequently used by autonomous systems for their ability to learn complex, non-linear data patterns and make accurate predictions in dynamic environments. H…
Improving Prediction Confidence in Learning-Enabled Autonomous Systems
Dimitrios Boursinos, Xenofon Koutsoukos
Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop bet…
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning
Dimitrios Boursinos, Xenofon Koutsoukos
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can…