arXiv subjects

Andrew Morris

Publications and source records attributed to Andrew Morris.

11 recordsLinked to original sources

arXiv2026

Beyond Technological Solutionism: Rethinking XR in Healthcare

The healthcare industry's enthusiastic adoption of Extended Reality (XR) technologies obscures a concerning reality: we were building increasingly sophisticated ways to perpetuate fundamentally broken healthcare systems. Through three deeply personal narratives - a rural patient cut off from care infrastructure, an urban professional navigating fragmented services, and a first-generation immigrant confronting cultural barriers - this provocation paper exposes how our obsession with technological innovation often worsens rather than resolves healthcare disparities. By applying the SEIPS 3.0 model to examine diabetes-CVD care coordination, we identify an "innovation paradox" where advanced technology creates new barriers to effective care. Our care interdependencies framework reveals that healthcare outcomes are shaped primarily by human relationships (50-60%), organizational coordination (25-30%), and sociocultural factors (15-20%), not technological sophistication. This research challenges the HCI community to confront its role in perpetuating healthcare inequities, demands a fundamental rethinking and proposes a new framework for healthcare innovation that prioritizes human relationships over technical capability, systemic change over feature sets, and actual care delivery over technological ambition. For healthcare providers, technology developers, and policymakers, our findings suggest that effective care coordination requires us to step back from our techno-solutionist mindset and engage

cs.HC↗

arXiv2026

Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling

Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable strong zero-shot image classification, but existing VLM-based distracted driver detectors often underperform in real-world conditions. We identify subject-specific appearance variations (e.g., clothing, age, and gender) as a key bottleneck: VLMs entangle these factors with behavior cues, leading to decisions driven by who the driver is rather than what the driver is doing. To address this, we propose a subject decoupling framework that extracts a driver appearance embedding and removes its influence from the image embedding prior to zero-shot classification, thereby emphasizing distraction-relevant evidence. We further orthogonalize text embeddings via metric projection onto Stiefel manifold to improve separability while staying close to the original semantics. Experiments demonstrate consistent gains over prior baselines, indicating the promise of our approach for practical road-safety applications. Code is available at https://github.com/mtakamichi/ZVL-DDD

cs.CV↗

arXiv2024

Anonymized Network Sensing Graph Challenge

The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations.

cs.NI↗

arXiv2024

Teaching Network Traffic Matrices in an Interactive Game Environment

The Internet has become a critical domain for modern society that requires ongoing efforts for its improvement and protection. Network traffic matrices are a powerful tool for understanding and analyzing networks and are broadly taught in online graph theory educational resources. Network traffic matrix concepts are rarely available in online computer network and cybersecurity educational resources. To fill this gap, an interactive game environment has been developed to teach the foundations of traffic matrices to the computer networking community. The game environment provides a convenient, broadly accessible, delivery mechanism that enables making material available rapidly to a wide audience. The core architecture of the game is a facility to add new network traffic matrix training modules via an easily editable JSON file. Using this facility an initial set of modules were rapidly created covering: basic traffic matrices, traffic patterns, security/defense/deterrence, a notional cyber attack, a distributed denial-of-service (DDoS) attack, and a variety of graph theory concepts. The game environment enables delivery in a wide range of contexts to enable rapid feedback and improvement. The game can be used as a core unit as part of a formal course or as a simple interactive introduction in a presentation.

cs.CR↗

arXiv2023

Mapping of Internet "Coastlines" via Large Scale Anonymized Network Source Correlations

Expanding the scientific tools available to protect computer networks can be aided by a deeper understanding of the underlying statistical distributions of network traffic and their potential geometric interpretations. Analyses of large scale network observations provide a unique window into studying those underlying statistics. Newly developed GraphBLAS hypersparse matrices and D4M associative array technologies enable the efficient anonymized analysis of network traffic on the scale of trillions of events. This work analyzes over 100,000,000,000 anonymized packets from the largest Internet telescope (CAIDA) and over 10,000,000 anonymized sources from the largest commercial honeyfarm (GreyNoise). Neither CAIDA nor GreyNoise actively emit Internet traffic and provide distinct observations of unsolicited Internet traffic (primarily botnets and scanners). Analysis of these observations confirms the previously observed Cauchy-like distributions describing temporal correlations between Internet sources. The Gull lighthouse problem is a well-known geometric characterization of the standard Cauchy distribution and motivates a potential geometric interpretation for Internet observations. This work generalizes the Gull lighthouse problem to accommodate larger classes of coastlines, deriving a closed-form solution for the resulting probability distributions, stating and examining the inverse problem of identifying an appropriate coastline given a continuous probability distribution, identifying a geometric heuristic for solving this problem computationally, and applying that heuristic to examine the temporal geometry of different subsets of network observations. Application of this method to the CAIDA and GreyNoise data reveals a several orders of magnitude difference between known benign and other traffic which can lead to potentially novel ways to protect networks.

cs.SI↗

arXiv2022

Large Scale Enrichment and Statistical Cyber Characterization of Network Traffic (Enriquecimiento a gran escala y caracterizaci\'on cibern\'etica estad\'istica del tr\'afico de red)

Modern network sensors continuously produce enormous quantities of raw data that are beyond the capacity of human analysts. Cross-correlation of network sensors increases this challenge by enriching every network event with additional metadata. These large volumes of enriched network data present opportunities to statistically characterize network traffic and quickly answer a key question: "What are the primary cyber characteristics of my network data?" The Python GraphBLAS and PyD4M analysis frameworks enable anonymized statistical analysis to be performed quickly and efficiently on very large network data sets. This approach is tested using billions of anonymized network data samples from the largest Internet observatory (CAIDA Telescope) and tens of millions of anonymized records from the largest commercially available background enrichment capability (GreyNoise). The analysis confirms that most of the enriched variables follow expected heavy-tail distributions and that a large fraction of the network traffic is due to a small number of cyber activities. This information can simplify the cyber analysts' task by enabling prioritization of cyber activities based on statistical prevalence. -- Los sensores de red modernos producen enormes cantidades de datos sin procesar que est\'an m\'as all\'a de la capacidad del an\'alisis humano. Una correlaci\'on cruzada de sensores de red se convierte en un desaf\'io al enriquecer cada evento de red con metadatos adicionales. Estos grandes vol\'umenes de datos de red enriquecidos presentan una oportunidad para caracterizar estad\'isticamente el tr\'afico de red y responder a la pregunta: "?Cu\'ales son las principales caracter\'isticas cibern\'eticas de mis datos de red?" Los esquemas de an\'alisis de Python GraphBLAS y D4M permiten realizar an\'alisis estad\'isticos an\'onimos, r\'apidos y eficientes en conjuntos grandes de datos de red. Este enfoque se prueba utilizando miles de millones de muestras de datos de red an\'onimos del observatorio de Internet m\'as grande (Telescopio CAIDA) y decenas de millones de registros an\'onimos del fondo comercial con la mayor capacidad de enriquecimiento (GreyNoise). El an\'alisis confirma que la mayor\'ia de las variables enriquecidas siguen las distribuciones de cola pesada y que una gran fracci\'on del tr\'afico de red se debe a una peque\`na cantidad de actividades cibern\'eticas. Esta informaci\'on puede simplificar la tarea de los analistas cibern\'eticos al permitir la priorizaci\'on de las actividades cibern\'eticas en funci\'on de la prevalencia estad\'istica.

cs.NI↗

arXiv2022

Temporal Correlation of Internet Observatories and Outposts

The Internet has become a critical component of modern civilization requiring scientific exploration akin to endeavors to understand the land, sea, air, and space environments. Understanding the baseline statistical distributions of traffic are essential to the scientific understanding of the Internet. Correlating data from different Internet observatories and outposts can be a useful tool for gaining insights into these distributions. This work compares observed sources from the largest Internet telescope (the CAIDA darknet telescope) with those from a commercial outpost (the GreyNoise honeyfarm). Neither of these locations actively emit Internet traffic and provide distinct observations of unsolicited Internet traffic (primarily botnets and scanners). Newly developed GraphBLAS hyperspace matrices and D4M associative array technologies enable the efficient analysis of these data on significant scales. The CAIDA sources are well approximated by a Zipf-Mandelbrot distribution. Over a 6-month period 70\% of the brightest (highest frequency) sources in the CAIDA telescope are consistently detected by coeval observations in the GreyNoise honeyfarm. This overlap drops as the sources dim (reduce frequency) and as the time difference between the observations grows. The probability of seeing a CAIDA source is proportional to the logarithm of the brightness. The temporal correlations are well described by a modified Cauchy distribution. These observations are consistent with a correlated high frequency beam of sources that drifts on a time scale of a month.

cs.NI↗

arXiv2022

Zero Botnets: An Observe-Pursue-Counter Approach

Adversarial Internet robots (botnets) represent a growing threat to the safe use and stability of the Internet. Botnets can play a role in launching adversary reconnaissance (scanning and phishing), influence operations (upvoting), and financing operations (ransomware, market manipulation, denial of service, spamming, and ad click fraud) while obfuscating tailored tactical operations. Reducing the presence of botnets on the Internet, with the aspirational target of zero, is a powerful vision for galvanizing policy action. Setting a global goal, encouraging international cooperation, creating incentives for improving networks, and supporting entities for botnet takedowns are among several policies that could advance this goal. These policies raise significant questions regarding proper authorities/access that cannot be answered in the abstract. Systems analysis has been widely used in other domains to achieve sufficient detail to enable these questions to be dealt with in concrete terms. Defeating botnets using an observe-pursue-counter architecture is analyzed, the technical feasibility is affirmed, and the authorities/access questions are significantly narrowed. Recommended next steps include: supporting the international botnet takedown community, expanding network observatories, enhancing the underlying network science at scale, conducting detailed systems analysis, and developing appropriate policy frameworks.

cs.CR↗

arXiv2020

FireBERT: Hardening BERT-based classifiers against adversarial attack

We present FireBERT, a set of three proof-of-concept NLP classifiers hardened against TextFooler-style word-perturbation by producing diverse alternatives to original samples. In one approach, we co-tune BERT against the training data and synthetic adversarial samples. In a second approach, we generate the synthetic samples at evaluation time through substitution of words and perturbation of embedding vectors. The diversified evaluation results are then combined by voting. A third approach replaces evaluation-time word substitution with perturbation of embedding vectors. We evaluate FireBERT for MNLI and IMDB Movie Review datasets, in the original and on adversarial examples generated by TextFooler. We also test whether TextFooler is less successful in creating new adversarial samples when manipulating FireBERT, compared to working on unhardened classifiers. We show that it is possible to improve the accuracy of BERT-based models in the face of adversarial attacks without significantly reducing the accuracy for regular benchmark samples. We present co-tuning with a synthetic data generator as a highly effective method to protect against 95% of pre-manufactured adversarial samples while maintaining 98% of original benchmark performance. We also demonstrate evaluation-time perturbation as a promising direction for further research, restoring accuracy up to 75% of benchmark performance for pre-made adversarials, and up to 65% (from a baseline of 75% orig. / 12% attack) under active attack by TextFooler.

cs.CL↗

arXiv2019

Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in retinal research. OCTA potential in the assessment of pathological conditions and the reproducibility of studies relies on the quality of the image analysis. However, automated segmentation of parafoveal OCTA images is still an open problem. In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Furthermore, we establish a standard for OCTA image segmentation by surveying a broad range of state-of-the-art vessel enhancement and binarisation procedures. We provide the most comprehensive comparison of these methods under a unified framework to date. Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance. For applications where manually segmented data is not available to retrain these approaches, our findings suggest that optimal oriented flux is the best handcrafted filter from those considered. Furthermore, we report on the importance of preserving network structure in the segmentation to enable deep vascular phenotyping. We introduce new metrics for network structure evaluation in segmented angiograms. Our results demonstrate that segmentation methods with equal Dice score perform very differently in terms of network structure preservation. Moreover, we compare the error in the computation of clinically relevant vascular network metrics (e.g. foveal avascular zone area and vessel density) across segmentation methods. Our results show up to 25% differences in vessel density accuracy depending on the segmentation method employed. These findings should be taken into account when comparing the results of clinical studies and performing meta-analyses.

eess.IV↗

arXiv2013

Calderon Reproducing Formulas and Applications to Hardy Spaces

We establish new Calderón reproducing formulas for self-adjoint operators $D$ that generate strongly continuous groups with finite propagation speed. These formulas allow the analysing function to interact with $D$ through holomorphic functional calculus whilst the synthesising function interacts with $D$ through functional calculus based on the Fourier transform. We apply these to prove the embedding $H^p_D(\wedge T^*M) \subseteq L^p(\wedge T^*M)$, $1\leq p\leq 2$, for the Hardy spaces of differential forms introduced by Auscher, McIntosh and Russ, where $D=d+d^*$ is the Hodge--Dirac operator on a complete Riemannian manifold $M$ that has polynomial volume growth. This fills a gap in that work. The new reproducing formulas also allow us to obtain an atomic characterisation of $H^1_D(\wedge T^*M)$. The embedding $H^p_L \subseteq L^p$, $1\leq p\leq 2$, where $L$ is either a divergence form elliptic operator on $\R^n$, or a nonnegative self-adjoint operator that satisfies Davies--Gaffney estimates on a doubling metric measure space, is also established in the case when the semigroup generated by the adjoint $-L^*$ is ultracontractive.

math.CA↗