<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dcterms="http://purl.org/dc/terms/">
<rdf:Description rdf:about="https://repository.nyitlibrary.org/items/show/4305">
    <dcterms:title><![CDATA[Interaction-anchored behavioral biometrics for continuous authentication on smartphones]]></dcterms:title>
    <dcterms:subject><![CDATA[Biometric identification<br />
Authentication<br />
Smartphones<br />
Human-computer interaction]]></dcterms:subject>
    <dcterms:abstract><![CDATA[Smartphones have become central to personal and professional life, yet their security remains highly dependent on single-point verification mechanisms that cannot guarantee protection once a device is unlocked. Continuous authentication offers persistent verification, but current systems rely on long decision windows or fixed sampling that introduce unacceptable latency, creating vulnerable windows for compromise. These limitations represent a critical gap between the speed at which an adversary can take control of a device and the rate at which the system can detect misuse. This dissertation proposes an interaction-anchored framework for continuous authentication that re-centers the problem around natural user events such as keystrokes, swipes, and possession changes. By extracting and aggregating features within short event windows, it is possible to reliably authenticate a user in 1-2 seconds, sharply reducing vulnerability without sacrificing accuracy. Across five empirical studies, this work demonstrates that smartphone dynamics (swiping, typing, and motion features), including when combined with body motion features, yield competitive performance at sub-five-second windows, including the first evidence that keystroke-based continuous authentication can achieve error rates below two percent at one-second intervals. Further analysis within these studies establishes the value of event alignment, multimodal fusion, and carefully selected feature families. Finally, to assess the robustness of these features and methods, statistical forgery attacks under zero-shot, few-shot, and whitebox threat models are systematically evaluated across keystroke and swiping modalities; generative adversarial networks (GANs) are additionally explored to synthesize behavioral traces and probe cross-modal transfer. Together, these analyses provide one of the first systematic assessments of generative threats to short-window authentication and establish empirical bounds on robustness, highlighting avenues for defensive strategies such as feature hardening and multimodal liveness checks. Overall, this work reframes continuous authentication as a problem of event-driven, low-latency verification. The findings show that accurate and efficient authentication can be achieved with lightweight models suitable for on-device inference, while also charting a path toward systems resilient to emerging adversarial threats. This positions interaction-anchored biometrics as both a practical and forward-looking foundation for smartphone security.]]></dcterms:abstract>
    <dcterms:creator><![CDATA[Cariello, Nicholas]]></dcterms:creator>
    <dcterms:publisher><![CDATA[Old Westbury, N.Y. : New York Institute of Technology, [2026]]]></dcterms:publisher>
    <dcterms:date><![CDATA[2026]]></dcterms:date>
    <dcterms:relation><![CDATA[<a href="https://scholar.google.com/citations?user=K1oSGxUAAAAJ&amp;hl=en">https://scholar.google.com/citations?user=K1oSGxUAAAAJ&amp;hl=en</a>]]></dcterms:relation>
    <dcterms:format><![CDATA[PDF]]></dcterms:format>
    <dcterms:language><![CDATA[English]]></dcterms:language>
    <dcterms:type><![CDATA[Thesis]]></dcterms:type>
    <dcterms:identifier><![CDATA[<span class="linkify-target internal-link">https://repository.nyitlibrary.org/files/original/403d66cc64be3f038eb8df9bd95b208a.pdf</span>]]></dcterms:identifier>
</rdf:Description><rdf:Description rdf:about="https://repository.nyitlibrary.org/items/show/4327">
    <dcterms:title><![CDATA[Adaptive Multimodal Fusion for Histopathological Tumor Analysis : A Hybrid Classical–Quantum Machine Learning Framework]]></dcterms:title>
    <dcterms:subject><![CDATA[Multimodal user interfaces (Computer systems)<br />
Quantum computing<br />
Diagnostic imaging—Digital techniques<br />
Tumors—Diagnosis—Data processing<br />
Pathology, Cellular—Data processing]]></dcterms:subject>
    <dcterms:abstract><![CDATA[Accurate analysis of histopathological images is essential for cancer diagnosis, prognostic assessment, and biomarker discovery. However, robust tumor analysis from whole-slide images (WSIs) remains challenging due not only to the gigapixel scale and morphological heterogeneity of histopathological data, but also to limitations of existing AI approaches. Current deep learning models often rely on a single-model paradigm, limiting their ability to simultaneously capture local cellular morphology, global tissue context, and uncertainty associated with complex pathological patterns while maintaining more consistent performance under heterogeneous clinical conditions. This dissertation presents a reinforcement learning (RL)–guided adaptive multimodal fusion framework that integrates complementary computational paradigms for tumor classification and biomarker prediction from histopathological images. The framework consists of four interconnected modules. First, a transformer-based WSI framework for predicting MYCN amplification status directly from neuroblastoma histopathology, demonstrating the feasibility of inferring clinically relevant molecular biomarkers from tissue morphology. Second, a Vision Transformer–based cellular classification framework to distinguish various histopathological cellular classes, enabling fine-grained characterization of tumor morphology at the cellular level. Third, a hybrid classical–quantum machine learning framework that combines transformer-derived image representations with parameterized quantum circuits to investigate quantum-native optimization strategies and computational behavior for high-dimensional pathology data. Finally, an adaptive RL–guided fusion mechanism that dynamically integrates heterogeneous predictive experts, including convolutional, transformer-based, and quantum models, through confidence-aware decision policies. We evaluated the framework using a private multi-institutional neuroblastoma dataset and the publicly available iiiBreakHis dataset. Experimental results demonstrated the effectiveness of the framework for both molecular biomarker prediction and fine-grained morphological analysis. The transformer-based MYCN amplification prediction model achieved 91.8% accuracy. The adaptive fusion framework achieved 98.8% accuracy and consistently outperformed individual expert models and conventional static fusion approaches. In addition, the hybrid classical–quantum framework exhibited distinct computational behavior, demonstrating a slower increase in runtime than the classical baseline within the evaluated experimental setting as the number of whole-slide images increased. Together, these findings demonstrate that RL–guided adaptive fusion of transformerbased, convolutional, and quantum learning models enables more accurate and robust histopathological tumor classification and biomarker prediction than individual models or conventional static fusion approaches. This dissertation contributes a unified computational framework for more stable histopathological tumor analysis and demonstrates how adaptive integration of complementary learning paradigms can support biomarker prediction and tumor classification from digital pathology images.]]></dcterms:abstract>
    <dcterms:creator><![CDATA[Md Jobair Hossain Faruk]]></dcterms:creator>
    <dcterms:publisher><![CDATA[Old Westbury, N.Y. : New York Institute of Technology, [2026]]]></dcterms:publisher>
    <dcterms:date><![CDATA[2026]]></dcterms:date>
    <dcterms:format><![CDATA[PDF]]></dcterms:format>
    <dcterms:language><![CDATA[English]]></dcterms:language>
    <dcterms:identifier><![CDATA[<a href="https://repository.nyitlibrary.org/files/original/fbe2f3906c5e15b72ec2b4d254d01762.pdf">https://repository.nyitlibrary.org/files/original/fbe2f3906c5e15b72ec2b4d254d01762.pdf</a>]]></dcterms:identifier>
</rdf:Description></rdf:RDF>
