I2S Masters/ Doctoral Theses

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.

Significant developments in communication methods to help support at-risk populations have increased over the last 10 years. We view at-risk populations as a group of people present in environments where the use of infrastructure or electricity, including telecommunications, is censored and/or dangerous. Security features that accompany these communication mechanisms are essential to protect the confidentiality of its user base and the integrity and availability of the communication network.

In this work, we look at the feasibility of using Bluetooth Mesh as a communication network and analyze the security features that are inherent to the protocol. Through this analysis we determine the strengths and weaknesses of Bluetooth Mesh security features when used as a messaging medium for at risk populations and provide improvements to current shortcomings. Our analysis includes looking at the Bluetooth Mesh Networking Security Fundamentals as described by the Bluetooth Sig: Encryption and Authentication, Separation of Concerns, Area isolation, Key Refresh, Message Obfuscation, Replay Attack Protection, Trashcan Attack Protection, and Secure Device Provisioning.  We look at how each security feature is implemented and determine if these implementations are sufficient in protecting the users from various attack vectors. For example, we examined the Blue Mirror attack, a reflection attack during the provisioning process which leads to the compromise of network keys, while also assessing the under-researched key refresh mechanism. We propose a mechanism to address Blue-Mirror-oriented attacks with the goal of creating a more secure provisioning process.  To analyze the key refresh mechanism, we implemented our own full-fledged Bluetooth Mesh network and implemented a key refresh mechanism. Through this we form an assessment of the throughput, range, and impacts of a key refresh in both lab and field environments that demonstrate the suitability of our solution as a secure communication method.

With the increasing scale of high-performance computing (HPC) systems, transient bit-flip errors are now more likely than ever, posing a threat to long-running scientific applications. A substantial portion of these applications involve the simulation of partial differential equations (PDEs) modeling physical processes over discretized spatial and temporal domains, with some requiring the solving of sparse linear systems. While these applications are often paired with system-level application-agnostic resilience techniques such as checkpointing and replication, the utilization of these techniques imposes significant overhead. In this work, we present a probability-aware framework that produces low-overhead selective protection schemes for the widely used Preconditioned Conjugate Gradient (PCG) method, whose performance can heavily degrade due to error propagation through the sparse matrix-vector multiplication (SpMV) operation. Through the use of a straightforward mathematical model and an optimized machine learning model, our selective protection schemes incorporate error probability to protect only certain crucial operations. An experimental evaluation using 15 matrices from the SuiteSparse Matrix Collection demonstrates that our protection schemes effectively reduce resilience overheads, often outperforming or matching both baseline and established protection schemes across all error probabilities.

The growth of IoT and voice assistant (VA) apps poses increasing concerns about sensitive data leaks. While privacy policies are required to describe how these apps use private user data (i.e., data practice), problems such as missing, inaccurate, and inconsistent policies have been repeatedly reported. Therefore, it is important to assess the actual data practice in apps and identify the potential gaps between the actual and declared data usage. We find that app stores lack in regulating the compliance between the app practices and their declaration, so we use AI to discover the compliance issues in these apps to assist the regulators and developers. For VA apps, we also develop a mechanism to enforce the compliance using AI. In this work, we conduct a measurement study using our framework called IoTPrivComp, which applies an automated analysis of IoT apps’ code and privacy policies to identify compliance gaps. We collect 1,489 IoT apps with English privacy policies from the Play Store. IoTPrivComp detects 532 apps with sensitive external data flows, among which 408 (76.7%) apps have undisclosed data leaks. Moreover, 63.4% of the data flows that involve health and wellness data are inconsistent with the practices disclosed in the apps’ privacy policies. Next, we focus on the compliance issues in skills. VAs, such as Amazon Alexa, are integrated with numerous devices in homes and cars to process user requests using apps called skills. With their growing popularity, VAs also pose serious privacy concerns. Sensitive user data captured by VAs may be transmitted to third-party skills without users’ consent or knowledge about how their data is processed. Privacy policies are a standard medium to inform the users of the data practices performed by the skills. However, privacy policy compliance verification of such skills is challenging, since the source code is controlled by the skill developers, who can make arbitrary changes to the behaviors of the skill without being audited; hence, conventional defense mechanisms using static/dynamic code analysis can be easily escaped. We present Eunomia, the first real-time privacy compliance firewall for Alexa Skills. As the skills interact with the users, Eunomia monitors their actions by hijacking and examining the communications from the skills to the users, and validates them against the published privacy policies that are parsed using a BERT-based policy analysis module. When non-compliant skill behaviors are detected, Eunomia stops the interaction and warns the user. We evaluate Eunomia with 55,898 skills on Amazon skills store to demonstrate its effectiveness and to provide a privacy compliance landscape of Alexa skills.

Deep learning leads the performance in many areas of computer vision. However, after a decade of research, it tends to require larger datasets and more complex models, leading to heightened resource consumption across all fronts. Regrettably, meeting these requirements proves challenging in many real-life scenarios. First, both data collection and labeling processes entail substantial labor and time investments. This challenge becomes especially pronounced in domains such as medicine, where identifying rare diseases demands meticulous data curation. Secondly, the large size of state-of-the-art models, such as ViT, Stable Diffusion, and ConvNext, hinders their deployment on resource-constrained platforms like mobile devices. Research indicates pervasive redundancies within current neural network structures, exacerbating the issue. Lastly, even with ample datasets and optimized models, the time required for training and inference remains prohibitive in certain contexts. Consequently, there is a burgeoning interest among researchers in exploring avenues for efficient artificial intelligence.

This study endeavors to delve into various facets of efficiency within computer vision, including data efficiency, model efficiency, as well as training and inference efficiency. The data efficiency is improved from the perspective of increasing information brought by given image inputs and reducing redundancies of RGB image formats. To achieve this, we propose to integrate both spatial and frequency representations to finetune the classifier. Additionally, we propose explicitly increasing the input information density in the frequency domain by deleting unimportant frequency channels. For model efficiency, we scrutinize the redundancies present in widely used vision transformers. Our investigation reveals that trivial attention in their attention modules covers useful non-trivial attention due to its large amount. We propose mitigating the impact of accumulated trivial attention weights. To increase training efficiency, we propose SuperLoRA, a generation of LoRA adapter, to fine-tune pretrained models with few iterations and extremely-low parameters. Finally, a model simplification pipeline is proposed to further reduce inference time on mobile devices. By addressing these challenges, we aim to advance the practicality and performance of computer vision systems in real-world applications.

Recent years have seen tremendous developments in the field of computer vision and its extensive applications. The fundamental task, image classification, benefiting from deep convolutional neural networks (CNN)'s extraordinary ability to extract deep semantic information from input data, has become the backbone for many other computer vision tasks, like object detection and segmentation. A modern detection usually has bounding-box regression and class prediction with a pre-trained classification model as the backbone. The architecture is proven to produce good results, however, improvements can be made with closer inspections. A detector takes a pre-trained CNN from the classification task and selects the final bounding boxes from multiple proposed regional candidates by a process called non-maximum suppression (NMS), which picks the best candidates by ranking their classification confidence scores. The localization evaluation is absent in the entire process. Another issue is the classification uses one-hot encoding to label the ground truth, resulting in an equal penalty for misclassifications between any two classes without considering the inherent relations between the classes. Ultimately, the realms of 2D image classification and 3D point cloud classification represent distinct avenues of research, each relying on significantly different architectures. Given the unique characteristics of these data types, it is not feasible to employ models interchangeably between them.

My research aims to address the following issues. (1) We proposed the first location-aware detection framework for single-shot detectors that can be integrated into any single-shot detectors. It boosts detection performance by calibrating the ranking process in NMS with localization scores. (2) To more effectively back-propagate gradients, we designed a super-class guided architecture that consists of a superclass branch (SCB) and a finer class branch (FCB). To further increase the effectiveness, the features from SCB with high-level information are fed to FCB to guide finer class predictions. (3) Recent works have shown 3D point cloud models are extremely vulnerable under adversarial attacks, which poses a serious threat to many critical applications like autonomous driving and robotic controls. To gap the domain difference in 3D and 2D classification and to increase the robustness of CNN models on 3D point cloud models, we propose a family of robust structured declarative classifiers for point cloud classification. We experimented with various 3D-to-2D mapping algorithm, bridging the gap between 2D and 3D classification. Furthermore, we empirically validate the internal constrained optimization mechanism effectively defend adversarial attacks through implicit gradients.

The last century has seen incredible growth in the field of quantum computing. Quantum computation offers the opportunity to find efficient solutions to certain computational problems which are intractable on classical computers. One class of problems that seems to benefit from quantum computing is the Hidden Subgroup Problem (HSP). The HSP includes, as special cases, the problems of integer factoring, discrete logarithm, shortest vector, and subset sum - making the HSP incredibly important in various fields of research.                               

The presented research examines the HSP for Dihedral groups with order 2^n and proves a quantum polynomial-time reduction to the so-called Codomain Fiber Intersection Problem (CFIP). The usual approach to the HSP relies on harmonic analysis in the domain of the problem and the best-known algorithm using this approach is sub-exponential, but still super-polynomial. The algorithm we will present deviates from the usual approach by focusing on the structure encoded in the codomain and uses this structure to direct a “walk” down the subgroup lattice terminating at the hidden subgroup.                               

Though the algorithm presented here is specifically designed for the DHSP, it has potential applications to many other types of the HSP. It is hypothesized that any group with a sufficiently structured subgroup lattice could benefit from the analysis developed here. As this approach diverges from the standard approach to the HSP it could be a promising step in finding an efficient solution to this problem.

Information theory provides methods for quantifying the information content of observed signals and has found application in the radar sensing space for many years. Here, we examine a type of information derived from Fisher information known as Marginal Fisher Information (MFI) and investigate its use to design pulse-agile waveforms. By maximizing this form of information, the expected error covariance about an estimation parameter space may be minimized. First, a novel method for designing MFI optimal waveforms given an arbitrary waveform model is proposed and analyzed. Next, a transformed domain approach is proposed in which the estimation problem is redefined such that information is maximized about a linear transform of the original estimation parameters. Finally, informationally optimal waveform design is paired with informationally optimal estimation (receive processing) and are combined into a cognitive radar concept. Initial experimental results are shown and a proposal for continued research is presented.

In uniform pulse-Doppler radar, there is a well known trade-off between unambiguous Doppler and unambiguous range. Pulse repetition interval (PRI) staggering, a technique that involves modulating the interpulse times, addresses this trade-space allowing for expansion of the unambiguous Doppler domain with little range swath incursion. Random PRI staggering provides additional diversity, but comes at the cost of increased Doppler sidelobes. Thus, careful PRI sequence design is required to avoid spurious sidelobe peaks that could result in false alarms.

In this thesis, two random PRI stagger models are defined and compared, and sidelobe peak mitigation is discussed. First, the co-array concept (borrowed from the intuitively related field of sparse array design in the spatial domain) is utilized to examine the effect of redundancy on sidelobe peaks for random PRI sequences. Then, a sidelobe peak suppression technique is introduced that involves a gradient-based optimization of the random PRI sequences, producing pseudo-random sequences that are shown to significantly reduce spurious Doppler sidelobes in both simulation and experimentally.

Electrostriction in an optical fiber is introduced by the interaction between the forward propagated optical signal and the acoustic standing waves in the radial direction resonating between the center of the core and the cladding circumference of the fiber. The response of electrostriction is dependent on fiber parameters, especially the mode field radius. A novel technique is demonstrated to characterize fiber properties by means of measuring their electrostriction response under intensity modulation. As the spectral envelope of electrostriction-induced propagation loss is anti-symmetrical, the signal-to-noise ratio can be significantly increased by subtracting the measured spectrum from its complex conjugate. It is shown that if the transversal field distribution of the fiber propagation mode is Gaussian, the envelope of the electrostriction-induced loss spectrum closely follows a Maxwellian distribution whose shape can be specified by a single parameter determined by the mode field radius.