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All students and faculty are welcome to attend the final defense of EECS graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.

Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.

UPCOMING DEFENSE NOTICES

Adam Petz - Formally Verified Bundling and Appraisal of Layered Attestation Protocols
PhD Dissertation Defense(CS)

When & Where:

June 8, 2022 - 10:00 AM
Nichols Hall, Room 246

Committee Members:

Perry Alexander, Chair
Alex Bardas
Drew Davidson
Andy Gill
Prasad Kulkarni

Abstract

Remote attestation is a technology for establishing trust in a remote computing system.  Core to the integrity of the attestation mechanisms themselves are components that orchestrate, cryptographically bundle, and appraise measurements of the target system.  Copland is a domain-specific language for specifying attestation protocols that operate in diverse, layered measurement topologies.  In this work we formally define and verify the Copland Compiler and Copland Virtual Machine for executing Copland protocols to produce evidence.  Appraisal is a dual un-bundling procedure over the raw evidence segments produced by arbitrary Copland-based attestations.  All artifacts are implemented as monadic, functional programs in the Coq proof assistant and verified with respect to a Copland reference semantics that characterizes attestation-relevant event traces and cryptographic evidence shapes.  Appraisal soundness is positioned within a novel end-to-end workflow that leverages formal properties of the attestation components to discharge assumptions about honest Copland participants.  These assumptions inform an existing model-finder tool that analyzes a Copland scenario in the context of an active adversary attempting to subvert attestation.  An initial case study exercises this workflow through the iterative design and analysis of a Copland protocol and accompanying security architecture for an Unmanned Air Vehicle DARPA demonstration platform.  We conclude by instantiating a more diverse benchmark of attestation patterns called the “Flexible Mechanisms for Remote Attestation”, leveraging Coq's built-in code synthesis to integrate the formal artifacts within an executable attestation environment.

 


 

Blake Bryant - A Novel Application of Distributed Ledger Technology to Enable Secure and Reliable Data Transport in Delay-Sensitive Applications
PhD Dissertation Defense(CS)

When & Where:

June 7, 2022 - 12:00 PM
Eaton Hall, Room 2001B

Committee Members:

Hossein Saiedian, Chair
Arvin Agah
Perry Alexander
Bo Luo
Reza Barati

Abstract

Multimedia networking is the area of study associated with the delivery of heterogeneous data including, but not limited to, imagery, video, audio, and interactive content. Multimedia and communication network researchers have continually struggled to devise solutions for addressing the three core challenges in multimedia delivery: security, reliability, and performance. Solutions to these challenges typically exist in a spectrum of compromises achieving gains in one aspect at the cost of one or more of the others. Networked videogames represent the pinnacle of multimedia challenges presented in a real-time, delay-sensitive, interactive format. Continual improvements to multimedia delivery have led to tools such as buffering, redundant coupling of low-resolution alternative data streams, congestion avoidance, and forced in-order delivery of best-effort service; however, videogames cannot afford to pay the latency tax of these solutions in their current state.

Practical assessments of contemporary videogame networking applications have confirmed security and performance flaws existing in well-funded, top-tier videogame titles.  This dissertation addresses these challenges through the application of a novel networking protocol, leveraging emerging blockchain technology to provide security, reliability, and performance gains to distributed network applications. This work provides a comprehensive overview of contemporary networking approaches used in delivering videogame multimedia content and their associated shortcomings. Additionally, key elements of blockchain technology are identified as focal points for solution development, notably the application of distributed ledger technology, consensus mechanisms, and smart contracts.  We conducted empirical evaluations of a network video game using both traditional TCP and UDP sockets compared with a modified video game sending state updates via hyperledger fabric channels. Reliability and security were substantially improved with no significant impact on performance.

The broader impact of this research is the improvement of real-time delivery for interactive multimedia content. This has wide-reaching effects across multiple industries including entertainment streaming, virtual conferencing, video games, manufacturing, financial transactions, and autonomous systems.

 


 

Rui Chen - Users Defined Policy Enforcement with Cross-App Interaction Discovery in IoT Platforms
MS Thesis Defense(CS)

When & Where:

June 6, 2022 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link.

Committee Members:

Fengjun Li, Chair
Alex Bardas
Bo Luo

Abstract

The Internet of Things platforms have been widely developed to better assist users to design, control, and monitor their smart home system. These platforms provide a programming interface and allows users to install a variety of IoT apps that published by third-party. As users could obtain the IoT apps from unvetted sources, a malicious app could be installed to perform unexpected behaviors that violating users’ security and safety, such as open the door when no motion detected. Additionally, prior research shows that due to the lack of access control mechanisms, even the benign IoT apps can cause severe security and safety risks by interact with each other in unanticipated ways. To address such threats, an improved access control system is needed to detect and monitor unexpected behaviors from IoT apps. In this paper, we provide a dynamic policy enforcement system for IoT that detects IoT behaviors and defines policies based on users’ expectation. The system relies on code analysis to identify single app behaviors and discover all potential cross-app interactions with configured devices. Discovered behaviors are displayed to users through app user interface and allow users to specify policy rules to restrict unwanted behaviors. Code instrumentation will be applied to guard apps actions and collect apps information at runtime. A policy enforcement module in the system will collect and enforce users specified policies at runtime by block actions that violate the policy. We implement the system with benign and malicious apps on SmartThings platform and shows that our system can effectively identify cross-app interactions and correctly enforce policy violations.

 


 

Gerald Brandon Ravenscroft - Spectral Cohabitation and Interference Mitigation via Physical Radar Emissions
PhD Comprehensive Defense(EE)

When & Where:

May 27, 2022 - 10:00 AM
Nichols Hall, Room 246

Committee Members:

Shannon Blunt, Chair
Christopher Allen
Erik Perrins
James Stiles
Chris Depcik

Abstract

Auctioning of frequency bands to support growing demand for high bandwidth 5G communications is driving research into spectral cohabitation strategies for next generation radar systems. The loss of radio frequency (RF) spectrum once designated for radar operation is forcing radar systems to either learn how to coexist in these frequency spectrum bands, without causing mutual interference, or move to other bands of the spectrum, the latter being the more undesirable choice. Two methods of spectral cohabitation are proposed and presented in this work, each taking advantage of recent developments in random FM (RFM) waveforms, which have the advantage of never repeating. RFM waveforms are optimized to have favorable radar waveform properties while also readily incorporating agile spectral notches. The first method of spectral cohabitation uses these spectral notches to avoid narrow-band RF interference (RFI) in the form of other spectrum users residing in the same band as the radar system, allowing both to operate while minimizing mutual interference. The second method of spectral cohabitation uses spectral notches, along with an optimization procedure, to embed a communications signal into a dual-purpose radar/communications emission, allowing one waveform to serve both functions simultaneously. Preliminary simulation and open-air experimental results are shown which attest to the efficacy of these two methods of spectral cohabitation. Improvements are proposed to extend the capabilities of each method such that they can provide further utility to both radar and communications functions while minimizing any mutually included performance degradation.

 


 

Javaria Ahmad - IoTPrivComp: Privacy Compliance in IoT Apps
PhD Comprehensive Defense(CS)

When & Where:

May 19, 2022 - 1:00 PM
Nichols Hall, Room 246

Committee Members:

Bo Luo, Chair
Alex Bardas
Tamzidul Hoque
Fengjun Li
Michael Zhuo Wang

Abstract

The growth of IoT apps poses increasing concerns on sensitive data leaks. While privacy policies are required to describe how IoT 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 IoT apps and identify the potential gaps between the actual data usage and the declared usages in the apps' privacy policies. In this work, we propose a framework called IoTPrivComp, which applies automated privacy policy and app code analysis of the IoT apps, to study the compliance gaps in IoT app practices and app privacy policies. We have collected 1,737 IoT apps from Play Store, and found that only 1,323 of them have English privacy policies available. We used IoTPrivComp to examine 411 apps that contain sensitive external data flows, and found compliance gaps in 312 (75.9%) of them. In addition, there are apps that do not have a privacy policy at all, while there is a significant number of apps that have undisclosed, inaccurately disclosed, and contradictorily disclosed data leaks. Out of the 43 data flows that involve health and wellness data, 34 (79.1%) flows were inconsistent with the disclosed practices in the app privacy policies.

 


 

PAST DEFENSE NOTICES


Jonathan Owen - Radar Spectrum Sharing via Non-repeating Frequency Notched FM Waveforms

When & Where:

May 17, 2022 - 1:00 PM
Nichols Hall, Room 246

Committee Members:

Shannon Blunt, Chair
Christopher Allen
Carl Leuschen
James Stiles
Zsolt Talata

Abstract

Spectrum sensing and transmit waveform frequency notching is a form of cognitive radar that seeks to reduce mutual interference with other spectrum users in the same band. With the reality of increasing radio frequency (RF) spectral congestion, radar systems capable of dynamic spectrum sharing are needed. The cognitive sense-and-notch (SAN) emission strategy has recently been experimentally demonstrated as an effective way in which to reduce the interference a spectrum-sharing radar causes to other in-band users. The case of modifying transmit waveform frequency notch locations when another spectrum user moves in frequency during the radar's coherent processing interval is considered here. The physical radar emission is based on a recent random FM waveform possessing attributes that are inherently robust to sidelobes that otherwise arise for spectral notching. To contend with dynamic interference the transmit notch may be required to move during the coherent processing interval (CPI), which introduces a nonstationarity effect that results in increased residual clutter after cancellation. Here a new approach to compensate for this nonstationarity is proposed that borrows the missing portion of the clutter (due to notching) from another pulsed response for which the notch is in a different location.


Eric Seals - Memory Bandwidth Dynamic Regulation and Throttling

When & Where:

May 13, 2022 - 1:00 AM
Learned Hall, Room 1136

Committee Members:

Heechul Yun, Chair
Alex Bardas
Drew Davidson

Abstract

Multi-core, integrated CPU-GPU embedded systems provide new capabilities for sophisticated real-time systems with size, weight, and power limitations; however, interference between shared resources remains a challenge in providing necessary performance guarantees. The shared main memory is a notable system bottleneck - causing throughput slowdowns and timing unpredictability.
In this paper, we propose a full system mechanism which can provide memory bandwidth regulation across both CPU and the GPU complexes. This system monitors the memory controller accesses directly through hardware statistics counters, performs memory regulation at the software level for real-time CPU tasks, and incorporates a feedback-based throttling mechanism for non-critical GPU kernels using hardware within the NVIDIA Tegra X1 memory controller subsystem. The system is built as a loadable Linux kernel module that extends the MemGuard tool. We show that this system can make CPU task execution more predictable against co-running, memory intensive interference on either CPU or GPU.


Serigne Seck - Packet Loss Prevention in Queues using SDN

When & Where:

May 12, 2022 - 11:00 AM
Eaton Hall, Room 2001B

Committee Members:

Taejoon Kim, Chair
Morteza Hashemi, Co-Chair
David Johnson

Abstract

Packets are transferred between nodes within a network. However, a packet can be dropped while trying to join the queue of a node it was routed to. In networking, this is referred to as packet loss. It can be caused by buffer scarcity in a congested network. Such phenomenon results in a reduced data rate and a delay increase due to packet retransmissions.

In this work, we propose an algorithm to perform load balancing on a network of queues via SDN to prevent packet loss. It implements a parameter K, based on the queues occupancy and traffic flow, to control an iterative packet redistribution process. In different experiments conducted on network models in which the queues varied in number, size and occupancy, our algorithm outperformed a load balancer using the Round-Robin technique.


Brian Quiroz - Mobile Edge Computing for Unmanned Vehicles

When & Where:

May 11, 2022 - 2:00 PM
Eaton Hall, Room 2001B

Committee Members:

Morteza Hashemi, Chair
Taejoon Kim
Prasad Kulkarni

Abstract

Unmanned aerial vehicles (UAVs) and autonomous vehicles are becoming more ubiquitous than ever before. From medical to delivery drones, to space exploration rovers and self-driving taxi services, these vehicles are starting to play a prominent role in society as well as in our day to day lives.

 Efficient computation and communication strategies are paramount to the effective functioning of these vehicles. Mobile Edge Computing (MEC) is an innovative network technology that enables resource-constrained devices - such as UAVs and autonomous vehicles - to offload computationally intensive tasks to a nearby MEC server. Moreover, vehicles such as self-driving cars must reliably and securely relay and receive latency-sensitive information to improve traffic safety. Extensive research performed on vehicle to vehicle (V2V) and vehicle to everything (V2X) communication indicates that they will both be further enhanced by the widespread usage of 5G technology.

 We consider two relevant problems in mobile edge computing for unmanned vehicles. The first problem was to satisfy resource-constrained UAV's need for a resource-efficient offloading policy. To that end, we implemented both a computation and an energy consumption model and trained a DQN agent that seeks to maximize task completion and minimize energy consumption. The second problem was establishing communication between two autonomous vehicles and between an autonomous vehicle and an MEC server. To accomplish this goal, we experimented by leveraging an autonomous vehicle's server to send and receive custom messages in real time. These experiments will serve as a stepping stone towards enabling mobile edge computing and device-to-device communication and computation.


Ruturaj Vaidya - Explore Effectiveness and Performance of Security Checks on Software Binaries

When & Where:

May 11, 2022 - 11:00 AM
Eaton Hall, Room 2001B

Committee Members:

Prasad Kulkarni, Chair
Alex Bardas
Drew Davidson
Esam El-Araby
Michael Vitevitch

Abstract

Binary analysis is difficult, as most of semantic and syntactic information available at source-level gets lost during the compilation process. If the binary is stripped and/ or optimized, then it negatively affects the efficacy of binary analysis frameworks. Moreover, handwritten assembly, obfuscation, excessive indirect calls or jumps, etc. further degrade their accuracy. Thus, it is important to investigate and assess the challenges to improve the binary analysis. One way of doing that is by studying security techniques implemented at binary-level.

In this dissertation we propose to implement existing compiler-level techniques for binary executables and thereby evaluate how does the loss of information at binary-level affect the performance of existing compiler-level techniques in terms of both efficiency and effectiveness.


Michael Bechtel - Shared Resource Denial-of-Service Attacks on Multicore Platforms

When & Where:

May 10, 2022 - 11:00 AM
Eaton Hall, Room 2001B

Committee Members:

Heechul Yun, Chair
Mohammad Alian
Drew Davidson
Prasad Kulkarni
Shawn Keshmiri

Abstract

With the increased adoption of machine learning algorithms across many different fields, powerful computing platforms have become necessary to meet their computational needs. Multicore platforms are a popular choice due to their ability to provide greater computing capabilities and still meet the different size, weight, and power (SWaP) constraints. As a result, multicore systems are also being employed at an increasing rate. However, contention for hardware resources between the multiple cores is a significant challenge as it can lead to interference and unpredictable timing behaviors. Furthermore, this contention can be intentionally induced by malicious actors with the specific goals of inhibiting system performance and increasing the execution time of safety-critical tasks. This is done by performing Denial-of-Service (DoS) attacks that target shared resources in order to prevent other cores from accessing them. When done properly, these DoS attacks can have significant impacts to performance and can threaten system safety. For example, we find that DoS attacks can cause >300X slowdown on the popular Raspberry Pi 3 embedded platform. Due to the inherent risks, it is vital that we discover and understand the mechanisms through which shared resource contention can occur and develop solutions that mitigate or prevent the potential impacts.

In this work, we investigate and evaluate shared resource contention on multicore platforms and the impacts it can have on the performance of real-time tasks. Leveraging this contention, we propose various Denial-of-Service attacks that each target different shared resources in the memory hierarchy with the goal of causing as much slowdown as possible. We show that each attack can inflict significant temporal slowdowns to victim tasks on target platforms by exploiting different hardware and software mechanisms. We then develop and analyze techniques for providing shared resource isolation and temporal performance guarantees for safety-critical tasks running on multicore platforms. In particular, we find that bandwidth throttling mechanisms are effective solutions against many DoS attacks and can protect the performance of real-time victim tasks.


Anushka Bhattacharya - Predicting In-Season Soil Mineral Nitrogen in Corn Production Using Deep Learning Model

When & Where:

May 10, 2022 - 9:00 AM
Nichols Hall, Room 246

Committee Members:

Taejoon Kim, Chair
Morteza Hashemi
Dorivar Ruiz Diaz

Abstract

One of the biggest challenges in nutrient management in corn (Zea mays) production is determining the amount of plant-available nitrogen (N) that will be supplied to the crop by the soil. Measuring a soil’s N-supplying power is quite difficult and approximations are often used in-lieu of intensive soil testing. This can lead to under/over-fertilization of crops, and in turn increased risk of crop N-deficiencies or environmental degradation. In this paper, we propose a deep learning algorithm to predict the inorganic-N content of the soil on a given day of the growing season. Since the historic data for inorganic nitrogen (IN) is scarce, deep learning has not yet been implemented in predicting fertilizer content. To overcome this hurdle, Generative Adversarial Network (GAN) is used to produce synthetic IN data and is trained using offline simulation data from the Decision Support System for Agrotechnology Transfer (DSSAT). Additionally, the time-series prediction problem is solved using long-short term memory (LSTM) neural networks. This model proves to be economical as it gives an estimate without the need for comprehensive soil testing, overcomes the issue of limited available data, and the accuracy makes it reliable for use.


Krushi Patel - Image Classification & Segmentation based on Enhanced CNN and Transformer Networks

When & Where:

May 9, 2022 - 2:00 PM
Nichols Hall, Room 250 - Gemini Room

Committee Members:

Fengjun Li, Chair
Prasad Kulkarni
Bo Luo
Cuncong Zhong
Guanghui Wang

Abstract

Convolutional Neural Networks (CNNs) have significantly improved the performance on various computer vision tasks such as image recognition and segmentation based on their rich representation power. To enhance the performance of CNN, a self-attention module is embedded after each layer in the network. Recently proposed Transformer-based models achieve outstanding performance by employing a multi-head self-attention module as the main building block. However, several challenges still need to be addressed, such as (1) focusing only on class-specified limited channels in CNN; (2) limited respective field in the local transformer; and (3) addition of redundant features and lack of multi-scale features in U-Net type segmentation architecture.


In our work, we propose new strategies to address these issues. First, we propose a novel channel-based self-attention module to diversify the focus more on the discriminative and significant channels, and the module can be embedded at the end of any backbone network for image classification. Second, to limit the noise added by the shallow layers of an encoder in U-Net type architecture, we replaced the skip connections with the Adaptive Global Context Module (AGCM). In addition, we introduced the Semantic Feature Enhancement Module (SFEM) for multi-scale feature enhancement in polyp segmentation. Third, we propose a Multi-scaled Overlapped Attention (MOA) mechanism in the local transformer-based network for image classification to establish the long-range dependencies and initiate the neighborhood window communication.


Justinas Lialys - Parametrically resonant surface plasmon polaritons

When & Where:

May 6, 2022 - 2:00 PM
2001B Eaton Hall

Committee Members:

Alessandro Salandrino, Chair
Kenneth Demarest
Shima Fardad
Rongqing Hui
Xinmai Yang

Abstract

The surface electromagnetic waves that propagate along a metal-dielectric or a metal-air interface are called surface plasmon polaritons (SPPs). These SPPs are advantageous in a broad range of applications, including in optical waveguides to increase the transmission rates of carrier waves, in near field optics to enhance the resolution beyond the diffraction limit, and in Raman spectroscopy to amplify the Raman signal. However, they have an inherent limitation:  as the tangential wavevector component of propagation is larger than what is permitted for the homogenous plane wave in the dielectric medium, this poses a phase-matching issue. In other words, the available spatial vector in the dielectric at a given frequency is smaller than what is required by SPP to be excited. The most commonly known technique to bypass this problem is by using the Otto and Kretschmann configurations. A glass prism is used to increase the available spatial vector in dielectric/air. Other methods are the evanescent field directional coupling, optical grating, localized scatterers, and coupling via highly focused beams. However, even with all these methods at our disposal, it is still challenging to couple SPPs that have a large propagation constant. 

As SPPs apply to a wide range of purposes, it is vitally important to overcome the SPP excitation dilemma. Presented here is a novel way to efficiently inject power into SPPs via temporal modulation of the dielectric adhered to the metal. In this configuration, the dielectric constant is modulated in time using an incident pump field. As a result of the induced changes in the dielectric constant, we show that efficient phase-matched coupling can be achieved even by a perpendicularly incident uniform plane wave. This novel method of exciting SPPs paves the way for further understanding and implementation of SPPs in a plethora of applications. For example, optical waveguides can be investigated under such excitation. Hence, this technique opens new possibilities in conventional plasmonics, as well as in the emerging field of nonlinear plasmonics. 


Andrei Elliott - Promise Land: Proving Correctness with Strongly Typed Javascript-Style Promises

When & Where:

May 6, 2022 - 2:00 PM
Nichols Hall, Room 250, Gemini Room

Committee Members:

Matt Moore, Chair
Perry Alexander
Drew Davidson

Abstract

Code that can run asynchronously is important in a wide variety of situations, from user interfaces to communication over networks, to the use of concurrency for performance gains. One widely used method of specifying asynchronous control flow is the Promise model as used in Javascript. Promises are powerful, but can be confusing and hard-to-debug. This problem is exacerbated by Javascript’s permissive type system, where erroneous code is likely to fail silently, with values being implicitly coerced into unexpected types at runtime.

The present work implements Javascript-style Promises in Haskell, translating the model to a strongly typed framework where we can use the type system to rule out some classes of bugs.

Common errors – such as failure to call one of the callbacks of an executor, which would, in Javascript, leave the Promise in an eternally-pending deadlock state – can be detected for free by the type system at compile time and corrected without even needing to run the code.

We also demonstrate that Promises form a monad, providing a monad instance that allows code using Promises to be written using Haskell’s do notation.


Hoang Trong Mai - Design and Development of Multi-band and Ultra-wideband Antennas and Circuits for Ice and Snow Radar Measurements

When & Where:

May 5, 2022 - 12:30 PM
Nichols Hall, Room 317

Committee Members:

Carl Leuschen, Chair
Fernando Rodriguez-Morales, Co-Chair
Christopher Allen

Abstract

Remote sensing based on radar technology has been successfully used for several decades as an effective tool of scientific discovery. A particular application of radar remote sensing instruments is the systematic monitoring of ice and snow masses in both hemispheres of the Earth. The operating requirements of these instruments are driven by factors such as science requirements and platform constraints, often necessitating the development of custom electronic components to enable the desired radar functionality.

This work focuses on component development and trade studies for two multichannel radar systems. First, this thesis presents the design and implementation of two dual-polarized ultra-wideband antennas for a ground-based dual-band ice penetrating radar. The first antenna operates at UHF (600–900 MHz) while the second antenna operates at VHF (140–215 MHz). Each antenna element is composed of two orthogonal octagon-shaped dipoles, two inter-locked printed circuit baluns and an impedance matching network for each polarization. Prototype of each band shows a VSWR of less than 2:1 at both polarizations over a fractional bandwidth exceeding 40%. The antennas developed offer cross-polarization isolation larger than 30 dB, an E-plane 3-dB beamwidth of 69 degrees, and a gain of at least 4 dBi with a variation of ± 1 dB across the bandwidth. This design with high power handling in mind also allows for straightforward adjustment of the antenna dimensions to meet other bandwidth constrains. It is being used as the basis for an airborne system.

Next, this work documents design details and measured performance of an improved and integrated x16 frequency multiplier system for an airborne snow-probing radar. This sub-system produces a 40 – 56 GHz linear frequency sweep from a 2.5 – 3.5 GHz chirp and mixes it down to the 2 – 18 GHz range.  The resulting chirp is used for transmission and analog de-chirping of the receive signal. The initial prototype developed through this work provided a higher level of integration and wider fractional bandwidth (>135%) compared to earlier versions implemented with the same frequency plan and a path to guide future realizations.

Lastly, this work documents a series of trade studies on antenna array configurations for both radar systems using electromagnetic simulation tools and measurements.


Xi Mo - Convolutional Neural Network in Pattern Recognition

When & Where:

May 4, 2022 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link.

Committee Members:

Cuncong Zhong, Chair
Taejoon Kim
Fengjun Li
Bo Luo
Hauzhen Fang

Abstract

Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier.

As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification.

Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN.

The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics.


Sirisha Thippabhotla - An Integrated Approach for de novo Gene Prediction, Assembly and Biosynthetic Gene Cluster Discovery of Metagenomic Sequencing Data

When & Where:

April 29, 2022 - 10:30 AM
Eaton Hall, Room 1

Committee Members:

Cuncong Zhong, Chair
Prasad Kulkarni
Fengjun Li
Zijun Yao
Liang Xu

Abstract

Metagenomics is the study of genomic content present in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling, and peptide assembly. Unfortunately, their informational dependencies have been overlooked, and have been formulated as independent problems. 

In this work, we propose a novel de novo analysis pipeline that leverages these informational dependencies, to improve functional analysis of metagenomics data. Specifically, the pipeline will contain four novel modules: an assembly graph module, a graph-based gene calling module, a peptide assembly module, and a biosynthetic gene cluster (BGC) discovery module. The assembly graph module will be computational and memory efficient. It will be based on a combination of de Bruijn and string graphs. The assembly graphs contain important sequencing information, which can be further exploited to improve functional annotation. De novo gene-calling enables us to predict novel genes and protein sequences, that have not been previously characterized. We hypothesize that de novo gene calling can benefit from assembly graph structures, as they contain important start/stop codon information that provide stronger ORF signals. The assembly graph framework will be designed for both nucleotide and protein sequences. The resulting protein sequences from gene calling can be further assembled into longer protein contigs using our assembly framework. For the novel BGC module, the gene members of a BGC will be marked in the assembly graph. Finding a BGC can be achieved by identifying a path connecting its gene members in the assembly graph. Experimental results have shown that our proposed pipeline improved existing gene calling sensitivity on unassembled reads, achieving a 10-15% improvement in sensitivity over the state-of-the-art methods, at a high specificity (>90%). Our pipeline further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins, delivering more hypothetical protein sequences.


Naveed Mahmud - Towards Complete Emulation of Quantum Algorithms using High-Performance Reconfigurable Computing

When & Where:

April 28, 2022 - 4:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link.

Committee Members:

Esam El-Araby, Chair
Perry Alexander
Prasad Kulkarni
Heechul Yun
Tyrone Duncan

Abstract

Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving specific problems. At present, two critical challenges for quantum computing are quantum state decoherence, and low scalability of current quantum devices. Decoherence places constraints on realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires quantum circuits that violate decoherence constraints. In addition, current quantum devices are of small-scale having low quantum bit(qubit) counts, and often producing inaccurate or noisy measurements, which also impacts the realistic applicability of real-world quantum algorithms. Consequently, benchmarking of existing quantum algorithms and investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms. Hardware-based emulation has been alternatively proposed as a more cost-effective and power-efficient approach. This work proposes a hardware-based emulation methodology for quantum algorithms, using cost-effective Field-Programmable Gate-Array(FPGA) technology. The proposed methodology consists of three components that are required for complete emulation of quantum algorithms; the first component models classical-to-quantum(C2Q) data encoding, the second emulates the behavior of quantum algorithms, and the third models the process of measuring the quantum state and extracting classical information, i.e., quantum-to-classical(Q2C) data decoding. The proposed emulation methodology is used to investigate and optimize methods for C2Q/Q2C data encoding/decoding, as well as several important quantum algorithms such as Quantum Fourier Transform(QFT), Quantum Haar Transform(QHT), and Quantum Grover’s Search(QGS). This work delivers contributions in terms of reducing complexities of quantum circuits, extending and optimizing quantum algorithms, and developing new quantum applications. For higher emulation performance and scalability of the framework, hardware design techniques and hardware architectural optimizations are investigated and proposed. The emulation architectures are designed and implemented on a high-performance-reconfigurable-computer(HPRC), and proposed quantum circuits are implemented on a state-of-the-art quantum processor. Experimental results show that the proposed hardware architectures enable emulation of quantum algorithms with higher scalability, higher accuracy, and higher throughput, compared to existing hardware-based emulators. As a case study, quantum image processing using multi-spectral images is considered for the experimental evaluations. 


Cecelia Horan - Open-Source Intelligence Investigations: Development and Application of Efficient Tools

When & Where:

April 27, 2022 - 10:00 AM
2001B Eaton Hall

Committee Members:

Hossein Saiedian, Chair
Drew Davidson
Fengjun Li

Abstract

Open-source intelligence is a branch within cybercrime investigation that focuses on information collection and aggregation. Through this aggregation, investigators and analysts can analyze the data for connections relevant to the investigation. There are many tools that assist with information collection and aggregation. However, these often require enterprise licensing. A solution to enterprise licensed tools is using open-source tools to collect information, often by scraping websites. These tools provide useful information, but they provide a large number of disjointed reports. The framework we developed automates information collection, aggregates these reports, and generates one single graphical report. By using a graphical report, the time required for analysis is also reduced. This framework can be used for different investigations. We performed a case study regarding the performance of the framework with missing person case information. It showed a significant improvement in the time required for information collection and report analysis. 


Ishrak Hayet - Invernet: An Adversarial Attack Framework to Infer Downstream Context Distribution Through Word Embedding Inversion

When & Where:

April 26, 2022 - 1:00 PM
Nichols Hall, Room 246

Committee Members:

Bo Luo, Chair
Zijun Yao, Co-Chair
Alex Bardas
Fengjun Li

Abstract

Word embedding has become a popular form of data representation that is used to train deep neural networks in many natural
language processing tasks, such as Machine Translation, Question Answer Generation, Named Entity Recognition, Next
Word/Sentence Prediction etc. With embedding, each word is represented as a dense vector which captures its semantic relationship
with other words and can better empower Machine Learning models to achieve state-of-the-art performance.
However, due to the memory and time intensive nature of learning such word embeddings, transfer learning has emerged as a
common practice to warm start the training process. As a result, an efficient way is to initialize with pretrained word vectors and then
fine-tune those on downstream domain specific smaller datasets. This study aims to find whether we can infer the contextual
distribution (i.e., how words cooccur in a sentence driven by syntactic regularities) of the downstream datasets given that we have
access to the embeddings from both pre-training and fine-tuning processes.
In this work, we propose a focused sampling method along with a novel model inversion architecture “Invernet” to invert word
embeddings into the word-to-word context information of the fine-tuned dataset. We consider the popular word2Vec models
including CBOW, SkipGram, and GloVe based algorithms with various unsupervised settings. We conduct extensive experimental
study on two real-world news datasets: Antonio Gulli’s News Dataset from Hugging Face repository and a New York Times dataset
from both quantitative and qualitative perspectives. Results show that “Invernet” has been able to achieve an average F1 score of 0.75
and an average AUC score of 0.85 in an attack scenario.
A concerning pattern from our experiments reveal that embedding models that are generally considered superior in different tasks
tend to be more vulnerable to model inversion. Our results suggest that a significant amount of context distribution information from
the downstream dataset can potentially leak if an attacker gets access to the pretrained and fine-tuned word embeddings. As a result,
attacks using “Invernet” can jeopardize the privacy of the users whose data might have been used to fine-tune the word embedding
model.


Sohaib Kiani - Designing Secure and Robust Machine Learning Models

When & Where:

April 25, 2022 - 10:00 AM
Nichols Hall, Room 250, Gemini Room

Committee Members:

Bo Luo, Chair
Alex Bardas
Fengjun Li
Cuncong Zhong
Xuemin Tu

Abstract

With the growing computational power and the enormous data available from many sectors, applications with machine learning (ML) components are widely adopted in our everyday lives. One major drawback associated with ML models is hard to guarantee same performance with changing environment. Since ML models are not traditional software that can be tested end-to-end. ML models are vulnerable against distributional shifts and cyber-attacks. Various cyber-attacks against deep neural networks (DNN) have been proposed in the literature, such as poisoning, evasion, backdoor, and model inversion. In the evasion attacks against DNN, the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications.

In our work, we proposed a novel multi-view adversarial image detector, namely ‘Argos’, based on a novel observation. That is, there exist two” souls” in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the “views”) will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that ‘Argos’ expects to detect. To this end, ‘Argos’ first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that ‘Argos’ significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks.


Timothy Barclay - Proof-Producing Synthesis of CakeML from Coq

When & Where:

April 22, 2022 - 2:00 PM
Nichols Hall, Room 246

Committee Members:

Perry Alexander, Chair
Alex Bardas
Drew Davidson
Matthew Moore
Eileen Nutting

Abstract

Coq's extraction plugin is used to produce code of a general purpose
  programming language from a specification written in the Calculus of Inductive
  Constructions (CIC). Currently, this mechanism is trusted, since there is no
  formal connection between the synthesized code and the CIC terms it originated
  from. This comes from a lack of formal specifications for the target
  languages: OCaml, Haskell, and Scheme. We intend to use the formally specified
  CakeML language as an extraction target, and generate a theorem in Coq that
  relates the generated CakeML abstract syntax to the CIC terms it is generated
  from. This work expands on the techniques used in the HOL4 translator from
  Higher Order Logic to CakeML. The HOL4 translator also allows for the
  generation of stateful code from the state and exception monad. We expand on
  their techniques by extracting terms with dependent types, and generating
  stateful code for other kinds of monads, like the reader monad, depending on
  what kind of computation the monad intends to represent.


Grant Jurgensen - A Verified Architecture for Trustworthy Remote Attestation

When & Where:

April 15, 2022 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link.

Committee Members:

Perry Alexander, Chair
Drew Davidson
Matthew Moore

Abstract

Remote attestation is a process where one digital system gathers and provides evidence of its state and identity to an external system. For this process to be successful, the external system must find the evidence convincingly trustworthy within that context. Remote attestation is difficult to make trustworthy due to the external system’s limited access to the attestation target. In contrast to local attestation, the appraising system is unable to directly observe and oversee the attestation target. In this work, we present a system architecture design and prototype implementation that we claim enables trustworthy remote attestation. Furthermore, we formally model the system within a temporal logic embedded in the Coq theorem prover and present key theorems that strengthen this trust argument.


Kaidong Li - Accurate and Robust Object Detection and Classification Based on Deep Neural Networks

When & Where:

January 10, 2022 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link.

Committee Members:

Cuncong Zhong, Chair
Taejoon Kim
Fengjun Li
Bo Luo
Haiyang Chao

Abstract

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.

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 increase the robustness of CNN models on 3D point cloud models, we propose a family of robust structured declarative classifiers for point cloud classification, where the internal constrained optimization mechanism can effectively defend adversarial attacks through implicit gradients.


Christian Daniel - Dynamic Metasurface Grouping for IRS Optimization in Massive MIMO Communications

When & Where:

January 6, 2022 - 3:00 PM
246 Nichols Hall

Committee Members:

Erik Perrins, Chair
Taejoon Kim, Co-Chair
Morteza Hashemi

Abstract

Intelligent Reflecting Surfaces (IRSs) grant the ability to control what was once considered the uncontrollable part of wireless communications, the channel. These smart signal mirrors show promise to significantly improve the effective signal-to-noise-ratio (SNR) of cell-users when the line-of-sight (LOS) channel between the base station (BS) and user is blocked. IRSs use implementable optimized phase shifts that beamform a reflected signal around channel blockages, and because they are passive devices, they have the benefit of having low cost and low power consumption. Previous works have concluded that IRSs need several hundred elements to outperform relays. Unfortunately, overhead and complexity costs related to optimizing these devices limit their scope to single-input single-output (SISO) systems. With multiple-input multiple-output (MIMO) and Massive MIMO becoming crucial components to modern 5G and beyond networks, a way to mitigate these overhead costs and integrate IRS technology with the promising MIMO techniques is paramount for these devices to have a place within modern cell technologies. This thesis proposes an IRS element grouping scheme that greatly reduces the number of unique IRS phases that need to be calculated and sent to the IRS controller via the limited rate feedback channel and allows for the ideal number of groups to be obtained at the BS before data transmission. Three methods are proposed to design the phase shifts and element partitioning within our scheme to improve effective SNR in an IRS-aided system. In our simulations, it is shown that our best performing method is one that dynamically partitions the IRS elements into non- uniform groups based on information gathered from the reflected channel and then optimizes its phase shifts. This method successfully handles the overhead trade-off problem, and shows significant achievable rate improvement from previous works.


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