Physical Sciences and Mathematics Commons^™

Enhancing Robustness Of Machine Learning Models Against Adversarial Attacks, Ronak Guliani

University Honors Theses

Machine learning models are integral for numerous applications, but they remain increasingly vulnerable to adversarial attacks. These attacks involve subtle manipulation of input data to deceive models, presenting a critical threat to their dependability and security. This thesis addresses the need for strengthening these models against such adversarial attacks. Prior research has primarily focused on identifying specific types of adversarial attacks on a limited range of ML algorithms. However, there is a gap in the evaluation of model resilience across algorithms and in the development of effective defense mechanisms. To bridge this gap, this work adopts a two-phase approach. First, …

Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie

Student Research Symposium

Typical Artificial Neural Networks (ANNs) have static architectures. The number of nodes and their organization must be chosen and tuned for each task. Choosing these values, or hyperparameters, is a bit of a guessing game, and optimizing must be repeated for each task. If the model is larger than necessary, this leads to more training time and computational cost. The goal of this project is to evolve networks that grow according to the task at hand. By gradually increasing the size and complexity of the network to the extent that the task requires, we will build networks that are more …

Quantum Grover's Oracles With Symmetry Boolean Functions, Peng Gao

Dissertations and Theses

Quantum computing has become an important research field of computer science and engineering. Among many quantum algorithms, Grover's algorithm is one of the most famous ones. Designing an effective quantum oracle poses a challenging conundrum in circuit and system-level design for practical application realization of Grover's algorithm.

In this dissertation, we present a new method to build quantum oracles for Grover's algorithm to solve graph theory problems. We explore generalized Boolean symmetric functions with lattice diagrams to develop a low quantum cost and area efficient quantum oracle. We study two graph theory problems: cycle detection of undirected graphs and generalized …

Multiple Diagram Navigation, Hisham Benotman

Dissertations and Theses

Domain novices learning about a new subject can struggle to find their way in large collections. Typical searching and browsing tools are better utilized if users know what to search for or browse to. In this dissertation, we present Multiple Diagram Navigation (MDN) to assist domain novices by providing multiple overviews of the content matter using multiple diagrams. Rather than relying on specific types of visualizations, MDN superimposes any type of diagram or map over a collection of documents, allowing content providers to reveal interesting perspectives of their content. Domain novices can navigate through the content in an exploratory way …

Fractals As Basis For Design And Critique, John Charles Driscoll

Dissertations and Theses

The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal …

A Resource Constrained Shortest Paths Approach To Reducing Personal Pollution Exposure, Elling Payne

REU Final Reports

As wildfires surge in frequency and impact in the Pacific Northwest, in tandem with increasingly traffic-choked roads, personal exposure to harmful airborne pollutants is a rising concern. Particularly at risk are school-age children, especially those living in disadvantaged communities near major motorways and industrial centers. Many of these children must walk to school, and the choice of route can effect exposure. Route-planning applications and frameworks utilizing computational shortest paths methods have been proposed which consider personal exposure with reasonable success, but few have focused on pollution exposure, and all have been limited in scalability or geographic scope. This paper addresses …

The Silencing Power Of Algorithms: How The Facebook News Feed Algorithm Manipulates Users' Perceptions Of Opinion Climates, Callie Jessica Morgan

University Honors Theses

This extended literature review investigates how the architecture and features of the Facebook Newsfeed algorithm, EdgeRank, can inhibit and facilitate the expression of political opinions. This paper will investigate how Elisabeth Noelle-Neumann's theory on public opinion, Spiral of Silence, can be used to assess the Facebook news feed as a political opinion source that actively shapes users' perceptions of minority and majority opinion climates. The feedback loops created by the algorithm's criteria influences users' decisions to self-censor or express their political opinions with interpersonal connections and unfamiliar connections on the site.

A Parallel Mesh Generator In 3d/4d, Kirill Voronin

Portland Institute for Computational Science Publications

In the report a parallel mesh generator in 3d/4d is presented. The mesh generator was developed as a part of the research project on space-time discretizations for partial differential equations in the least-squares setting. The generator is capable of constructing meshes for space-time cylinders built on an arbitrary 3d space mesh in parallel. The parallel implementation was created in the form of an extension of the finite element software MFEM. The code is publicly available in the Github repository

Certifying Loop Pipelining Transformations In Behavioral Synthesis, Disha Puri

Dissertations and Theses

Due to the rapidly increasing complexity in hardware designs and competitive time to market trends in the industry, there is an inherent need to move designs to a higher level of abstraction. Behavioral Synthesis is the process of automatically compiling such Electronic System Level (ESL) designs written in high-level languages such as C, C++ or SystemC into Register-Transfer Level (RTL) implementation in hardware description languages such as Verilog or VHDL. However, the adoption of this flow is dependent on designers' faith in the correctness of behavioral synthesis tools.

Loop pipelining is a critical transformation employed in behavioral synthesis process, and …

Algorithm For Premature Ventricular Contraction Detection From A Subcutaneous Electrocardiogram Signal, Iris Lynn Shelly

Dissertations and Theses

Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat.

An implantable cardiac …

Prediction: The Quintessential Model Validation Test, Wayne Wakeland

Systems Science Friday Noon Seminar Series

It is essential to objectively test how well policy models predict real world behavior. The method used to support this assertion involves the review of three SD policy models emphasizing the degree to which the model was able to fit the historical outcome data and how well model-predicted outcomes matched real world outcomes as they unfolded. Findings indicate that while historical model agreement is a favorable indication of model validity, the act of making predictions without knowing the actual data, and comparing these predictions to actual data, can reveal model weaknesses that might be overlooked when all of the available …

Evaluation Of Data-Path Topologies For Self-Timed Conditional Statements, Navaneeth Prasannakumar Jamadagni

Dissertations and Theses

This research presents a methodology to evaluate data path topologies that implement a conditional statement for an average-case performance that is better than the worst-case performance. A conditional statement executes one of many alternatives depending on how Boolean conditions evaluate to true or false. Alternatives with simple computations take less time to execute. The self-timed designs can exploit the faster executing alternatives and provide an average-case behavior, where the average depends on the frequency of simple and complex computations, and the difference in the completion times of simple and complex computations. The frequency of simple and complex computations depends on …

Tweakable Ciphers: Constructions And Applications, Robert Seth Terashima

Dissertations and Theses

Tweakable ciphers are a building block used to construct a variety of cryptographic algorithms. Typically, one proves (via a reduction) that a tweakable-cipher-based algorithm is about as secure as the underlying tweakable cipher. Hence improving the security or performance of tweakable ciphers immediately provides corresponding benefits to the wide array of cryptographic algorithms that employ them. We introduce new tweakable ciphers, some of which have better security and others of which have better performance than previous designs. Moreover, we demonstrate that tweakable ciphers can be used directly (as opposed to as a building block) to provide authenticated encryption with associated …

Advances In Piecewise Smooth Image Reconstruction, Ralf Juengling

Dissertations and Theses

Advances and new insights into algorithms for piecewise smooth image reconstruction are presented. Such algorithms fit a piecewise smooth function to image data without prior knowledge of the number of regions or the location of region boundaries in the best fitting function. This is a difficult model selection problem since the number of parameters of possible solutions varies widely.

The approach followed in this work was proposed by Yvan Leclerc. It uses the Minimum Description Length principle to make the reconstruction problem well-posed: the best fitting function yields the shortest encoding of the image data. In order to derive a …

Using Acl2 To Verify Loop Pipelining In Behavioral Synthesis, Disha Puri, Sandip Ray, Kecheng Hao, Fei Xie

Civil and Environmental Engineering Faculty Publications and Presentations

Behavioral synthesis involves compiling an Electronic System-Level (ESL) design into its RegisterTransfer Level (RTL) implementation. Loop pipelining is one of the most critical and complex transformations employed in behavioral synthesis. Certifying the loop pipelining algorithm is challenging because there is a huge semantic gap between the input sequential design and the output pipelined implementation making it infeasible to verify their equivalence with automated sequential equivalence checking techniques. We discuss our ongoing effort using ACL2 to certify loop pipelining transformation. The completion of the proof is work in progress. However, some of the insights developed so far may already be of …

Interpreting Individual Classifications Of Hierarchical Networks, Will Landecker, Michael David Thomure, Luis M.A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby

Computer Science Faculty Publications and Presentations

Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known …

The Intersection Between Science And Computer Science Is Almost Empty, Dick Hamlet

Systems Science Friday Noon Seminar Series

Traditionally, a science such as physics overlaps with mathematics and engineering in a way that has been astonishingly productive. The math provides precise expression for the science, which in turn supplies the engineering with the information it needs to exploit physical phenomena. Computer science naturally wishes to put itself in the center of the traditional picture as a science. Unfortunately, it won't wash. The `science' of programming is pure and simple mathematics, not science. The distinction is more than linguistic, since science and mathematics have quite distinct goals and methods. By making the wrong choice, computer science research has been …

Evolved Design Of A Nonlinear Proportional Integral Derivative (Npid) Controller, Shubham Chopra

Dissertations and Theses

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the …

Resizable, Scalable, Concurrent Hash Tables, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers.

We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize.

To achieve this performance, this hash table implementation uses a new concurrent …

Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom

Systems Science Friday Noon Seminar Series

In this presentation I will describe the latest version of the Numenta HTM Cortical Learning Algorithm and why it is interesting for doing research into radical new computer architectures. Then I will discuss the hardware acceleration research we are doing, and briefly look at some preliminary applications development.

Higher-Level Application Of Adaptive Dynamic Programming/Reinforcement Learning – A Next Phase For Controls And System Identification?, George G. Lendaris

Systems Science Friday Noon Seminar Series

Humans have the ability to make use of experience while performing system identification and selecting control actions for changing situations. In contrast to current technological implementations that slow down as more knowledge is stored, as more experience is gained, human processing speeds up and has enhanced effectiveness. An emerging experience-based (“higher level”) approach promises to endow our technology with enhanced efficiency and effectiveness.

The notions of context and context discernment are important to understanding this human ability. These are defined as appropriate to controls and system-identification. Some general background on controls, Dynamic Programming, and Adaptive Critic leading to Adaptive Dynamic …

Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher

Systems Science Friday Noon Seminar Series

Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …

Segmentation Of Thermographic Images Of Hands Using A Genetic Algorithm, Payel Ghosh, Judith Gold, Melanie Mitchell

Computer Science Faculty Publications and Presentations

This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and …

Solving Continuous Linear Least-Squares Problems By Iterated Projection, Ralf Juengling

Computer Science Faculty Publications and Presentations

I present a new divide-and-conquer algorithm for solving continuous linear least-squares problems. The method is applicable when the column space of the linear system relating data to model parameters is “translation invariant”. The central operation is a matrix- vector product, which makes the method very easy to implement. Secondly, the structure of the computation suggests a straightforward parallel implementation.

A complexity analysis for sequential implementation shows that the method has the same asymptotic complexity as well-known algorithms for discrete linear least-squares. For illustration we work out the details for the problem of fitting quadratic bivariate polyno- mials to a piecewise …

Finding Irc-Like Meshes Sans Layer 7 Payloads, Akshay Dua, Jim Binkley, Suresh Singh

Computer Science Faculty Publications and Presentations

We present an algorithm for detecting IRC-like chat networks that does not rely on Layer 7 payload information. The goal is to extract only those meshes from conventional flows where long-term periodic data is being exchanged between an external server and multiple internal clients. Flow data is passed through a series of filters that reduce the memory requirements needed for final candidate mesh sorting. Final outputs consist of two sorted lists including the fanout list, sorted by the number of client hosts in the mesh, and a secondary list called the evil sort. The latter consists of meshes with any …

Prostate Segmentation On Pelvic Ct Images Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A genetic algorithm (GA) for automating the segmentation of the prostate on pelvic computed tomography (CT) images is presented here. The images consist of slices from three-dimensional CT scans. Segmentation is typically performed manually on these images for treatment planning by an expert physician, who uses the “learned” knowledge of organ shapes, textures and locations to draw a contour around the prostate. Using a GA brings the flexibility to incorporate new “learned” information into the segmentation process without modifying the fitness function that is used to train the GA. Currently the GA uses prior knowledge in the form of texture …

Traffic Analysis Of Udp-Based Flows In Ourmon, Jim Binkley, Divya Parekh

Computer Science Faculty Publications and Presentations

We present a custom UDP flow tuple with an IP address key and a set of simple related statistical attributes. Attributes are used to calculate a per host metric called the UDP work weight which roughly measures the amount of network noise caused by a host. The work weight is used to produce a near real-time sorted top N report for UDP host tuples. We also present a derived attribute based on an algorithm called the UDP guesstimator. The UDP guesstimator roughly classifies port report hosts into various traffic categories including security threats (DOS/scanning) or P2P hosts based on high …

Rcu Semantics: A First Attempt, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

There is not yet a formal statement of RCU (read-copy update) semantics. While this lack has thus far not been an impediment to adoption and use of RCU, it is quite possible that formal semantics would point the way towards tools that automatically validate uses of RCU or that permit RCU algorithms to be automatically generated by a parallel compiler. This paper is a first attempt to supply a formal definition of RCU. Or at least a semi-formal definition: although RCU does not yet wear a tux (though it does run in Linux), at least it might yet wear some …

Toward A Sound Integration Of Isabelle With A Combined Decision Procedure, Tom Harke

Computer Science Faculty Publications and Presentations

I present work on a project to integrate Isabelle, an extremely versatile interactive proof assistant, with a combined decision procedure, the Cooperating Validity Checker (CVC). Isabelle is sound and flexible, however it is often tedious to use. CVC is fully automatic, but only handles decision problems expressible over a relatively weak set of theories including linear arithmetic, uninterpreted functions, data types, and firstorder quantifier-free logic. My goal is to increase the amount of automation in Isabelle, by making it use CVC as an oracle for such problems, but without compromising Isabelle’s soundness.

In this paper I report on the progress …

Reconstructability Analysis Detection Of Optimal Gene Order In Genetic Algorithms, Martin Zwick, Stephen Shervais

Systems Science Faculty Publications and Presentations

The building block hypothesis implies that genetic algorithm efficiency will be improved if sets of genes that improve fitness through epistatic interaction are near to one another on the chromosome. We demonstrate this effect with a simple problem, and show that information-theoretic reconstructability analysis can be used to decide on optimal gene ordering.