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 data in a way that (1) is robust against common misuses and (2) can, in some cases, result in significantly shorter ciphertexts than other approaches.

Many practical problems in the field of engineering become so complex that they may be effectively solved only with the aid of a computer. An effective solution depends on the use of an efficient algorithm. Plotting root locus diagrams is such a problem. This thesis presents such an algorithm. Root locus design of feedback control systems is a very powerful tool. Stability of systems under the influence of variables can be easily determined from the root locus diagram. For even moderately complex systems of the type found in practical applications, determination of the locus is extremely difficult if accuracy is required. The difficulty lies in the classical method of graphically determining the location of points on the locus by trial and error. Such a method cannot be efficiently applied to a computer program. The text presents an original algorithm for plotting the root locus of a general system. The algorithm is derived using the combined methods of complex variable algebra and numerical analysis. For each abscissa desired a polynomial is generated. The real roots of this polynomial are the ordinate values for points on the root locus. Root finding methods from numerical analysis enable the solution of the problem to be one of convergent iteration rather than trial and error. Among the material presented is a computer program for solution of the general problem, an example of a completely analytic solution, and a table of solutions for more simple systems. The program inputs are the coefficients of the open loop transfer function and the range and increments of the real axis which are to be swept. The output lists the real and imaginary components of all solution points at each increment of the sweep. Also listed are the magnitude and angle components of the solution point and the value of system gain for which this is a solution. For less complex problems, the method can be applied analytically. This may result in an explicit relation between the real and imaginary components of all solution points or even in a single expression which can be analyzed using the methods of analytic geometry. As with any advance in the theory of problem solving, the ideas presented in the thesis are best applied in conjunction with previous solution methods. Specifically, an idea of the approximate location of the root locus can be obtained using sketching rules which are well known. The method presented here becomes much more efficient when even a rough approximation is known. Furthermore, the specific locations of system poles and zeros are not required, but can be helpful in planning areas in which to search for solutions.

Connotational subtyping is an approach to typing that allows an object's type to change dynamically, following changes to the object's internal state. This allows for a more precise representation of a problem domain with logical objects that have variable behavior. Two approaches to supporting connotational subtyping in the Ruby programming language were implemented: a language-level implementation using pure Ruby and a modification to the Ruby 1.8.7 interpreter. While neither implementation was wholly successful the language level implementation created complications with reflective language features like self and super and, while Ruby 1.8.7 has been obsoleted by Ruby 1.9 (YARV), the results suggest that Chambers-style, predicate-based runtime type inference could be incorporated into Ruby with only some reduced interpreter performance.

Many models have been developed to explain the process of self organization-the emergence of seemingly purposeful behaviors from groups of entities with limited individual intelligence. However, the underlying behavior that facilitates the emergence of this global pattern is not generally well understood. Our study focuses on different low complexity building algorithms and characterizes how nests are built using these algorithms. Three rules postulated to be functions of wasps' building behavior were developed. First is the random rule, in which there is no constraint per the choice of site to be initiated. The second is the 2-cell rule where only sites with at least two ready walls are initiated. Third, the maxWall rule ensures only sites with the maximum number of ready walls are initiated. This work provides better insight and visualization through simulation into wasps building behavior. This acquired knowledge can be applied to robotics and distributed optimization processes.

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable. This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.

Fifty percent of Fortune 500 companies trust Xen, an open-source bare-metal hypervisor, to virtualize their websites and mission critical services in the cloud. Providing superior fault tolerance, scalability, and migration, virtualization allows these companies to run several isolated operating systems simultaneously on the same physical server. These isolated operating systems, called virtual machines, require a virtual traffic guard to cooperate with one another. This guard known as the Credit2 scheduler along with the newest Xen hypervisor was recently developed to supersede the older schedulers. Since wasted CPU cycles can be costly, the Credit2 prototype must undergo significant performance validation before being released into production. Furthermore, leading commercial virtualization products, including VMWare and Microsoft Hyper-V frequently adopt Xen's proven technologies. This thesis provides quantitative performance measurements of the Credit1 and Credit2 schedulers, and provides recommendations for building hypervisor schedulers.

Video game developers attempt to convey moods to emphasize their game's narrative. Events that occur within the game usually convey success or failure in some way meaningful to the story's progress. Ideally, when these events occur, the intended change in mood should be perceivable to the player. One way of doing so is to change the music. This requires musical tracks to represent many possible moods, states and game events. This can be very taxing on composers, and encoding the control flow (when to transition) of the tracks can prove to be tricky as well. This thesis presents AUD.js, a system developed for procedural music generation for JavaScript-based web games. By taking input from game events, the system can create music corresponding to various Western perceptions of music mood. The system was trained with classic video game music. Game development students rated the mood of 80 pieces, after which statistical representations of those pieces were extracted and added into AUD.js. AUD.js can adapt its generated music to new sets of input parameters, thereby updating the perceived mood of the generated music at runtime. We conducted A/B tests comparing static music, both composed and computer-generated, to dynamically adapting music. We find that AUD.js provides reasonably effective music for games, but that adaptiveness of the music does not necessarily improve player experience over composed music. By conducting a user study during Global Game Jam 2014, we also find that since AUD.js provides a software solution to music composition, it can be a useful tool for game music integration under time pressure.

This thesis presents a novel quantum algorithm that solves the Chromatic Number problem. Complexity analysis of this algorithm revealed a run time of O(2n/2n2(log2n)2). This is an improvement over the best known algorithm, with a run time of 2nnO(1) [1]. This algorithm uses the Quantum Search algorithm (often called Grover's Algorithm), and the Quantum Counting algorithm. Chromatic Number is an example of an NP-Hard problem, which suggests that other NP-Hard problems can also benefit from a speed-up provided by quantum technology. This has wide implications as many real world problems can be framed as NP-Hard problems, so any speed-up in the solution of these problems is highly sought after. A bulk of this thesis consists of a review of the underlying principles of quantum mechanics and quantum computing, building to the Quantum Search and Quantum Counting algorithms. The review is written with the assumption that the reader has no prior knowledge on quantum computing. This culminates with a presentation of algorithms for generating the quantum circuits required to solve K-Coloring and Chromatic Number.

Using deep learning to synthetically generate music is a research domain that has gained more attention from the public in the past few years. A subproblem of music generation is music extension, or the task of taking existing music and extending it. This work proposes the Continuer Pipeline, a novel technique that uses deep learning to take music and extend it in 5 second increments. It does this by treating the musical generation process as an image generation problem; we utilize latent diffusion models (LDMs) to generate spectrograms, which are image representations of music. The Continuer Pipeline is able to receive a waveform as an input, and its output will be what the pipeline predicts the next five seconds might sound like. We trained the Continuer Pipeline using the expansive diffusion model functionality provided by the HuggingFace platform, and our dataset consisted of 256x256 spectrogram images representing 5-second snippets of various hip-hop songs from Spotify. The musical waveforms generated by the Continuer Pipeline are currently at a much lower quality compared to human-generated music, but we affirm that the Continuer Pipeline still has many uses in its current state, and we describe many avenues for future improvement to this technology.