Physical Sciences and Mathematics Commons^™

Towards Intelligent Runtime Framework For Distributed Heterogeneous Systems, Polykarpos Thomadakis

Computer Science Theses & Dissertations

Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and …

Inverse Mappers For Qcd Global Analysis, Manal Almaeen

Computer Science Theses & Dissertations

Inverse problems – using measured observations to determine unknown parameters – are well motivated but challenging in many scientific problems. Mapping parameters to observables is a well-posed problem with unique solutions, and therefore can be solved with differential equations or linear algebra solvers. However, the inverse problem requires backward mapping from observable to parameter space, which is often nonunique. Consequently, solving inverse problems is ill-posed and a far more challenging computational problem.

Our motivated application in this dissertation is the inverse problems in nuclear physics that characterize the internal structure of the hadrons. We first present a machine learning framework …

Towards Privacy And Security Concerns Of Adversarial Examples In Deep Hashing Image Retrieval, Yanru Xiao

Computer Science Theses & Dissertations

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications.

In this dissertation, we …

Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan

Computer Science Theses & Dissertations

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …

Using Ensemble Learning Techniques To Solve The Blind Drift Calibration Problem, Devin Scott Drake

Computer Science Theses & Dissertations

Large sets of sensors deployed in nearly every practical environment are prone to drifting out of calibration. This drift can be sensor-based, with one or several sensors falling out of calibration, or system-wide, with changes to the physical system causing sensor-reading issues. Recalibrating sensors in either case can be both time and cost prohibitive. Ideally, some technique could be employed between the sensors and the final reading that recovers the drift-free sensor readings. This paper covers the employment of two ensemble learning techniques — stacking and bootstrap aggregation (or bagging) — to recover drift-free sensor readings from a suite of …

Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones

Computer Science Theses & Dissertations

Collections are the tools that people use to make sense of an ever-increasing number of archived web pages. As collections themselves grow, we need tools to make sense of them. Tools that work on the general web, like search engines, are not a good fit for these collections because search engines do not currently represent multiple document versions well. Web archive collections are vast, some containing hundreds of thousands of documents. Thousands of collections exist, many of which cover the same topic. Few collections include standardized metadata. Too many documents from too many collections with insufficient metadata makes collection understanding …

Autonomous Robot Navigation In Unknown Terrains Using Parallel Numerical Artificial Potential Fields, John C. Schneider

Computer Science Theses & Dissertations

We present a new artificial potential field formulation for resolution complete robot navigation that unifies the purely geometric path planning problem with the lower level force control problem. Our formulation is designed for numerical computation over a massively parallel mesh of processors and is responsive to newly discovered terrain features. It does not suffer from many of the problems commonly associated with potential fields and with adequate resolution provides provably correct, collision free convergence to the goal. In addition, our formulation supports many desirable, practical features required for implementation, such as bounded actuator torques, attainable incremental constructability, realizable computation and …

Real Time Texture Analysis From The Parallel Computation Of Fractal Dimension, Halford I. Hayes Jr.

Computer Science Theses & Dissertations

The discrimination of texture features in an image has many important applications: from detection of man-made objects from a surrounding natural background to identification of cancerous from healthy tissue in X-ray imagery. The fractal structure in an image has been used with success to identify these features but requires unacceptable processing time if executed sequentially.

The paradigm of data parallelism is presented as the best method for applying massively parallel processing to the computation of fractal dimension of an image. With this methodology, and sufficient numbers of processors, this computation can reach real time speeds necessary for many applications. A …

Monitoring Computer Systems: An Intelligent Approach, Myron Zhihong Xu

Computer Science Theses & Dissertations

Monitoring modern computer systems is increasingly difficult due to their peculiar characteristics. To cope with this situation, the dissertation develops an approach to intelligent monitoring. The resulting model consists of three major designs: representing targets, controlling data collection, and autonomously refining monitoring performance. The model explores a more declarative object-oriented model by introducing virtual objects to dynamically compose abstract representations, while it treats conventional hard-wired hierarchies and predefined object classes as primitive structures. Taking the representational framework as a reasoning bed, the design for controlling mechanisms adopts default reasoning backed up with ordered constraints, so that the amount of data …

Reasoning By Analogy In A Multi-Level System Architecture For The Design Of Mechanisms, Ghassan F. Issa

Computer Science Theses & Dissertations

Since the first attempts to integrate AI technology and engineering design nearly two decades ago, few expert systems have been shown to demonstrate sufficient reasoning capabilities to solve real-world design problems. The complex nature of design, the lack of understanding of the design process, and the limitations of current expert system technology have all been shown to have adverse effects on the maturity of this research area. Therefore, our direction in this research concentrates on understanding the design process, investigating a novel area of research focusing on creative design, and incorporating the results into a system model feasible for production …

Effectiveness Analysis Of Knowledge Bases, Shensheng Zhao

Computer Science Theses & Dissertations

Knowledge base systems (expert systems) are entering a critical stage as interest spreads from university research to practical applications. If knowledge base systems are to withstand this transition, special attention must be paid to checking their effectiveness. The issue of effectiveness analysis of knowledge base systems has been largely ignored and few works have been published in this field. This dissertation shows how the effectiveness of a knowledge base system can be defined, discussed and analyzed at the knowledge base system level and the knowledge base level. We characterize the effectiveness of a knowledge base system in terms of minimality, …

Integration Of Abductive And Deductive Inference Diagnosis Model And Its Application In Intelligent Tutoring System, Jingying Zhang

Computer Science Theses & Dissertations

This dissertation presents a diagnosis model, Integration of Abductive and Deductive Inference diagnosis model (IADI), in the light of the cognitive processes of human diagnosticians. In contrast with other diagnosis models, that are based on enumerating, tracking and classifying approaches, the IADI diagnosis model relies on different inferences to solve the diagnosis problems. Studies on a human diagnosticians' process show that a diagnosis process actually is a hypothesizing process followed by a verification process. The IADI diagnosis model integrates abduction and deduction to simulate these processes. The abductive inference captures the plausible features of this hypothesizing process while the deductive …

A Classification Approach For Automated Reasoning Systems--A Case Study In Graph Theory, Rong Lin

Computer Science Theses & Dissertations

Reasoning systems which create classifications of structured objects face the problem of how object descriptions can be used to reflect their components as well as relations among these components. Current reasoning systems on graph theory do not adequately provide models to discover complex relations among mathematical concepts (eg: relations involving subgraphs) mainly due to the inability to solve this problem. This thesis presents an approach to construct a knowledge-based system, GC (Graph Classification), which overcomes this difficulty in performing automated reasoning in graph theory. We describe graph concepts based on an attribute called Linear Recursive Constructivity (LRC). LRC defines classes …