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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Cognitive computing: algorithm design in the intersection of cognitive science and emerging computer architectures

Chandler, Benjamin 22 January 2016 (has links)
For the first time in decades computers are evolving into a fundamentally new class of machine. Transistors are still getting smaller, more economical, and more power-efficient, but operating frequencies leveled off in the mid-2000's. Today, improving performance requires placing a larger number of slower processing cores on each of many chips. Software written for such machines must scale out over many cores rather than scaling up with a faster single core. Biological computation is an extreme manifestation of such a many-slow-core architecture and therefore offers a potential source of ideas for leveraging new hardware. This dissertation addresses several problems in the intersection of emerging computer architectures and biological computation, termed Cognitive Computing: What mechanisms are necessary to maintain stable representations in a large distributed learning system? How should complex biologically-inspired algorithms be tested? How do visual sensing limitations like occlusion influence performance of classification algorithms? Neurons have a limited dynamic output range, but must process real-world signals over a wide dynamic range without saturating or succumbing to endogenous noise. Many existing neural network models leverage spatial competition to address this issue, but require hand-tuning of several parameters for a specific, fixed distribution of inputs. Integrating spatial competition with a stabilizing learning process produces a neural network model capable of autonomously adapting to a non-stationary distribution of inputs. Human-engineered complex systems typically include a number of architectural features to curtail complexity and simplify testing. Biological systems do not obey these constraints. Biologically-inspired algorithms are thus dramatically more difficult to engineer. Augmenting standard tools from the software engineering community with features targeted towards biologically-inspired systems is an effective mitigation. Natural visual environments contain objects that are occluded by other objects. Such occlusions are under-represented in the standard benchmark datasets for testing classification algorithms. This bias masks the negative effect of occlusion on performance. Correcting the bias with a new dataset demonstrates that occlusion is a dominant variable in classification performance. Modifying a state-of-the-art algorithm with mechanisms for occlusion resistance doubles classification performance in high-occlusion cases without penalty for unoccluded objects.

Cognitive Computing for Decision Support

January 2020 (has links)
abstract: The Cognitive Decision Support (CDS) model is proposed. The model is widely applicable and scales to realistic, complex decision problems based on adaptive learning. The utility of a decision is discussed and four types of decisions associated with CDS model are identified. The CDS model is designed to learn decision utilities. Data enrichment is introduced to promote the effectiveness of learning. Grouping is introduced for large-scale decision learning. Introspection and adjustment are presented for adaptive learning. Triage recommendation is incorporated to indicate the trustworthiness of suggested decisions. The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management. Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning. A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

Convolutional Neural Networks for Hurricane Road Closure Probability and Tree Debris Estimation

Unknown Date (has links)
Hurricanes cause significant property loss every year. A substantial part of that loss is due to the trees destroyed by the wind, which in turn block the roads and produce a large amount of debris. The debris not only can cause damage to nearby properties, but also needs to be cleaned after the hurricane. Neural Networks grown significantly as a field over the last year finding a lot of applications in many disciplines like computer science, medicine, banking, physics, and engineering. In this thesis, a new method is proposed to estimate the tree debris due to high winds using the Convolutional Neural Networks (CNNs). For the purposes of this thesis the tree satellite image dataset was created which then was used to train two networks CNN-I and CNN-II for tree recognition and tree species recognition, respectively. Satellite images were used as the input for the CNNs to recognize the locations and types of the trees that can produce the debris. The tree images selected by CNN were used to approximate the tree parameters that were later used to calculate the tree failure density function often called fragility function (at least one failure in the time period) for each recognized tree. The tree failure density functions were used to compose the probability of road closure due to hurricane winds and overall amount of the tree debris. The proposed approach utilizes the current trends in Neural Networks and is easily applicable, such that can help cities and state authorities to better plan for the adverse consequences of tree failures due to hurricane winds. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / 2019 / September 27, 2019. / Convolutional Neuron Networks, Fragility, Hurricane, Tree debris, Wind / Includes bibliographical references. / Xiuwen Liu, Professor Co-Directing Thesis; Sungmoon Jung, Professor Co-Directing Thesis; Peixiang Zhao, Committee Member; Shayok Chakraborty, Committee Member.

Topics of deep learning in security and compression

Wang, Xiao 22 January 2021 (has links)
This thesis covers topics at the intersection of deep learning (DL), security and compression. These topics include the issues of security and compression of DL models themselves, as well as their applications in the fields of cyber security and data compression. The first part of the thesis focuses on the security problems of DL. Recent studies have revealed the vulnerability of DL under several malicious attacks such as adversarial attacks, where the output of a DL model is manipulated through an invisibly small perturbation of the model's input. We propose to defend against these threats by incorporating stochasticity into DL models. Multiple randomization schemes are introduced including Defensive Dropout (DD), Hierarchical Random Switching (HRS) and Adversarially Trained Model Switching (AdvMS). The next part of the thesis discusses the usage of DL in security domain. In particular, we consider anomaly detection problems in an unsupervised learning setting using auto-encoders and apply this method to both side-channel signals and proxy logs. In the third part we discuss the interaction between DL and Compressed Sensing (CS). In CS systems, the processing time is largely limited by the computational cost of sparse reconstruction. We show that full reconstruction can be bypassed by training deep networks that extract information directly from the compressed signals. From another perspective, CS also help reducing the complexity of DL models by providing a more compact data representation. The last topic is DL based codecs for image compression. As an extension to the current framework, we propose Substitutional Neural Image Compression (SNIC) that finds the optimal input substitute for a specific compression target. SNIC leads to both improved rate-distortion trade-off and easier bit-rate control.

Super-resolution for Natural Images and Magnetic Resonance Images

January 2020 (has links)
abstract: Image super-resolution (SR) is a low-level image processing task, which has manyapplications such as medical imaging, satellite image processing, and video enhancement, etc. Given a low resolution image, it aims to reconstruct a high resolution image. The problem is ill-posed since there can be more than one high resolution image corresponding to the same low-resolution image. To address this problem, a number of machine learning-based approaches have been proposed. In this dissertation, I present my works on single image super-resolution (SISR) and accelerated magnetic resonance imaging (MRI) (a.k.a. super-resolution on MR images), followed by the investigation on transfer learning for accelerated MRI reconstruction. For the SISR, a dictionary-based approach and two reconstruction based approaches are presented. To be precise, a convex dictionary learning (CDL) algorithm is proposed by constraining the dictionary atoms to be formed by nonnegative linear combination of the training data, which is a natural, desired property. Also, two reconstruction-based single methods are presented, which make use of (i)the joint regularization, where a group-residual-based regularization (GRR) and a ridge-regression-based regularization (3R) are combined; (ii)the collaborative representation and non-local self-similarity. After that, two deep learning approaches are proposed, aiming at reconstructing high-quality images from accelerated MRI acquisition. Residual Dense Block (RDB) and feedback connection are introduced in the proposed models. In the last chapter, the feasibility of transfer learning for accelerated MRI reconstruction is discussed. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

Exploiting structure in coordinating multiple decision makers

Mostafa, Hala 01 January 2011 (has links)
This thesis is concerned with sequential decision making by multiple agents, whether they are acting cooperatively to maximize team reward or selfishly trying to maximize their individual rewards. The practical intractability of this general problem led to efforts in identifying special cases that admit efficient computation, yet still represent a wide enough range of problems. In our work, we identify the class of problems with structured interactions, where actions of one agent can have non-local effects on the transitions and/or rewards of another agent. We addressed the following research questions: (1) How can we compactly represent this class of problems? (2) How can we efficiently calculate agent policies that maximize team reward (for cooperative agents) or achieve equilibrium (self-interested agents)? (3) How can we exploit structured interactions to make reasoning about communication offline tractable? For representing our class of problems, we developed a new decision-theoretic model, Event-Driven Interactions with Complex Rewards (EDI-CR), that explicitly represents structured interactions. EDI-CR is a compact yet general representation capable of capturing problems where the degree of coupling among agents ranges from complete independence to complete dependence. For calculating agent policies, we draw on several techniques from the field of mathematical optimization and adapt them to exploit the special structure in EDI-CR. We developed a Mixed Integer Linear Program formulation of EDI-CR with cooperative agents that results in programs much more compact and faster to solve than formulations ignoring structure. We also investigated the use of homotopy methods as an optimization technique, as well as formulation of self-interested EDI-CR as a system of non-linear equations. We looked at the issue of communication in both cooperative and self-interested settings. For the cooperative setting, we developed heuristics that assess the impact of potential communication points and add the ones with highest impact to the agents’ decision problems. Our heuristics successfully pick communication points that improve team reward while keeping problem size manageable. Also, by controlling the amount of communication introduced by a heuristic, our approach allows us to control the tradeoff between solution quality and problem size. For self-interested agents, we look at an example setting where communication is an integral part of problem solving, but where the self-interested agents have a reason to be reticent (e.g. privacy concerns). We formulate this problem as a game of incomplete information and present a general algorithm for calculating approximate equilibrium profile in this class of games.

A diagnosis system using a task integrated problem solver architecture (TIPS), including causal reasoning /

Punch, William F. January 1989 (has links)
No description available.

Explaining knowledge systems : justifying diagnostic conclusions /

Tanner, Michael Clay January 1989 (has links)
No description available.

Machine understanding of devices causal explanation of diagnostic conclusions /

Keuneke, Anne Marie January 1989 (has links)
No description available.

An extensible, task-specific shell for routine design problem solving /

Herman, David Joseph January 1992 (has links)
No description available.

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