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Articles 211 - 240 of 490
Full-Text Articles in Computer Sciences
Correctness And Progress Verification Of Non-Blocking Programs, Christina Peterson
Correctness And Progress Verification Of Non-Blocking Programs, Christina Peterson
Electronic Theses and Dissertations
The progression of multi-core processors has inspired the development of concurrency libraries that guarantee safety and liveness properties of multiprocessor applications. The difficulty of reasoning about safety and liveness properties in a concurrent environment has led to the development of tools to verify that a concurrent data structure meets a correctness condition or progress guarantee. However, these tools possess shortcomings regarding the ability to verify a composition of data structure operations. Additionally, verification techniques for transactional memory evaluate correctness based on low-level read/write histories, which is not applicable to transactional data structures that use a high-level semantic conflict detection. In …
Learning Internal State Memory Representations From Observation, Josiah Wong
Learning Internal State Memory Representations From Observation, Josiah Wong
Electronic Theses and Dissertations
Learning from Observation (LfO) is a machine learning paradigm that mimics how people learn in daily life: learning how to do something simply by watching someone else do it. LfO has been used in various applications, from video game agent creation to driving a car, but it has always been limited by the inability of an observer to know what a performing entity chooses to remember as they act in an environment. Various methods have either ignored the effects of memory or otherwise made simplistic assumptions about its structure. In this dissertation, we propose a new method, Memory Composition Learning, …
Optimization Algorithms For Deep Learning Based Medical Image Segmentations, Aliasghar Mortazi
Optimization Algorithms For Deep Learning Based Medical Image Segmentations, Aliasghar Mortazi
Electronic Theses and Dissertations
Medical image segmentation is one of the fundamental processes to understand and assess the functionality of different organs and tissues as well as quantifying diseases and helping treatment planning. With ever increasing number of medical scans, the automated, accurate, and efficient medical image segmentation is as unmet need for improving healthcare. Recently, deep learning has emerged as one the most powerful methods for almost all image analysis tasks such as segmentation, detection, and classification and so in medical imaging. In this regard, this dissertation introduces new algorithms to perform medical image segmentation for different (a) imaging modalities, (b) number of …
Automated Synthesis Of Memristor Crossbar Networks, Dwaipayan Chakraborty
Automated Synthesis Of Memristor Crossbar Networks, Dwaipayan Chakraborty
Electronic Theses and Dissertations
The advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore's law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational sciences as newer devices pave the way for novel approaches to computing. One of such devices is the memristor -- a resistor with non-volatile memory. Memristors can …
Utilizing Edge In Iot And Video Streaming Applications To Reduce Bottlenecks In Internet Traffic, Kutalmis Akpinar
Utilizing Edge In Iot And Video Streaming Applications To Reduce Bottlenecks In Internet Traffic, Kutalmis Akpinar
Electronic Theses and Dissertations
There is a large increase in the surge of data over Internet due to the increasing demand on multimedia content. It is estimated that 80% of Internet traffic will be video by 2022, according to a recent study. At the same time, IoT devices on Internet will double the human population. While infrastructure standards on IoT are still nonexistent, enterprise solutions tend to encourage cloud-based solutions, causing an additional surge of data over the Internet. This study proposes solutions to bring video traffic and IoT computation back to the edges of the network, so that costly Internet infrastructure upgrades are …
A Study Of Perceptions On Incident Response Exercises, Information Sharing, Situational Awareness, And Incident Response Planning In Power Grid Utilities, Joseph Garmon
Electronic Theses and Dissertations
The power grid is facing increasing risks from a cybersecurity attack. Attacks that shut off electricity in Ukraine have already occurred, and successful compromises of the power grid that did not shut off electricity to customers have been privately disclosed in North America. The objective of this study is to identify how perceptions of various factors emphasized in the electric sector affect incident response planning. Methods used include a survey of 229 power grid personnel and the use of partial least squares structural equation modeling to identify causal relationships. This study reveals the relationships between perceptions by personnel responsible for …
Reinforcement Learning For Optimal Control Of Network Epidemic Processes, Alec H. Kerrigan
Reinforcement Learning For Optimal Control Of Network Epidemic Processes, Alec H. Kerrigan
Honors Undergraduate Theses
Our society is increasingly interconnected, making it easy for cascades/epidemic (diseases, disinformation etc). Current epidemic control efforts are based on approximate network epidemic models, which often ignore the unique complexity and rich information embedded in the complex interconnections of real-world networks/populations.Deep reinforcement learning (RL) is a powerful tool at learning policies for these nonlinear, complex processes in high-dimension. To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model, we design a RL framework with a custom reward structure using the node2vec embedding technique. Results indicate deep RL is able to determine and converge on an optimal intervention policy in …
Visual-Textual Video Synopsis Generation, Aidean Sharghi Karganroodi
Visual-Textual Video Synopsis Generation, Aidean Sharghi Karganroodi
Electronic Theses and Dissertations
In this dissertation we tackle the problem of automatic video summarization. Automatic summarization techniques enable faster browsing and indexing of large video databases. However, due to the inherent subjectivity of the task, no single video summarizer fits all users unless it adapts to individual user's needs. To address this issue, we introduce a fresh view on the task called "Query-focused'' extractive video summarization. We develop a supervised model that takes as input a video and user's preference in form of a query, and creates a summary video by selecting key shots from the original video. We model the problem as …
Towards More Reliable Neural Network Learning Models, Navid Kardan
Towards More Reliable Neural Network Learning Models, Navid Kardan
Electronic Theses and Dissertations
Ideally, when a neural network makes a wrong decision or encounters an out-of-distribution example, its predictive confidence should be as low as possible. Three primary contributions in this dissertation address this challenge. The first two contributions are new approaches to mitigate overconfident predictions in modern neural networks. In the first (1), called competitive overcomplete output layer neural networks, several classifiers, as part of the same output layer, are trained simultaneously and later their consensus produces more reliable predictions. The second approach (2) reformulates the original classification problem into several new versions by combining classes together and training a classifier on …
Training Neural Networks Through The Integration Of Evolution And Gradient Descent, Gregory Morse
Training Neural Networks Through The Integration Of Evolution And Gradient Descent, Gregory Morse
Electronic Theses and Dissertations
Neural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past decade, little work has been invested in developing algorithms that incorporate the advances from both deep learning and neuroevolution. This dissertation introduces two new algorithms that are steps towards the integration of …
Task Focused Robotic Imitation Learning, Pooya Abolghasemi
Task Focused Robotic Imitation Learning, Pooya Abolghasemi
Electronic Theses and Dissertations
For many years, successful applications of robotics were the domain of controlled environments, such as industrial assembly lines. Such environments are custom designed for the convenience of the robot and separated from human operators. In recent years, advances in artificial intelligence, in particular, deep learning and computer vision, allowed researchers to successfully demonstrate robots that operate in unstructured environments and directly interact with humans. One of the major applications of such robots is in assistive robotics. For instance, a wheelchair mounted robotic arm can help disabled users in the performance of activities of daily living (ADLs) such as feeding and …
Multi-Modal Interfaces For Sensemaking Of Graph-Connected Datasets, Anthony Wehrer
Multi-Modal Interfaces For Sensemaking Of Graph-Connected Datasets, Anthony Wehrer
Electronic Theses and Dissertations
The visualization of hypothesized evolutionary processes is often shown through phylogenetic trees. Given evolutionary data presented in one of several widely accepted formats, software exists to render these data into a tree diagram. However, software packages commonly in use by biologists today often do not provide means to dynamically adjust and customize these diagrams for studying new hypothetical relationships, and for illustration and publication purposes. Even where these options are available, there can be a lack of intuitiveness and ease-of-use. The goal of our research is, thus, to investigate more natural and effective means of sensemaking of the data with …
Blockchain-Driven Secure And Transparent Audit Logs, Ashar Ahmad
Blockchain-Driven Secure And Transparent Audit Logs, Ashar Ahmad
Electronic Theses and Dissertations
In enterprise business applications, large volumes of data are generated daily, encoding business logic and transactions. Those applications are governed by various compliance requirements, making it essential to provide audit logs to store, track, and attribute data changes. In traditional audit log systems, logs are collected and stored in a centralized medium, making them prone to various forms of attacks and manipulations, including physical access and remote vulnerability exploitation attacks, and eventually allowing for unauthorized data modification, threatening the guarantees of audit logs. Moreover, such systems, and given their centralized nature, are characterized by a single point of failure. To …
Scalable Network Design And Management With Decentralized Software-Defined Networking, Kuldip Singh Atwal
Scalable Network Design And Management With Decentralized Software-Defined Networking, Kuldip Singh Atwal
Electronic Theses and Dissertations
Network softwarization is among the most significant innovations of computer networks in the last few decades. The lack of uniform and programmable interfaces for network management led to the design of OpenFlow protocol for the university campuses and enterprise networks. This breakthrough coupled with other similar efforts led to an emergence of two complementary but independent paradigms called software-defined networking (SDN) and network function virtualization (NFV). As of this writing, these paradigms are becoming the de-facto norms of wired and wireless networks alike. This dissertation mainly addresses the scalability aspect of SDN for multiple network types. Although centralized control and …
Action Recognition, Temporal Localization And Detection In Trimmed And Untrimmed Video, Rui Hou
Action Recognition, Temporal Localization And Detection In Trimmed And Untrimmed Video, Rui Hou
Electronic Theses and Dissertations
Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature …
Detecting Anomalies From Big Data System Logs, Siyang Lu
Detecting Anomalies From Big Data System Logs, Siyang Lu
Electronic Theses and Dissertations
Nowadays, big data systems (e.g., Hadoop and Spark) are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. A common problem about big data systems is called anomaly, e.g., a status deviated from normal execution, which decreases the performance of computation or kills running programs. It is becoming a necessity to detect anomalies and analyze their causes. An effective and economical approach is to analyze system logs. Big data systems produce numerous unstructured logs that contain buried valuable information. However manually detecting anomalies from system logs is a tedious and daunting …
Multi-Touch Detection And Semantic Response On Non-Parametric Rear-Projection Surfaces, Jason Hochreiter
Multi-Touch Detection And Semantic Response On Non-Parametric Rear-Projection Surfaces, Jason Hochreiter
Electronic Theses and Dissertations
The ability of human beings to physically touch our surroundings has had a profound impact on our daily lives. Young children learn to explore their world by touch; likewise, many simulation and training applications benefit from natural touch interactivity. As a result, modern interfaces supporting touch input are ubiquitous. Typically, such interfaces are implemented on integrated touch-display surfaces with simple geometry that can be mathematically parameterized, such as planar surfaces and spheres; for more complicated non-parametric surfaces, such parameterizations are not available. In this dissertation, we introduce a method for generalizable optical multi-touch detection and semantic response on uninstrumented non-parametric …
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Electronic Theses and Dissertations
In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are "high-risk" Artificial Intelligence …
D-Fens: Dns Filtering & Extraction Network System For Malicious Domain Names, Jeffrey Spaulding
D-Fens: Dns Filtering & Extraction Network System For Malicious Domain Names, Jeffrey Spaulding
Electronic Theses and Dissertations
While the DNS (Domain Name System) has become a cornerstone for the operation of the Internet, it has also fostered creative cases of maliciousness, including phishing, typosquatting, and botnet communication among others. To address this problem, this dissertation focuses on identifying and mitigating such malicious domain names through prior knowledge and machine learning. In the first part of this dissertation, we explore a method of registering domain names with deliberate typographical mistakes (i.e., typosquatting) to masquerade as popular and well-established domain names. To understand the effectiveness of typosquatting, we conducted a user study which helped shed light on which techniques …
Towards High-Performance Big Data Processing Systems, Hong Zhang
Towards High-Performance Big Data Processing Systems, Hong Zhang
Electronic Theses and Dissertations
The amount of generated and stored data has been growing rapidly, It is estimated that 2.5 quintillion bytes of data are generated every day, and 90% of the data in the world today has been created in the last two years. How to solve these big data issues has become a hot topic in both industry and academia. Due to the complex of big data platform, we stratify it into four layers: storage layer, resource management layer, computing layer, and methodology layer. This dissertation proposes brand-new approaches to address the performance of big data platforms like Hadoop and Spark on …
Assessment Of Information Security Culture In Higher Education, Henry Glaspie
Assessment Of Information Security Culture In Higher Education, Henry Glaspie
Electronic Theses and Dissertations
Information security programs are instituted by organizations to provide guidance to their users who handle their data and systems. The main goal of these programs is to protect the organization's information assets through the creation and cultivation of a positive information security culture within the organization. As the collection and use of data expands in all economic sectors, the threat of data breach due to human error increases. Employee's behavior towards information security is influenced by the organizations information security programs and the overall information security culture. This study examines the human factors of an information security program and their …
Trust-Based Rating Prediction And Malicious Profile Detection In Online Social Recommender Systems, Anahita Davoudi
Trust-Based Rating Prediction And Malicious Profile Detection In Online Social Recommender Systems, Anahita Davoudi
Electronic Theses and Dissertations
Online social networks and recommender systems have become an effective channel for influencing millions of users by facilitating exchange and spread of information. This dissertation addresses multiple challenges that are faced by online social recommender systems such as: i) finding the extent of information spread; ii) predicting the rating of a product; and iii) detecting malicious profiles. Most of the research in this area do not capture the social interactions and rely on empirical or statistical approaches without considering the temporal aspects. We capture the temporal spread of information using a probabilistic model and use non-linear differential equations to model …
Personalized Digital Body: Enhancing Body Ownership And Spatial Presence In Virtual Reality, Sungchul Jung
Personalized Digital Body: Enhancing Body Ownership And Spatial Presence In Virtual Reality, Sungchul Jung
Electronic Theses and Dissertations
person's sense of acceptance of a virtual body as his or her own is generally called virtual body ownership (VBOI). Having such a mental model of one's own body transferred to a virtual human surrogate is known to play a critical role in one's sense of presence in a virtual environment. Our focus in this dissertation is on top-down processing based on visual perception in both the visuomotor and the visuotactile domains, using visually personalized body cues. The visual cues we study here range from ones that we refer to as direct and others that we classify as indirect. Direct …
Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery, Senglee Koh
Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery, Senglee Koh
Electronic Theses and Dissertations
State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task …
Machine Learning Methods For Flow Cytometry Analysis And Visualization, Emily Sassano
Machine Learning Methods For Flow Cytometry Analysis And Visualization, Emily Sassano
Electronic Theses and Dissertations
Flow cytometry is a popular analytical cell-biology instrument that uses specific wavelengths of light to profile heterogeneous populations of cells at the individual level. Current cytometers have the capability of analyzing up to 20 parameters on over a million cells, but despite the complexity of these datasets, a typical workflow relies on subjective labor-intensive manual sequential analysis. The research presented in this dissertation provides two machine learning methods to increase the objectivity, efficiency, and discovery in flow cytometry data analysis. The first, a supervised learning method, utilizes previously analyzed data to evaluate new flow cytometry files containing similar parameters. The …
A Value Sensitive Design Approach To Adolescent Mobile Online Safety, Arup Kumar Ghosh
A Value Sensitive Design Approach To Adolescent Mobile Online Safety, Arup Kumar Ghosh
Electronic Theses and Dissertations
With the rise of adolescent smartphone use, concerns about teen online safety are also on the rise. A number of parental control apps are available for mobile devices, but adoption of these apps has been markedly low. To better understand these apps, their users, and design opportunities in the space of mobile online safety for adolescents, we have conducted four studies informed by the principles of Value Sensitive Design (VSD). In Study 1 (Chapter 2), we conducted a web-based survey of 215 parents and their teens (ages 13-17) using two separate logistic regression models (parent and teen) to examine the …
Sampling And Subspace Methods For Learning Sparse Group Structures In Computer Vision, Maryam Jaberi
Sampling And Subspace Methods For Learning Sparse Group Structures In Computer Vision, Maryam Jaberi
Electronic Theses and Dissertations
The unprecedented growth of data in volume and dimension has led to an increased number of computationally-demanding and data-driven decision-making methods in many disciplines, such as computer vision, genomics, finance, etc. Research on big data aims to understand and describe trends in massive volumes of high-dimensional data. High volume and dimension are the determining factors in both computational and time complexity of algorithms. The challenge grows when the data are formed of the union of group-structures of different dimensions embedded in a high-dimensional ambient space. To address the problem of high volume, we propose a sampling method referred to as …
Compiler Design Of A Policy Specification Language For Conditional Gradual Release, Manasa Kashyap Harinath
Compiler Design Of A Policy Specification Language For Conditional Gradual Release, Manasa Kashyap Harinath
Electronic Theses and Dissertations
Securing the confidentiality and integrity of information manipulated by computer software is an old yet increasingly important problem. Current software permission systems present on Android or iOS provide inadequate support for developing applications with secure information flow policies. To be useful, information flow control policies need to specify declassifications and the conditions under which declassification must occur. Having these declassifications scattered all over the program makes policies hard to find, which makes auditing difficult. To overcome these challenges, a policy specification language, 'Evidently' is discussed that allows one to specify information flow control policies separately from the program and which …
Practical Dynamic Transactional Data Structures, Pierre Laborde
Practical Dynamic Transactional Data Structures, Pierre Laborde
Electronic Theses and Dissertations
Multicore programming presents the challenge of synchronizing multiple threads. Traditionally, mutual exclusion locks are used to limit access to a shared resource to a single thread at a time. Whether this lock is applied to an entire data structure, or only a single element, the pitfalls of lock-based programming persist. Deadlock, livelock, starvation, and priority inversion are some of the hazards of lock-based programming that can be avoided by using non-blocking techniques. Non-blocking data structures allow scalable and thread-safe access to shared data by guaranteeing, at least, system-wide progress. In this work, we present the first wait-free hash map which …
Learning Algorithms For Fat Quantification And Tumor Characterization, Sarfaraz Hussein
Learning Algorithms For Fat Quantification And Tumor Characterization, Sarfaraz Hussein
Electronic Theses and Dissertations
Obesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and …