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A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki
A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki
Electronic Theses and Dissertations
Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively …
Authorship Identification Of Translation Algorithms., Keishin Nishiyama
Authorship Identification Of Translation Algorithms., Keishin Nishiyama
Electronic Theses and Dissertations
Authorship analysis is a process of identifying a true writer of a given document and has been studied for decades. However, only a handful of studies of authorship analysis of translators are available despite the fact that online translations are widely available and also popularly employed in automatic translations of posts in social networking services. The identification of translation algorithms has potential to contribute to the investigation of cybercrimes, involving translation of scam messages by algorithmic translations to reach speakers of foreign languages. This study tested bag of words (BOW) approach in authorship attribution and the existing approaches to translator …
Using Hydroacoustics To Investigate Biological Responses In Fish Abundance To Restoration Efforts In The Penobscot River, Maine, Constantin C. Scherelis
Using Hydroacoustics To Investigate Biological Responses In Fish Abundance To Restoration Efforts In The Penobscot River, Maine, Constantin C. Scherelis
Electronic Theses and Dissertations
Spatiotemporal advantages linked to hydroacoustic sampling techniques have caused a surge in the use of these techniques for fisheries monitoring studies applied over long periods of time in marine systems. Dynamic physical conditions such as tidal height, boat traffic, floating debris, and suspended particle concentrations result in unwanted noise signatures that vary in intensity and location within a hydroacoustic beam over time and can be mixed with the acoustic returns from intended targets (e.g., fish). Typical processing filters applied over long term datasets to minimize noise and maximize signals do not address spatiotemporal fluctuations of noise in dynamic systems. We …
Accurate And Justifiable : New Algorithms For Explainable Recommendations., Behnoush Abdollahi
Accurate And Justifiable : New Algorithms For Explainable Recommendations., Behnoush Abdollahi
Electronic Theses and Dissertations
Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user's explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms …
A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener
A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener
Electronic Theses and Dissertations
This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, …
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Electronic Theses and Dissertations
While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …
Vertex Weighted Spectral Clustering, Mohammad Masum
Vertex Weighted Spectral Clustering, Mohammad Masum
Electronic Theses and Dissertations
Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to …
An Investigation Into The Performance Evaluation Of Connected Vehicle Applications: From Real-World Experiment To Parallel Simulation Paradigm, Md Salman Ahmed
An Investigation Into The Performance Evaluation Of Connected Vehicle Applications: From Real-World Experiment To Parallel Simulation Paradigm, Md Salman Ahmed
Electronic Theses and Dissertations
A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than …
Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami
Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami
Electronic Theses and Dissertations
This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate …
Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani
Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani
Electronic Theses and Dissertations
This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.
Weakly Labeled Action Recognition And Detection, Waqas Sultani
Weakly Labeled Action Recognition And Detection, Waqas Sultani
Electronic Theses and Dissertations
Research in human action recognition strives to develop increasingly generalized methods that are robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendous strides in improving recognition accuracy on ever larger and complex benchmark datasets, comprising realistic actions "in the wild" videos. Unfortunately, the all-encompassing, dense, global representations that bring about such improvements often benefit from the inherent characteristics, specific to datasets and classes, that do not necessarily reflect knowledge about the entity to be recognized. This results in specific models that perform well within datasets but generalize poorly. Furthermore, training of supervised action recognition and detection methods …
Designing Light Filters To Detect Skin Using A Low-Powered Sensor, Muhammad Uzair Tariq
Designing Light Filters To Detect Skin Using A Low-Powered Sensor, Muhammad Uzair Tariq
Electronic Theses and Dissertations
Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions …
Exploring The Multi-Touch Interaction Design Space For 3d Virtual Objects To Support Procedural Training Tasks, Sarah Holderness
Exploring The Multi-Touch Interaction Design Space For 3d Virtual Objects To Support Procedural Training Tasks, Sarah Holderness
Electronic Theses and Dissertations
Multi-touch interaction has the potential to be an important input method for realistic training in 3D environments. However, multi-touch interaction has not been explored much in 3D tasks, especially when trying to leverage realistic, real-world interaction paradigms. A systematic inquiry into what realistic gestures look like for 3D environments is required to understand how users translate real-world motions to multi-touch motions. Once those gestures are defined, it is important to see how we can leverage those gestures to enhance training tasks. In order to explore the interaction design space for 3D virtual objects, we began by conducting our first study …
Pervasive Secure Content Delivery Networks Implementation, Hector Lugo-Cordero
Pervasive Secure Content Delivery Networks Implementation, Hector Lugo-Cordero
Electronic Theses and Dissertations
Over the years, communication networks have been shifting their focus from providing connectivity in a client/server model to providing a service or content. This shift has led to topic areas like Service-Oriented Architecture (SOA), Heterogeneous Wireless Mesh Networks, and Ubiquitous Computing. Furthermore, probably the broadest of these areas which embarks all is the Internet of Things (IoT). The IoT is defined as an Internet where all physical entities (e.g., vehicles, appliances, smart phones, smart homes, computers, etc.), which we interact daily are connected and exchanging data among themselves and users. The IoT has become a global goal for companies, researchers, …
Jml Template Generation, Kushal Raghav Poojari
Jml Template Generation, Kushal Raghav Poojari
Electronic Theses and Dissertations
The Java Modeling Language (JML) is a behavioral interface specific language designed to specify Java modules (which are Java classes and interfaces). Specifications are used to describe the intended functionality without considering the way it is implemented. In JML, if a user wants to write specifications for a Java file, he or she must undertake several steps. To help automate the process of creating annotations for method specifications, a tool Jmlspec was created. Jmlspec generated a file that refines the source file and has empty placeholders in which one can write specifications. Although Jmlspec worked with older versions of Java, …
On The Security Of Nosql Cloud Database Services, Mohammad Ahmadian
On The Security Of Nosql Cloud Database Services, Mohammad Ahmadian
Electronic Theses and Dissertations
Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a …
Resource Allocation And Pricing In Secondary Dynamic Spectrum Access Networks, Enas Khairullah
Resource Allocation And Pricing In Secondary Dynamic Spectrum Access Networks, Enas Khairullah
Electronic Theses and Dissertations
The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there …
Learning Robotic Manipulation From User Demonstrations, Rouhollah Rahmatizadeh
Learning Robotic Manipulation From User Demonstrations, Rouhollah Rahmatizadeh
Electronic Theses and Dissertations
Personal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to execute tasks as demonstrated by the user. First, we describe a learning from demonstration (LfD) method that learns assistive tasks that need to be …
Visual Saliency Detection And Semantic Segmentation, Nasim Souly
Visual Saliency Detection And Semantic Segmentation, Nasim Souly
Electronic Theses and Dissertations
Visual saliency is the ability to select the most relevant data in the scene and reduce the amount of data that needs to be processed. We propose a novel unsupervised approach to detect visual saliency in videos. For this, we employ a hierarchical segmentation technique to obtain supervoxels of a video, and simultaneously, we build a dictionary from cuboids of the video. Then we create a feature matrix from coefficients of dictionary elements. Next, we decompose this matrix into sparse and redundant parts and obtain salient regions using group lasso. Our experiments provide promising results in terms of predicting eye …
Online, Supervised And Unsupervised Action Localization In Videos, Khurram Soomro
Online, Supervised And Unsupervised Action Localization In Videos, Khurram Soomro
Electronic Theses and Dissertations
Action recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window techniques. The first part of this dissertation presents an efficient approach for localizing actions by learning contextual relations between different video regions in training. In testing, …
Adversarial Attacks On Vision Algorithms Using Deep Learning Features, Andy Michel
Adversarial Attacks On Vision Algorithms Using Deep Learning Features, Andy Michel
Electronic Theses and Dissertations
Computer vision algorithms, such as those implementing object detection, are known to be susceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause vision algorithms to incorrectly classify inputs that they would have otherwise classified correctly. A number of approaches have been recently investigated to generate such adversarial examples for deep neural networks. Many of these approaches either require grey-box access to the deep neural net being attacked or rely on adversarial transfer and grey-box access to a surrogate neural network. In this thesis, we present an approach to the synthesis of adversarial examples for computer vision …
Novel Computational Methods For Integrated Circuit Reverse Engineering, Travis Meade
Novel Computational Methods For Integrated Circuit Reverse Engineering, Travis Meade
Electronic Theses and Dissertations
Production of Integrated Circuits (ICs) has been largely strengthened by globalization. System-on-chip providers are capable of utilizing many different providers which can be responsible for a single task. This horizontal structure drastically improves to time-to-market and reduces manufacturing cost. However, untrust of oversea foundries threatens to dismantle the complex economic model currently in place. Many Intellectual Property (IP) consumers become concerned over what potentially malicious or unspecified logic might reside within their application. This logic which is inserted with the intention of causing harm to a consumer has been referred to as a Hardware Trojan (HT). To help IP consumers, …
Network Partitioning In Distributed Agent-Based Models, Antoniya Petkova
Network Partitioning In Distributed Agent-Based Models, Antoniya Petkova
Electronic Theses and Dissertations
Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical. The real-time requirement necessitates the use of in-memory computing, as it is difficult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use …
Super Resolution Of Wavelet-Encoded Images And Videos, Vildan Atalay
Super Resolution Of Wavelet-Encoded Images And Videos, Vildan Atalay
Electronic Theses and Dissertations
In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, …
Adaptive Audio Classification Framework For In-Vehicle Environment With Dynamic Noise Characteristics, Haitham Alsaadan
Adaptive Audio Classification Framework For In-Vehicle Environment With Dynamic Noise Characteristics, Haitham Alsaadan
Electronic Theses and Dissertations
With ever-increasing number of car-mounted electric devices that are accessed, managed, and controlled with smartphones, car apps are becoming an important part of the automotive industry. Audio classification is one of the key components of car apps as a front-end technology to enable human-app interactions. Existing approaches for audio classification, however, fall short as the unique and time-varying audio characteristics of car environments are not appropriately taken into account. Leveraging recent advances in mobile sensing technology that allows for an active and accurate driving environment detection, in this thesis, we develop an audio classification framework for mobile apps that categorizes …
Rationalizing The Band Gap Tunability Of Semiconductors Via Electronic Structure Calculations, Matthew N. Srnec
Rationalizing The Band Gap Tunability Of Semiconductors Via Electronic Structure Calculations, Matthew N. Srnec
Electronic Theses and Dissertations
The polymorphs of titanium dioxide and various diamond-like semiconductor materials are promising candidates in photovoltaic solar cell applications. Several of these polymorphs have been studied with experimental and computational methods, which often aim at tuning the electronic structure, particularly the band gap value of the crystalline solid. Prior studies report that the addition of a substituent into the structure of titanium dioxide decreases its band gap value, but the reasons for this are unknown. Possible explanations for the change in band gap involve the substituent atom's crystal radius, electronegativity, and ionization energy. Understanding the cause of these changes will provide …
Reasoning About Frame Properties In Object-Oriented Programs, Yuyan Bao
Reasoning About Frame Properties In Object-Oriented Programs, Yuyan Bao
Electronic Theses and Dissertations
Framing is important for specification and verification of object-oriented programs. This dissertation develops the local reasoning approach for framing in the presence of data structures with unrestricted sharing and subtyping. It can verify shared data structures specified in a concise way by unifying fine-grained region logic and separation logic. Then the fine-grained region logic is extended to reason about subtyping. First, fine-grained region logic is adapted from region logic to express regions at the granularity of individual fields. Conditional region expressions are introduced; not only does this allow one to specify more precise frame conditions, it also has the ability …
Learning Dynamic Network Models For Complex Social Systems, Alireza Hajibagheri
Learning Dynamic Network Models For Complex Social Systems, Alireza Hajibagheri
Electronic Theses and Dissertations
Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network. To study …
Complex Affect Recognition In The Wild, Behnaz Nojavanasghari
Complex Affect Recognition In The Wild, Behnaz Nojavanasghari
Electronic Theses and Dissertations
Artificial social intelligence is a step towards human-like human-computer interaction. One important milestone towards building socially intelligent systems is enabling computers with the ability to process and interpret the social signals of humans in the real world. Social signals include a wide range of emotional responses from a simple smile to expressions of complex affects. This dissertation revolves around computational models for social signal processing in the wild, using multimodal signals with an emphasis on the visual modality. We primarily focus on complex affect recognition with a strong interest in curiosity. In this dissertation,we ?rst present our collected dataset, EmoReact. …
Smart Image Search System Using Personalized Semantic Search Method, Fangyu Zhang
Smart Image Search System Using Personalized Semantic Search Method, Fangyu Zhang
Electronic Theses and Dissertations
Due to the emerge in huge numbers of information on the internet nowadays, search technologies are widely used in various fields. Achieving the most relevant search result for the users becomes a big challenge now. While the traditional semantic search technologies seem to achieve the most relevant search result, however, it faces two problems: one is the one-size-fits-all problem, and another is low efficiency. The purpose of this research is to build a Smart Image Search System by using the personalized semantic search method to solve those problems. The personalized semantic search method makes the search system avoids the one-size-fits-all …