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Articles 151 - 180 of 490

Full-Text Articles in Computer Sciences

On Patching Learning Discrepancies In Neural Network Training, Mohamed Elfeki Jan 2020

On Patching Learning Discrepancies In Neural Network Training, Mohamed Elfeki

Electronic Theses and Dissertations, 2020-2023

Neural network's ability to model data patterns proved to be immensely useful in a plethora of practical applications. However, using the physical world's data can be problematic since it is often cluttered, crowded with scattered insignificant patterns, contain unusual compositions, and widely infiltrated with biases and imbalances. Consequently, training a neural network to find meaningful patterns in seas of chaotic data points becomes virtually as hard as finding a needle in a haystack. Specifically, attempting to simulate real-world multi-modal noisy distributions with high precision leads the network to learn an ill-informed inference distribution. In this work, we discuss four techniques …

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Infrastructure For Performance Monitoring And Analysis Of Systems And Applications, Ramin Izadpanah Jan 2020

Infrastructure For Performance Monitoring And Analysis Of Systems And Applications, Ramin Izadpanah

Electronic Theses and Dissertations, 2020-2023

The growth of High Performance Computer (HPC) systems increases the complexity with respect to understanding resource utilization, system management, and performance issues. HPC performance monitoring tools need to collect information at both the application and system levels to yield a complete performance picture. Existing approaches limit the abilities of the users to do meaningful analysis on actionable timescale. Efficient infrastructures are required to support largescale systems performance data analysis for both run-time troubleshooting and post-run processing modes. In this dissertation, we present methods to fill these gaps in the infrastructure for HPC performance monitoring and analysis. First, we enhance the …

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Algorithms And Applications Of Novel Capsule Networks, Rodney Lalonde Jan 2020

Algorithms And Applications Of Novel Capsule Networks, Rodney Lalonde

Electronic Theses and Dissertations, 2020-2023

Convolutional neural networks, despite their profound impact in countless domains, suffer from significant shortcomings. Linearly-combined scalar feature representations and max pooling operations lead to spatial ambiguities and a lack of robustness to pose variations. Capsule networks can potentially alleviate these issues by storing and routing the pose information of extracted features through their architectures, seeking agreement between the lower-level predictions of higher-level poses at each layer. In this dissertation, we make several key contributions to advance the algorithms of capsule networks in segmentation and classification applications. We create the first ever capsule-based segmentation network in the literature, SegCaps, by introducing …

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Differential Recurrent Neural Networks For Human Activity Recognition, Naifan Zhuang Jan 2020

Differential Recurrent Neural Networks For Human Activity Recognition, Naifan Zhuang

Electronic Theses and Dissertations, 2020-2023

Human activity recognition has been an active research area in recent years. The difficulty of this problem lies in the complex dynamical motion patterns embedded through the sequential frames. The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model various time-series data, where the current hidden state has to be considered in the context of the past hidden states. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient …

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Machine Learning Based Rf Transmitter Characterization In The Presence Of Adversaries, Debashri Roy Jan 2020

Machine Learning Based Rf Transmitter Characterization In The Presence Of Adversaries, Debashri Roy

Electronic Theses and Dissertations, 2020-2023

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the …

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Efficient String Algorithms With Applications In Bioinformatics, Sahar Hooshmand Jan 2020

Efficient String Algorithms With Applications In Bioinformatics, Sahar Hooshmand

Electronic Theses and Dissertations, 2020-2023

The work presented in this dissertation deals with establishing efficient methods for solving some algorithmic problems, which have applications to Bioinformatics. After a short introduction in Chapter 1, an algorithm for genome mappability problem is presented in Chapter 2. Genome mappability is a measure for the approximate repeat structure of the genome with respect to substrings of specific length and a tolerance to define the number of mismatches. The similarity between reads is measured by using the Hamming distance function. Genome mappability is computed for each position in the string and has several applications in designing high-throughput short-read sequencing experiments. …

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Learning Context-Sensitive Human Emotions In Categorical And Dimensional Domains, Pooyan Balouchian Jan 2020

Learning Context-Sensitive Human Emotions In Categorical And Dimensional Domains, Pooyan Balouchian

Electronic Theses and Dissertations, 2020-2023

Still image emotion recognition (ER) has been receiving increasing attention in recent years due to the tremendous amount of social media content on the Web. Many works offer both categorical and dimensional methods to detect image sentiments, while others focus on extracting the true social signals, such as happiness and anger. Deep learning architectures have delivered great suc- cess, however, their dependency on large-scale datasets labeled with (1) emotion, and (2) valence, arousal and dominance, in categorical and dimensional domains respectively, introduce challenges the community tries to tackle. Emotions offer dissimilar semantics when aroused in different con- texts, however "context-sensitive" …

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Improving The Security Of Critical Infrastructure: Metrics, Measurements, And Analysis, Jeman Park Jan 2020

Improving The Security Of Critical Infrastructure: Metrics, Measurements, And Analysis, Jeman Park

Electronic Theses and Dissertations, 2020-2023

In this work, we propose three important contributions needed in the process of improving the security of the critical infrastructure: metrics, measurement, and analysis. To improve security, metrics are key to ensuring the accuracy of the assessment and evaluation. Measurements are the core of the process of identifying the causality and effectiveness of various behaviors, and accurate measurement with the right assumptions is a cornerstone for accurate analysis. Finally, contextualized analysis essential for understanding measurements. Different results can be derived for the same data according to the analysis method, and it can serve as a basis for understanding and improving …

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Decentralized Adaptable Task Allocation For Ongoing Tasks, Vera Kazakova Jan 2020

Decentralized Adaptable Task Allocation For Ongoing Tasks, Vera Kazakova

Electronic Theses and Dissertations, 2020-2023

This thesis extends an existing bio-inspired model for decentralized task allocation and benchmarks it against alternative approaches to assess robustness in dynamic conditions, applicability to domains with ongoing and hierarchical tasks, and scalability to large teams of agents. The work addresses decentralized task allocation of simple non-communicating agents in dynamic environments of multiple tasks. Multi-area patrolling is used as the sample domain: specific number of agents is required to successfully patrol each area on each timestep, indefinitely, until the system's security needs. Agents must individually decide whether to patrol and where (i.e., task availability is not limited by task demand …

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The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices, Eugene Taranta Jan 2020

The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices, Eugene Taranta

Electronic Theses and Dissertations, 2020-2023

Humans use gestures to emphasize ideas and disseminate information. Their importance is apparent in how we continuously augment social interactions with motion—gesticulating in harmony with nearly every utterance to ensure observers understand that which we wish to communicate, and their relevance has not escaped the HCI community's attention. For almost as long as computers have been able to sample human motion at the user interface boundary, software systems have been made to understand gestures as command metaphors. Customization, in particular, has great potential to improve user experience, whereby users map specific gestures to specific software functions. However, custom gesture recognition …

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Separating Content Selection From Surface Realization In Neural Text Summarization, Logan Lebanoff Jan 2020

Separating Content Selection From Surface Realization In Neural Text Summarization, Logan Lebanoff

Electronic Theses and Dissertations, 2020-2023

Text summarization is a rapidly growing field with many new innovations. End-to-end models using the sequence-to-sequence architecture achieve high scores according to automatic metrics on standard datasets. However, they frequently generate summaries that are factually inconsistent with the original article -- a vital problem to be solved before the summaries can be used in real-world applications. In addition, they are not generalizable to new domains, especially those with few training examples. In this dissertation, we propose to explicitly separate the two steps of content selection and surface realization in summarization. Content selection is the process of choosing important words/phrases/sentences from …

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Novel Computational Approaches For Multidimensional Brain Image Analysis, Harish Raviprakash Jan 2020

Novel Computational Approaches For Multidimensional Brain Image Analysis, Harish Raviprakash

Electronic Theses and Dissertations, 2020-2023

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical …

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Interdisciplinary Cybersecurity For Resilient Cyberdefense, Rachid Ait Maalem Lahcen Jan 2020

Interdisciplinary Cybersecurity For Resilient Cyberdefense, Rachid Ait Maalem Lahcen

Electronic Theses and Dissertations, 2020-2023

Cybersecurity's role is to protect confidentiality, integrity, and availability of enterprise assets. Confidentiality secures data from theft, integrity mitigates modification of data in a malicious way, and availability assures continuation of systems' access and services. However, achieving these goals is difficult due to the mushrooming of various cyber attackers that come from individuals or state actors with motives ranging from ideological, financial, state-sponsored espionage, revenge, or simple curiosity and boredom. The difficulty also lies in the complexity of the cyber layers that are not well studied. Layers that interconnect and require effective communication and collaboration. This effectiveness is still lacking …

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Musical Cryptography Using Long Short-Term Memory Networks, Curtis Helsel Jan 2020

Musical Cryptography Using Long Short-Term Memory Networks, Curtis Helsel

Honors Undergraduate Theses

Musical cryptography is a technique in which plain text messages are enciphered into a musical composition. Recently, a surge of music composition by means of machine learning have produced natural-sounding music that can be deemed as composed by humans. The combination of machine-generated music and enciphering a message into the composition is a logical step in musical cryptography. Outlined in this thesis is a method that incorporates the use of a specific type of recurrent neural network, Long Short-Term Memory, and a variant of the substitution cipher to form of symmetric-key cryptography system. Exploration was also completed to determine how …

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Understanding The Challenges Child Welfare Workers Encounter Related To Promoting The Online Safety Of Foster Youth, Denielle Kirk L. Abaquita Jan 2020

Understanding The Challenges Child Welfare Workers Encounter Related To Promoting The Online Safety Of Foster Youth, Denielle Kirk L. Abaquita

Honors Undergraduate Theses

Foster care case managers are responsible for the wellbeing of foster youth in the foster care system. Teens (ages 13-17) in foster care are most vulnerable to serious risks, such as sex trafficking. Such risks have been heightened by the advent of internet-based technologies that connect foster youth with unsafe others at unprecedented frequency and speed. This thesis examines how case managers tackle the challenge of online safety as it relates to adolescents in the foster care system in the United States. I conducted 32 semi-structured interviews with case managers who worked with foster teens (ages 13-17) within the past …

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Co-Designing "Teenovate": An Intergenerational Online Safety Design Team, Arianna J. Davis Jan 2020

Co-Designing "Teenovate": An Intergenerational Online Safety Design Team, Arianna J. Davis

Honors Undergraduate Theses

The Socio-Technical Interaction Research (STIR) Lab at UCF intends to create a new participatory design program, called "Teenovate," where teenagers and adults work together to design technologies that keep teens safe online. Previous participatory design projects, however, commonly focus on younger children under the age of 13. Teens differ significantly from young children in how they develop, socialize, and perceive the world. To inform the design of Teenovate, so that their unique needs are appropriately met, we conducted a participatory design study with 21 teens using polls, open-ended response questions, and subsequent group discussions. The teens were intrigued by the …

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Analyzing And Detecting Internet Of Things Malware Using Residual Static Graph- And String-Based Artifacts, Hisham Alasmary Jan 2020

Analyzing And Detecting Internet Of Things Malware Using Residual Static Graph- And String-Based Artifacts, Hisham Alasmary

Electronic Theses and Dissertations, 2020-2023

Recently, the Internet of Things (IoT) has become wider and adopted many features from social networks and mainly uses sensing devices technologies, causing a rapid increase in production and adoption. However, security and privacy are serious threats that users usually take precautions to protect their devices and information. Thus, understanding the security shortcomings at first stage will educate IoT users to protect their connected things. Understanding IoT software through analysis, comparison (with other types of malware), and detection (from benign IoT) is an essential problem to mitigate security threats. We focus on two central perspectives, the graph and string representations …

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Video Content Understanding Using Text, Amir Mazaheri Jan 2020

Video Content Understanding Using Text, Amir Mazaheri

Electronic Theses and Dissertations, 2020-2023

The rise of the social media and video streaming industry provided us a plethora of videos and their corresponding descriptive information in the form of concepts (words) and textual video captions. Due to the mass amount of available videos and the textual data, today is the best time ever to study the Computer Vision and Machine Learning problems related to videos and text. In this dissertation, we tackle multiple problems associated with the joint understanding of videos and text. We first address the task of multi-concept video retrieval, where the input is a set of words as concepts, and the …

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Towards Robust Artificial Intelligence Systems, Sunny Raj Jan 2020

Towards Robust Artificial Intelligence Systems, Sunny Raj

Electronic Theses and Dissertations, 2020-2023

Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a …

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Explore And Design Novel Structures For More Efficient And Better Deep Convolutional Neural Networks, Min Wang Jan 2020

Explore And Design Novel Structures For More Efficient And Better Deep Convolutional Neural Networks, Min Wang

Electronic Theses and Dissertations, 2020-2023

Deep Convolutional Neural Networks have achieved remarkable performance on visual recognition problems, and have been extensively adopted in real-world applications, such as Apple's Face ID security system, autonomous driving cars, and automatic image tagging in online album services. One major concern in the development of CNNs is that their computational complexity grows along with the increase in their accuracy. Therefore, there is a continuous demand to find the right balance between accuracy and complexity in the design of CNN models. This dissertation focuses on designing various novel structures to enhance the performance of CNNs and their efficiency. Our efforts fall …

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Equivariance And Invariance For Robust Unsupervised And Semi-Supervised Learning, Liheng Zhang Jan 2020

Equivariance And Invariance For Robust Unsupervised And Semi-Supervised Learning, Liheng Zhang

Electronic Theses and Dissertations, 2020-2023

Although there is a great success of applying deep learning on a wide variety of tasks, it heavily relies on a large amount of labeled training data, which could be hard to obtain in many real scenarios. To address this problem, unsupervised and semi-supervised learning emerge to take advantage of the plenty of cheap unlabeled data to improve the model generalization. In this dissertation, we claim that equivariant and invariance are two critical criteria to approach robust unsupervised and semi-supervised learning. The idea is as follows: the features of a robust model ought to be sufficiently informative and equivariant to …

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Navigating Immersive And Interactive Vr Environments With Connected 360° Panoramas, Samuel Cosgrove Jan 2020

Navigating Immersive And Interactive Vr Environments With Connected 360° Panoramas, Samuel Cosgrove

Electronic Theses and Dissertations, 2020-2023

Emerging research is expanding the idea of using 360-degree spherical panoramas of real-world environments for use in "360 VR" experiences beyond video and image viewing. However, most of these experiences are strictly guided, with few opportunities for interaction or exploration. There is a desire to develop experiences with cohesive virtual environments created with 360 VR that allow for choice in navigation, versus scripted experiences with limited interaction. Unlike standard VR with the freedom of synthetic graphics, there are challenges in designing appropriate user interfaces (UIs) for 360 VR navigation within the limitations of fixed assets. To tackle this gap, we …

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Stochastic Sampling And Machine Learning Techniques For Social Media State Production, Neda Hajiakhoond Bidoki Jan 2020

Stochastic Sampling And Machine Learning Techniques For Social Media State Production, Neda Hajiakhoond Bidoki

Electronic Theses and Dissertations, 2020-2023

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries' use of the global information environment. Our hope is that this type of …

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Reconstruction Of Bacterial Strain Genomes From Shotgun Metagenomic Reads, Xin Li Jan 2020

Reconstruction Of Bacterial Strain Genomes From Shotgun Metagenomic Reads, Xin Li

Electronic Theses and Dissertations, 2020-2023

It is necessary to study bacterial strains in environmental samples. The environmental samples are mixed DNA samples collected from the ocean, soil, lake, human body sites, etc. In a natural environment, they provide us new insights into the diversity of our earth. As for bacterial strains on or inside human bodies, to select the proper treatment for diseases caused by bacterial strains, it is critical to identify the corresponding strains and reconstruct their genomes. However, it is a challenge to do so with the DNA from a large number of unknown microbial species mixed together in an environmental sample. The …

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Deep Recurrent Networks For Gesture Recognition And Synthesis, Mehran Maghoumi Jan 2020

Deep Recurrent Networks For Gesture Recognition And Synthesis, Mehran Maghoumi

Electronic Theses and Dissertations, 2020-2023

It is hard to overstate the importance of gesture-based interfaces in many applications nowadays. The adoption of such interfaces stems from the opportunities they create for incorporating natural and fluid user interactions. This highlights the importance of having gesture recognizers that are not only accurate but also easy to adopt. The ever-growing popularity of machine learning has prompted many application developers to integrate automatic methods of recognition into their products. On the one hand, deep learning often tops the list of the most powerful and robust recognizers. These methods have been consistently shown to outperform all other machine learning methods …

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High Performance And Secure Execution Environments For Emerging Architectures, Mazen Alwadi Jan 2020

High Performance And Secure Execution Environments For Emerging Architectures, Mazen Alwadi

Electronic Theses and Dissertations, 2020-2023

Energy-efficiency and performance have been the driving forces of system architectures and designers in the last century. Given the diversity of workloads and the significant performance and power improvements when running workloads on customized processing elements, system vendors are drifting towards new system architectures (e.g., FAM or HMM). Such architectures are being developed with the purpose of improving the system's performance, allow easier data sharing, and reduce the overall power consumption. Additionally, current computing systems suffer from a very wide attack surface, mainly due to the fact that such systems comprise of tens to hundreds of sub-systems that could be …

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Endpoints And Interdependencies In Internet Of Things Residual Artifacts: Measurements, Analyses, And Insights Into Defenses, Jinchun Choi Jan 2020

Endpoints And Interdependencies In Internet Of Things Residual Artifacts: Measurements, Analyses, And Insights Into Defenses, Jinchun Choi

Electronic Theses and Dissertations, 2020-2023

The usage of Internet of Things (IoT) devices is growing fast. Moreover, the lack of security measures among the IoT devices and their persistent online connection give adversaries an opportunity to exploit them for multiple types of attacks, such as distributed denial-of-service (DDoS). To understand the risks of IoT devices, we analyze IoT malware from an endpoint standpoint. We investigate the relationship between endpoints infected and attacked by IoT malware, and gain insights into the underlying dynamics in the malware ecosystem. We observe the affinities and different patterns among endpoints. Towards this, we reverse-engineer 2,423 IoT malware samples and extract …

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The Susceptibility Of Deep Neural Networks To Natural Perturbations, Mesut Ozdag Jan 2020

The Susceptibility Of Deep Neural Networks To Natural Perturbations, Mesut Ozdag

Electronic Theses and Dissertations, 2020-2023

Deep learning systems have achieved great success in various types of applications in recent years. They are increasingly being adopted for safety-critical tasks such as face recognition, surveillance systems, speech recognition, and autonomous driving. On the other hand, it has been found that deep neural networks (DNNs) can easily be fooled by adversarial input samples. These imperceptible perturbations on images can lead any machine learning system to misclassify the objects with high confidence. Furthermore, they can be almost indistinguishable to a human observer. These systems can also be exposed to adverse weather conditions such as fog, rain, and snow. This …

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Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad Jan 2020

Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad

Electronic Theses and Dissertations, 2020-2023

Successful software authorship identification has both software forensics applications and privacy implications. However, the process requires an efficient extraction of quality authorship attributes. The extraction of such attributes is very challenging due to several factors such as the variety of software formats, number of available samples, and possible obfuscation or adversarial manipulation. We focus on software authorship identification from three central perspectives: large-scale single-authored software, real-world multi-authored software, and the robustness assessment of code authorship identification methods against adversarial attacks. First, we propose DL-CAIS, a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software …

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Improving Security Of Crypto Wallets In Blockchain Technologies, Hossein Rezaeighaleh Jan 2020

Improving Security Of Crypto Wallets In Blockchain Technologies, Hossein Rezaeighaleh

Electronic Theses and Dissertations, 2020-2023

A big challenge in blockchain and cryptocurrency is securing the private key from potential hackers. Nobody can rollback a transaction made with a stolen key once the network confirms it. The technical solution to protect private keys is the cryptocurrency wallet, software, hardware, or a combination to manage the keys. In this dissertation, we try to investigate the significant challenges in existing cryptocurrency wallets and propose innovative solutions. Firstly, almost all cryptocurrency wallets suffer from the lack of a secure and convenient backup and recovery process. We offer a new cryptographic scheme to securely back up a hardware wallet relying …

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