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Full-Text Articles in Computer Sciences
Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt
Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt
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Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
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In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …
An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow
An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow
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This paper presents a framework for the generation of 3D models. This is an important problem for many reasons. For example, 3D models are important for systems that are involved in target recognition. These systems use 3D models to train up accuracy on identifying real world object. Traditional means of gathering 3D models have limitations that the generation of 3D models can help overcome. The framework uses a novel generative adversarial network (GAN) that learns latent representations of two dimensional views of a model to bootstrap the network’s ability to learn to generate three dimensional objects. The novel architecture is …
Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi
Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi
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Quantitative susceptibility mapping (QSM) is a powerful technique that reveals changes in the underlying tissue susceptibility distribution. It can be used to measure the concentrations of iron and calcium in the brain both of which are linked with numerous neurodegenerative diseases. However, reconstructing the QSM image from the MRI phase data is an ill-posed inverse problem. Different methods have been proposed to overcome this difficulty. Still, the reconstructed QSM images suffer from streaking artifacts and underestimate the measured susceptibility of deep gray matter, veins, and other high susceptibility regions. This thesis proposes a structurally constrained Susceptibility Weighted Imaging and Mapping …
Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty
Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty
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Real-time distributed systems constitute computing nodes that are connected by a network and coordinate with one another to accomplish a cooperative task, combining the responsiveness, fault-tolerance and geographic independence to support time-constrained collaborative applications, including distributed Human-Machine Teaming. In this thesis research the viability of real-time distributed collaborative technologies is demonstrated through the design, development and validation of prototype systems that support two human-machine teaming scenarios namely, ACE-IMS (Affirmation Cue based Interruption Management Systems) and ReadMI (Real-time Assessment of Dialogue in Motivational Interview). ACE-IMS demonstrates how a combination of AI capabilities and the cloud and mobile computing infrastructure can be …
Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin
Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin
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Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …
Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie
Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie
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Deep Neural Networks (DNNs) are powerful tools blossomed in a variety of successful real-life applications. While the performance of DNNs is outstanding, their opaque nature raises a growing concern in the community, causing suspicions on the reliability and trustworthiness of decisions made by DNNs. In order to release such concerns and towards building reliable deep learning systems, research efforts are actively made in diverse aspects such as model interpretation, model fairness and bias, adversarial attacks and defenses, and so on. In this dissertation, we focus on the research topic of DNN interpretations for visual intelligence, aiming to unfold the black-box …
Analyzing Public View Towards Vaccination Using Twitter, Mahajan Rutuja
Analyzing Public View Towards Vaccination Using Twitter, Mahajan Rutuja
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Educating people about vaccination tends to target vaccine acceptance and reduction of hesitancy. Social media provides a promising platform for studying public perception regarding vaccination. In this study, we harvested tweets over a year related to vaccines from February 2018 to January 2019. We present a two-stage classifier to: (1) classify the tweets as relevant or non-relevant and (2) categorize them in terms of pro-vaccination, anti-vaccination, or neutral outlook. We found that the classifier was able to distinguish clearly between anti-vaccination and pro-vaccination tweets, but also misclassified many of these as neutral. Using Latent Dirichlet Allocation, we found that two …
Detecting Malicious Behavior In Openwrt With Qemu Tracing, Jeremy Porter
Detecting Malicious Behavior In Openwrt With Qemu Tracing, Jeremy Porter
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In recent years embedded devices have become more ubiquitous than ever before and are expected to continue this trend. Embedded devices typically have a singular or more focused purpose, a smaller footprint, and often interact with the physical world. Some examples include routers, wearable heart rate monitors, and thermometers. These devices are excellent at providing real time data or completing a specific task quickly, but they lack many features that make security issues more obvious. Generally, Embedded devices are not easily secured. Malware or rootkits in the firmware of an embedded system are difficult to detect because embedded devices do …
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
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Information Extraction (IE) techniques are developed to extract entities, relationships, and other detailed information from unstructured text. The majority of the methods in the literature focus on designing supervised machine learning techniques, which are not very practical due to the high cost of obtaining annotations and the difficulty in creating high quality (in terms of reliability and coverage) gold standard. Therefore, semi-supervised and distantly-supervised techniques are getting more traction lately to overcome some of the challenges, such as bootstrapping the learning quickly. This dissertation focuses on information extraction, and in particular entities, i.e., Named Entity Recognition (NER), from multiple domains, …
Automated Vehicle Electronic Control Unit (Ecu) Sensor Location Using Feature-Vector Based Comparisons, Gregory S. Buthker
Automated Vehicle Electronic Control Unit (Ecu) Sensor Location Using Feature-Vector Based Comparisons, Gregory S. Buthker
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In the growing world of cybersecurity, being able to map and analyze how software and hardware interact is key to understanding and protecting critical embedded systems like the Engine Control Unit (ECU). The aim of our research is to use our understanding of the ECU's control flow attained through manual analysis to automatically map and identify sensor functions found within the ECU. We seek to do this by generating unique sets of feature vectors for every function within the binary file of a car ECU, and then using those feature sets to locate functions within each binary similar to their …
Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra
Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra
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Coaching patients to follow the rehabilitation routines correctly and timely after surgery is often a challenge due to the limited medical knowledge of patients and limited availability of clinicians. Similarly, it is also a challenge to train medical professionals with both the technical and communication skills required in their practices. The recent emergence of VR technologies shines the light on improving the current training practices. In this thesis research, I will look at the development and application of VR-based immersive training games for two particular cases: 1. Post hand surgery rehab; and, 2. Training for Social determinants of health (SDOH) …
Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal
Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal
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The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecule level. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how the immune system of a patient evolves over different stages of disease development. A recent study has shown that the hierarchical clustering (HC) algorithm gives the best results for B-cell clones analysis - an important type of immune repertoire sequencing (IR-Seq) analysis. However, due to the inherent complexity, the classical hierarchical clustering algorithm does not scale well to large sequence datasets. Surprisingly, no algorithms …
Accelerating Reverse Engineering Image Processing Using Fpga, Matthew Joshua Harris
Accelerating Reverse Engineering Image Processing Using Fpga, Matthew Joshua Harris
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In recent decades, field programmable gate arrays (FPGAs) have evolved beyond simple, expensive computational components with minimal computing power to complex, inexpensive computational engines. Today, FPGAs can perform algorithmically complex problems with improved performance compared to sequential CPUs by taking advantage of parallelization. This concept can be readily applied to the computationally dense field of image manipulation and analysis. Processed on a standard CPU, image manipulation suffers with large image sets processed by highly sequential algorithms, but by carefully adhering to data dependencies, parallelized FPGA functions or kernels offer the possibility of significant improvement through threaded CPU functions. This thesis …
Virtual Reality And Analysis Framework For Studying Different Layout Designs, Madison Glines
Virtual Reality And Analysis Framework For Studying Different Layout Designs, Madison Glines
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This thesis describes the tools for studying different design prototypes. The goal was to develop effective tools to study these designs using a data-driven approach. “Proof of concept” experiments were conducted, in which participants were allowed to interact with a virtual environment depicting different designs as data pertaining to their virtual location and orientation was recorded for later analysis. The designs included “flat” store racks, as opposed to racks with more varied shapes, as well as “curved” racks. Focus of the design studies was to assist in identifying optimal locations for different product types. The automated data collection mechanisms required …
Securing Modern Cyberspace Using A Multi-Faceted Approach, Yu Li
Securing Modern Cyberspace Using A Multi-Faceted Approach, Yu Li
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Security has become one of the most significant concerns for our cyberspace. Securing the cyberspace, however, becomes increasingly challenging. This can be attributed to the rapidly growing diversities and complexity of the modern cyberspace. Specifically, it is not any more dominated by connected personal computers (PCs); instead, it is greatly characterized by cyber-physical systems (CPS), embedded systems, dynamic services, and human-computer interactions. Securing modern cyberspace therefore calls for a multi-faceted approach capable of systematically integrating these emerging characteristics. This dissertation presents our novel and significant solutions towards this direction. Specifically, we have devised automated, systematic security solutions to three critical …
Islands Of Fitness Compact Genetic Algorithm For Rapid In-Flight Control Learning In A Flapping-Wing Micro Air Vehicle: A Search Space Reduction Approach, Kayleigh E. Duncan
Islands Of Fitness Compact Genetic Algorithm For Rapid In-Flight Control Learning In A Flapping-Wing Micro Air Vehicle: A Search Space Reduction Approach, Kayleigh E. Duncan
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On-going effective control of insect-scale Flapping-Wing Micro Air Vehicles could be significantly advantaged by active in-flight control adaptation. Previous work demonstrated that in simulated vehicles with wing membrane damage, in-flight recovery of effective vehicle attitude and vehicle position control precision via use of an in-flight adaptive learning oscillator was possible. Most recent approaches to this problem employ an island-of-fitness compact genetic algorithm (ICGA) for oscillator learning. The work presented provides the details of a domain specific search space reduction approach implemented with existing ICGA and its effect on the in-flight learning time. Further, it will be demonstrated that the proposed …
Towards Data And Model Confidentiality In Outsourced Machine Learning, Sagar Sharma
Towards Data And Model Confidentiality In Outsourced Machine Learning, Sagar Sharma
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With massive data collections and needs for building powerful predictive models, data owners may choose to outsource storage and expensive machine learning computations to public cloud providers (Cloud). Data owners may choose cloud outsourcing due to the lack of in-house storage and computation resources or the expertise of building models. Similarly, users, who subscribe to specialized services such as movie streaming and social networking, voluntarily upload their data to the service providers' site for storage, analytics, and better services. The service provider, in turn, may also choose to benefit from ubiquitous cloud computing. However, outsourcing to a public cloud provider …
Abusive And Hate Speech Tweets Detection With Text Generation, Abhishek Nalamothu
Abusive And Hate Speech Tweets Detection With Text Generation, Abhishek Nalamothu
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According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text …
Llvm-Ir Based Decompilation, Ilsoo Jeon
Llvm-Ir Based Decompilation, Ilsoo Jeon
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Decompilation is a process of transforming an executable program into a source-like high-level language code, which plays an important role in malware analysis, and vulnerability detection. In this thesis, we design and implement the middle end of a decompiler framework, focusing on Low Level Language properties reduction using the optimization techniques, propagation and elimination. An open-source software tool, dagger, is used to translate binary code to LLVM (Low Level Virtual Machine) Intermediate Representation code. We perform data flow analysis and control flow analysis on the LLVM format code to generate high-level code using a Functional Programming Langauge (FPL), Haskell. The …
Speech Enabled Navigation In Virtual Environments, Raksha Rajashekar
Speech Enabled Navigation In Virtual Environments, Raksha Rajashekar
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Navigating in a Virtual Environment with traditional input devices such as mouse, joysticks and keyboards provide limited maneuverability and is also time consuming. While working in a virtual environment, changing parameters to obtain the desired visualization requires time to achieve by manually entering parameter values in an algorithm to test outcomes. The following thesis presents an alternate user interface to reduce user efforts, while navigating within the Virtual Environment. The user interface is an Android application which is designed to accommodate spoken commands. This Speech Enabled User Interface termed as the Speech Navigation Application (SNA), provides the user with an …
Kbot: Knowledge-Enabled Personalized Chatbot For Self-Management Of Asthma In Pediatric Population, Dipesh Kadariya
Kbot: Knowledge-Enabled Personalized Chatbot For Self-Management Of Asthma In Pediatric Population, Dipesh Kadariya
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Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, the demand for continuous monitoring of patient’s adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design …
Conditional Dilated Attention Tracking Model - C-Datm, Tyler Clayton Highlander
Conditional Dilated Attention Tracking Model - C-Datm, Tyler Clayton Highlander
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Current commercial tracking systems do not process images fast enough to perform target-tracking in real- time. State-of-the-art methods use entire scenes to locate objects frame-by-frame and are commonly computationally expensive because they use image convolutions. Alternatively, attention mechanisms track more efficiently by mimicking human optical cognitive interaction to only process small portions of an image. Thus, in this work we use an attention-based approach to create a model called C-DATM (Conditional Dilated Attention tracking Model) that learns to compare target features in a sequence of image-frames using dilated convolutions. The C-DATM is tested using the Modified National Institute of Standards …
Leveraging Schema Information For Improved Knowledge Graph Navigation, Rama Someswar Chittella
Leveraging Schema Information For Improved Knowledge Graph Navigation, Rama Someswar Chittella
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Over the years, the semantic web has emerged as a new generation of the world wide web featuring advanced technologies and research contributions. It has revolutionized the usage of information by allowing users to capture and publish machine-understandable data and expedite methods such as ontologies to perform the same. These ontologies help in the formal representation of a specified domain and foster comprehensive machine understanding. Although, the engineering of ontologies and usage of logic have been an integral part of the web semantics, new areas of research such as the semantic web search, linking and usage of open data on …
Static Evaluation Of Type Inference And Propagation On Global Variables With Varying Context, Ivan Frasure
Static Evaluation Of Type Inference And Propagation On Global Variables With Varying Context, Ivan Frasure
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Software reverse engineering (SRE) is a broad field with motivations ranging from verifying or documenting gordian source code files to understanding and reimplementing binary object files and executables. SRE of binaries is exceptionally compelling and challenging due to large amounts of information that can be lost in the compilation progress. A central area in SRE is type inference. Type inference is built around a fundamental step in understanding the behavior of a binary, recovering the types of data in the program. Type inference has many unique techniques in both static and dynamic type inference systems that have been implemented in …
Anticipation In Dynamic Environments: Deciding What To Monitor, Zohreh A. Dannenhauer
Anticipation In Dynamic Environments: Deciding What To Monitor, Zohreh A. Dannenhauer
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In dynamic environments, external changes may occur that may affect planning decisions and goal choices. We claim that an intelligent agent should actively watch for what can go wrong and anticipate changes in the environment that allows the changing of its plan or changing of a given goal. In this thesis, we focus on the relationship between perception, act, interpretation, and planning. We claim that these components are not independent and need to interact with each other to help the agent succeed in achieving its goals and plans. If newly encountered world information affects the plan, the agent adapts to …
Software Implementations And Applications Of Elliptic Curve Cryptography, Kirill Kultinov
Software Implementations And Applications Of Elliptic Curve Cryptography, Kirill Kultinov
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Elliptic Curve Cryptography (ECC) is a public-key cryptography system. Elliptic Curve Cryptography (ECC) can achieve the same level of security as the public-key cryptography system, RSA, with a much smaller key size. It is a promising public key cryptography system with regard to time efficiency and resource utilization. This thesis focuses on the software implementations of ECC over finite field GF(p) with two distinct implementations of the Big Integer classes using character arrays, and bit sets in C++ programming language. Our implementation works on the ECC curves of the form y^2 = x^3 + ax + b (mod p). The …
Rules With Right Hand Existential Or Disjunction With Rowltab, Sri Jitendra Satpathy
Rules With Right Hand Existential Or Disjunction With Rowltab, Sri Jitendra Satpathy
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One hotly debated research topic is, “What is the best approach for modeling ontologies?”. In the earlier stages of modeling ontologies, researchers have favored the usage of description logic to capture knowledge. One such choice is the Web Ontology Language (OWL) that is based on description logic. Many tools were designed around this principle and are still widely being used to model and explore ontologies. However, not all users find description logic to be intuitive, at least not without an extensive background in formal logics. Due to this, researchers have tried to explore other ways that will enable such users …
Recognition Of Incomplete Objects Based On Synthesis Of Views Using A Geometric Based Local-Global Graphs, Michael Christopher Robbeloth
Recognition Of Incomplete Objects Based On Synthesis Of Views Using A Geometric Based Local-Global Graphs, Michael Christopher Robbeloth
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The recognition of single objects is an old research field with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues associated to shadows, illumination and other visual characteristics. With object incompleteness, we mean missing parts of a known object and not low-resolution images of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not provide a robust answer to the challenging problem. In this dissertation, we present a suite of high-level, model-based computer vision techniques encompassing both geometric and machine learning approaches …
Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi
Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi
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In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic …