Computer Sciences Commons^™

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

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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 …

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 …

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

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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 …

Iamhappy: Towards An Iot Knowledge-Based Cross-Domain Well-Being Recommendation System For Everyday Happiness, Amelia Gyrard, Amit Sheth

Kno.e.sis Publications

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people’s feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internetof Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every …

Deep Neural Ranking For Crowdsourced Geopolitical Event Forecasting, Giuseppe Nebbione, Derek Doran, Srikanth Nadella, Brandon Minnery

Computer Science and Engineering Faculty Publications

There are many examples of “wisdom of the crowd” effects in which the large number of participants imparts confidence in the collective judgment of the crowd. But how do we form an aggregated judgment when the size of the crowd is limited? Whose judgments do we include, and whose do we accord the most weight? This paper considers this problem in the context of geopolitical event forecasting, where volunteer analysts are queried to give their expertise, confidence, and predictions about the outcome of an event. We develop a forecast aggregation model that integrates topical information about a question, meta-data about …

Question Answering For Suicide Risk Assessment Using Reddit, Amanuel Alambo, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelia Gyrard, Randon S. Welton, Jyotishman Pathak, Amit P. Sheth

Kno.e.sis Publications

Mental Health America designed ten questionnaires that are used to determine the risk of mental disorders. They are also commonly used by Mental Health Professionals (MHPs) to assess suicidality. Specifically, the Columbia Suicide Severity Rating Scale (C-SSRS), a widely used suicide assessment questionnaire, helps MHPs determine the severity of suicide risk and offer an appropriate treatment. A major challenge in suicide treatment is the social stigma wherein the patient feels reluctance in discussing his/her conditions with an MHP, which leads to inaccurate assessment and treatment of patients. On the other hand, the same patient is comfortable freely discussing his/her mental …

Visual Entailment: A Novel Task For Fine-Grained Image Understanding, Ning Xie, Farley Lai, Derek Doran, Asim Kadav

Computer Science and Engineering Faculty Publications

Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained reasoning but the dataset is synthetic and consists of similar objects and sentence structures across the dataset. In this paper, we introduce a new inference task, Visual Entailment (VE) - consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model …

Xr-Based Workforce Develop In The Southwestern Region Of Ohio, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

No abstract provided.

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

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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

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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, …

Automatic Identification Of Individual Drugs In Death Certificates, Soon Jye Kho, Amit Sheth, Olivier Bodenreider

Kno.e.sis Publications

Background:

Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging due to the variability of drug names.

Objectives:

To automatically identify individual drugs in death certificates.

Methods:

We use RxNorm to collect variants for drug names (generic names, synonyms, brand names) and we algorithmically generate common misspellings. We use this automatically compiled list to identify drug mentions from 703,106 death certificates and compare the performance of our automated approach to that of …

Visual Entailment Task For Visually-Grounded Language Learning, Ning Xie, Farley Lai, Derek Doran, Asim Kadav

Computer Science and Engineering Faculty Publications

We introduce a new inference task - Visual Entailment (VE) - which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a natural language sentence as in TE tasks. A novel dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is proposed for VE tasks based on the Stanford Natural Language Inference corpus and Flickr30k. We introduce a differentiable architecture called the Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and several other state-of-the-art visual question answering (VQA) based models are evaluated on the SNLI-VE dataset, facilitating grounded language understanding and providing …

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 …

Empathi: An Ontology For Emergency Managing And Planning About Hazard Crisis, Manas Gaur, Kaeedeh Shekarpour, Amelia Gyrard, Amit P. Sheth

Kno.e.sis Publications

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people.
A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts describing the domain of emergency managing and planning of hazard crises.
Although empathi has a coarse-grained view, it considers the necessary …

Augmenting Flight Imagery From Aerial Refueling, James D. Anderson, Scott Nykl, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

© 2019, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. When collecting real-world imagery, objects in the scene may be occluded by other objects from the perspective of the camera. However, in some circumstances an occluding object is absent from the scene either for practical reasons or the situation renders it infeasible. Utilizing augmented reality techniques, those images can be altered to examine the affect of the object’s occlusion. This project details a novel method for augmenting real images with virtual objects in a virtual environment. Specifically, images from …

Extensions To The Ontology Design Pattern Representation Language, Quinn Hirt, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Recently, modular ontology modeling has become a more popular ontology engineering paradigm. With it, the need for additional metadata associated with ontology design patterns has grown. The Ontology Pattern Language (OPLa) was developed to facilitate annotating ontologies with useful metadata, as well as supporting tooling infrastructure. In this paper, we detail three extensions to OPLa into a reorganized namespace: OPLa-core, containing the original annotations; OPLa-SD, for use in detailing schema diagrams; and OPLa-CP, an adaptation of the content-pattern annotation schema.

Comodide – The Comprehensive Modular Ontology Engineering Ide, Cogan Shimizu, Karl Hammar

Computer Science and Engineering Faculty Publications

No abstract provided.

Modl: A Modular Ontology Design Library, Cogan Shimizu, Quinn Hirt, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high upfront cost, e.g. reusing a pattern is predicated upon being aware of its existence in the first place. Thus, to help overcome these barriers, we have developed MODL: a modular ontology design library. MODL is a curated collection of well-documented ontology design patterns, drawn from a wide variety of interdisciplinary use-cases. In this paper we present MODL as a useful resource for the development of high-quality, modular ontologies, discuss its use, …

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) …

An Interactive Game For Cultural Proficiencytraining Featuring Virtual Reality Immersion, Paul J. Hershberger, Blaine A. Klingler, Matt Davis, Sankalp Mishra, Miteshkumar Vasoya, Dixit Patel, Aishwarya Bositty, Tanuja Addanki, Frank A. Allen, Suneesh Menon, Sabrina Neeley, Angie Castle, Todd Pavlak, Yong Pei, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

No abstract provided.

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 …

Adaptive Knowledge Networks: A Time Capsule, Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L. Shalin

Kno.e.sis Publications

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

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

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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

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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

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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

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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 …