Development Of A Human-Ai Teaming Based Mobile Language Learning Solution For Dual Language Learners In Early And Special Educations,
2018
Wright State University
Development Of A Human-Ai Teaming Based Mobile Language Learning Solution For Dual Language Learners In Early And Special Educations, Saurabh Shukla
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Learning English as a secondary language is often an overwhelming challenge for dual language learners (DLLs), whose first language (L1) is not English, especially for children in early education (PreK-3 age group). These early DLLs need to devote a considerable amount of time learning to speak and read the language, in order to gain the language proficiency to function and compete in the classroom. Fear of embarrassment when mispronouncing words in front of others may drive them to remain silent; effectively hampering their participation in the class and overall curricular growth. The process of learning a new language can benefit …
Overwhelming The Saa System Of Delivery Uavs By Drone Swarming,
2018
Wright State University
Overwhelming The Saa System Of Delivery Uavs By Drone Swarming, Barry Lynn Pfaff
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As the Internet continues to replace the brick-and-mortar store as the main place for purchasing goods, web-based companies are looking for ways to reduce the cost of delivering those goods. The use of Unmanned Aerial Vehicles, or UAVs, is one delivery method that is increasingly being used. These UAVs can be programmed with delivery routes and destinations and can complete the job while requiring limited intervention from human controllers. Sense and Avoidance (SAA) systems have recently been incorporated into these UAVs so that they can detect objects in their flight path, reroute the UAV accordingly and operate even more autonomously. …
Multiple Drone Detection And Acoustic Scene Classification With Deep Learning,
2018
Wright State University
Multiple Drone Detection And Acoustic Scene Classification With Deep Learning, Hari Charan Vemula
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Classification of environmental scenes and detection of events in one's environment from audio signals enables one to create better-planning agents, intelligent navigation systems, pattern recognition systems, and audio surveillance systems. This thesis will explore the use of Convolutional Neural Networks(CNN'S) with spectrograms and raw audio waveforms as inputs to Deep Neural Networks with hand engineered features extracted from large-scale feature extraction schemes to identify the acoustic scenes and events. The first part focuses on building an audio pattern recognition system capable of detecting the if there are zero, one, or two DJI phantoms in the scene within the range of …
Augment The Multi-Modal Interaction Capabilities Of Hololens Through Networked Sensory Devices,
2018
Wright State University
Augment The Multi-Modal Interaction Capabilities Of Hololens Through Networked Sensory Devices, Subhadra Tummalapally
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Augmented Reality (AR) places virtual contents along with real objects to enhance user perception in the physical environment. It is a technology that has shown great potential in many industries such as medicine, education and transportation. AR devices, such as Microsoft HoloLens, come with multi-modal interaction capabilities, such as voice, gesture and gaze, to support intuitive and reliable human-computer interactions. However, HoloLens gesture support is limited to simple gestures like tap, bloom and hold, it stops short to support meaningful interaction through full hand gesture and motion tracking, which may be critical in field operations of various industry practices in …
Using Natural Language Processing And Machine Learning For Analyzing Clinical Notes In Sickle Cell Disease Patients,
2018
Wright State University
Using Natural Language Processing And Machine Learning For Analyzing Clinical Notes In Sickle Cell Disease Patients, Shufa Khizra
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Sickle Cell Disease (SCD) is a hereditary disorder in red blood cells that can lead to excruciating pain episodes. SCD causes the normal red blood cells to distort its shape and turn into sickle shape. The distorted shape makes the hemoglobin inflexible and stick to the walls of the vessels thereby obstructing the free flow of blood and eventually making the tissues suffer from lack of oxygen. The lack of oxygen causes serious problems including Acute Chest Syndrome (ACS), stroke, infection, organ damage, and over the lifetime an SCD can harm a persons spleen, brain, kidneys, eyes, bones. Sickling of …
Multi-Scale And Multi-Modal Streaming Data Aggregation And Processing For Decision Support During Natural Disasters,
2018
Wright State University
Multi-Scale And Multi-Modal Streaming Data Aggregation And Processing For Decision Support During Natural Disasters, Shruti Kar
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With the surge in digital information systems, there is a data deluge from various sources that can be analyzed and integrated to produce relevant, reliable and actionable information, for better decision making. We employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for an aggregate level analysis and location-centric demand/request matching in the context of disaster relief. After classifying the Need expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that Need on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood …
Threats And Mitigation Of Ddos Cyberattacks Against The U.S. Power Grid Via Ev Charging,
2018
Wright State University
Threats And Mitigation Of Ddos Cyberattacks Against The U.S. Power Grid Via Ev Charging, Glenn Sean Morrison
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Cars are an ever changing and integral part of modern society. Two of the biggest changes in vehicles today are their heavy integration with wireless communication and the push toward battery powered Electric Vehicles (EV). EV and EV charging stations have become a part of the Internet of Things (IoT). While this connectedness increases the convenience and functionality of the vehicles and charging stations, it also opens them up to a wide range of cyber threats. This thesis examines the potential threats against the EV charging ecosystem through a historical analysis of past cyberattacks and identified vulnerabilities. As EV charging …
Image Stitching And Matching Tool In The Automated Iterative Reverse Engineer (Aire) Integrated Circuit Analysis Suite,
2018
Wright State University
Image Stitching And Matching Tool In The Automated Iterative Reverse Engineer (Aire) Integrated Circuit Analysis Suite, David C. Bowman
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Due to current market forces, leading-edge semiconductor fabrication plants have moved outside of the US. While this is not a problem at first glance, when it comes to security-sensitive applications, over-production, device cloning, or design alteration becomes a possibility. Since these vulnerabilities exist during the fabrication phase, a Reverse Engineering (RE) step must be introduced to help ensure secure device operation. This thesis proposes several unique methods and a collection of tools to ensure trust assurance in integrated circuit design by detecting fabrication flaws and possible hardware Trojans using several image processing techniques; fused into a singular view of the …
Verifying Data-Oriented Gadgets In Binary Programs To Build Data-Only Exploits,
2018
Wright State University
Verifying Data-Oriented Gadgets In Binary Programs To Build Data-Only Exploits, Zachary David Sisco
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Data-Oriented Programming (DOP) is a data-only code-reuse exploit technique that "stitches" together sequences of instructions to alter a program's data flow to cause harm. DOP attacks are difficult to mitigate because they respect the legitimate control flow of a program and by-pass memory protection schemes such as Address Space Layout Randomization, Data Execution Prevention, and Control Flow Integrity. Techniques that describe how to build DOP payloads rely on a program's source code. This research explores the feasibility of constructing DOP exploits without source code-that is, using only binary representations of programs. The lack of semantic and type information introduces difficulties …
Human-Intelligence And Machine-Intelligence Decision Governance Formal Ontology,
2018
Old Dominion University
Human-Intelligence And Machine-Intelligence Decision Governance Formal Ontology, Faisal Mahmud
Engineering Management & Systems Engineering Theses & Dissertations
Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in …
Deep Recurrent Learning For Efficient Image Recognition Using Small Data,
2018
Old Dominion University
Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam
Electrical & Computer Engineering Theses & Dissertations
Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …
Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems,
2018
Old Dominion University
Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems, Hassan Hadi Latheeth Al-Maksousy
Computer Science Theses & Dissertations
Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). …
A Model For Seasonal Dynamic Networks,
2018
Wright State University
A Model For Seasonal Dynamic Networks, Jace D. Robinson
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Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. This paper presents statistical model of systems with seasonal dynamics, modeled as a dynamic network, to address this challenge. It assumes the probability of edge formations depend on a type assigned to incident nodes and the current time. Time dependencies are modeled by unique seasonal processes. The model is studied on several synthetic and real datasets. Superior fidelity of this model on seasonal datasets compared to existing network models, while being able to remain equally accurate for networks with randomly changing structure, is shown. The model is …
Content-Based Clustering And Visualization Of Social Media Text Messages,
2018
Wright State University
Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard
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Although Twitter has been around for more than ten years, crisis management agencies and first response personnel are not able to fully use the information this type of data provides during a crisis or natural disaster. This thesis addresses clustering and visualizing social media data by textual similarity, rather than by only time and location, as a tool for first responders. This thesis presents a tool that automatically clusters geotagged text data based on their content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis. For …
A Multi-Formal Languages Collaborative Scheme For Complex Human Activity Recognition And Behavioral Patterns Extraction,
2018
Wright State University
A Multi-Formal Languages Collaborative Scheme For Complex Human Activity Recognition And Behavioral Patterns Extraction, Anargyros Angeleas
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Human Activity Recognition is an actively researched domain for the past few decades, and is one of the most eminent applications of today. It is already part of our life, but due to high level of uncertainty and challenges of human detection, we have only application specific solutions. Thus, the problem being very demanding and still remains unsolved. Within this PhD we delve into the problem, and approach it from a variety of viewpoints. At start, we present and evaluate different architectures and frameworks for activity recognition. Henceforward, the focal point of our attention is automatic human activity recognition. We …
Development Of An Ios App For Learning Intonation Of Wind Instruments,
2018
Wright State University
Development Of An Ios App For Learning Intonation Of Wind Instruments, Swathi Pamidi
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Learning music instrument is a challenging task for a beginner without constant guidance from an instructor. The primary objective of this thesis research is to design and develop an iOS mobile / iPad learning app that helps users to learn and practice intonation for a suite of wind instruments by themselves with comfort and ease through app-provided tuning and charting guidance and app-assisted self-assessment. Particularly, our successfully-implemented app provides the following features to enhance the user's learning experience: 1 ) Provides learners easy-to-access information for the fingering and tuning techniques of wind instruments by converting Dr. Shelley Jagow's book - …
Malware Analysis Skills Taught In University Courses,
2018
Wright State University
Malware Analysis Skills Taught In University Courses, Swetha Gorugantu
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Career opportunities for malware analysts are growing at a fast pace due to the evolving nature of cyber threats as well as the necessity to counter them. However, employers are often unable to hire analysts fast though due to a lack of the required skillset. Hence, the primary purpose of the thesis is to conduct a gap analysis between the binary analysis skills taught in universities with those that the recruiters are looking for. Malware can be analyzed using three main types of tools and techniques: high-level profiling, static analysis, and dynamic analysis. These methods provide detailed information about the …
Sensor Data Streams Correlation Platform For Asthma Management,
2018
Wright State University
Sensor Data Streams Correlation Platform For Asthma Management, Vaikunth Sridharan
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Asthma is a high-burden chronic inflammatory disease with prevalence in children with twice the rate compared to adults. It can be improved by continuously monitoring patients and their environment using the Internet of Things (IoT) based devices. These sensor data streams so obtained are essential to comprehend multiple factors triggering asthma symptoms. In order to support physicians in exploring causal associations and finding actionable insights, a visualization system with a scalable cloud infrastructure that can process multimodal sensor data and Patient Generated Health Data (PGHD) is necessary. In this thesis, we describe a cloud-based asthma management and visualization platform that …
Building An Abstract-Syntax-Tree-Oriented Symbolic Execution Engine For Php Programs,
2018
Wright State University
Building An Abstract-Syntax-Tree-Oriented Symbolic Execution Engine For Php Programs, Jin Huang
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This thesis presents the design, implementation, and evaluation of an abstract-syntax-tree-oriented symbolic execution engine for the PHP programming language. As a symbolic execution engine, our system emulate the execution of a PHP program by assuming that all inputs are with symbolic rather than concrete values. While our system inherits the basic definition of symbolic execution, it fundamentally differs from existing symbolic execution implementations that mainly leverage intermediate representation (IRs) to operate. Specifically, our system directly takes the abstract syntax tree (AST) of a program as input and subsequently interprets this AST. Performing symbolic execution using AST offers unique advantages. First, …
Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application,
2018
Talpiot College of Education, Holon, Israel; Darca School, Bat Yam, Israel; and Bnei Akiva School, Holon, Israel
Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck
Journal of Social, Behavioral, and Health Sciences
Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning …
