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Speech Interfaces And Pilot Performance: A Meta-Analysis, Kenneth A. Ward 2019 Embry-Riddle Aeronautical University

Speech Interfaces And Pilot Performance: A Meta-Analysis, Kenneth A. Ward

International Journal of Aviation, Aeronautics, and Aerospace

As the aviation industry modernizes, new technology and interfaces must support growing aircraft complexity without increasing pilot workload. Natural language processing presents just such a simple and intuitive interface, yet the performance implications for use by pilots remain unknown. A meta-analysis was conducted to understand performance effects of using speech and voice interfaces in a series of pilot task analogs. The inclusion criteria selected studies that involved participants performing a demanding primary task, such as driving, while interacting with a vehicle system to enter numbers, dial radios, or enter a navigation destination. Compared to manual system interfaces, voice interfaces reduced ...


Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, Matthew Peacock 2019 Edith Cowan University

Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, Matthew Peacock

Theses: Doctorates and Masters

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet ...


Learning To Grasp In Unstructured Environments With Deep Convolutional Neural Networks Using A Baxter Research Robot, Shehan Caldera 2019 Edith Cowan University

Learning To Grasp In Unstructured Environments With Deep Convolutional Neural Networks Using A Baxter Research Robot, Shehan Caldera

Theses: Doctorates and Masters

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and ...


Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler 2018 University of Nebraska at Omaha

Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler

Yuliya Lierler

Answer set programming is a popular constraint programming paradigm that has seen wide use across various industry applications. However, logic programs under answer set semantics often require careful design and nontrivial expertise from a programmer to obtain satisfactory solving times. In order to reduce this burden on a software engineer we propose an automated rewriting technique for non-ground logic programs that we implement in a system Projector. We conduct rigorous experimental analysis, which shows that applying system Projector to a logic program can improve its performance, even after significant human-performed optimizations.


Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler 2018 Department of Compter Science

Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper claims the correctness   of a number of interesting program rewritings. Notably, they  assume  programs with variables and  such important language features as choice, disjunction, and aggregates. We showcase the utility of some considered rewritings  by using ...


Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice E. Hall 2018 Selected Works

Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice E. Hall

Alice Hall

This study investigated identification and parasocial relationships (PSRs) with media characters by examining viewers’ responses to the movie Star Wars: The Force Awakens through an online survey of 113 audience members who saw the film in a theater within a month of its release. Participants reported stronger PSR and identification with the more familiar characters from the first trilogy than with the new characters introduced in the film, although the association with identification was limited to older participants. Star Wars fanship was associated with identification and PSR for old and new characters. Familiarity with the earlier films was associated with ...


Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan 2018 Louisiana State University and Agricultural and Mechanical College

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or ...


Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. McCulloch 2018 University of New Orleans

Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch

University of New Orleans Theses and Dissertations

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It ...


Iterated Belief Revision Under Resource Constraints: Logic As Geometry, Dan P. Guralnik, Daniel E. Koditschek 2018 University of Pennsylvania

Iterated Belief Revision Under Resource Constraints: Logic As Geometry, Dan P. Guralnik, Daniel E. Koditschek

Departmental Papers (ESE)

We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots.

The proposed memory architecture---called the universal memory architecture (UMA)---maintains an epistemic state in the form of a system of default rules similar to those studied by Pearl and by Goldszmidt and Pearl (systems Z and Z+). A duality between the category of UMA representations and the category of the corresponding model spaces, extending the Sageev-Roller duality between discrete poc sets and discrete median algebras provides a two-way dictionary from inference to geometry, leading to immense savings in computation, at ...


Deep Visual Recommendation System, Raksha Sunil 2018 San Jose State University

Deep Visual Recommendation System, Raksha Sunil

Master's Projects

Recommendation system is a filtering system that predicts ratings or preferences that a user might have. Recommendation system is an evolved form of our trivial information retrieval systems. In this paper, we present a technique to solve new item cold start problem. New item cold start problem occurs when a new item is added to a shopping website like Amazon.com. There is no metadata for this item, no ratings and no reviews because it’s a new item in the system. Absence of data results in no recommendation or bad recommendations. Our approach to solve new item cold start ...


Virtual Robot Climbing Using Reinforcement Learning, Ujjawal Garg 2018 San Jose State University

Virtual Robot Climbing Using Reinforcement Learning, Ujjawal Garg

Master's Projects

Reinforcement Learning (RL) is a field of Artificial Intelligence that has gained a lot of attention in recent years. In this project, RL research was used to design and train an agent to climb and navigate through an environment with slopes. We compared and evaluated the performance of two state-of-the-art reinforcement learning algorithms for locomotion related tasks, Deep Deterministic Policy Gradients (DDPG) and Trust Region Policy Optimisation (TRPO). We observed that, on an average, training with TRPO was three times faster than DDPG, and also much more stable for the locomotion control tasks that we experimented. We conducted experiments and ...


Bots, Bias And Big Data: Artificial Intelligence, Algorithmic Bias And Disparate Impact Liability In Hiring Practices, McKenzie Raub 2018 University of Arkansas, Fayetteville

Bots, Bias And Big Data: Artificial Intelligence, Algorithmic Bias And Disparate Impact Liability In Hiring Practices, Mckenzie Raub

Arkansas Law Review

No abstract provided.


Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss 2018 University of Arkansas, Fayetteville

Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing ...


Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep VARAKANTHAM 2018 Singapore Management University

Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham

MITB Thought Leadership Series

BIKE-SHARING programmes face many of the issues encountered by their counterparts in the carsharing world. But in Singapore, there are a number of factors that have a unique impact on the industry. These include the regulatory structure and the significant fines for those companies who do not abide by these regulations. When this is combined with the competitive nature of the industry in one of the world's most dynamic cities, it becomes clear that first movers who leverage machine learning and prediction will come to dominate the industry


Augmented Reality In Foreign Language Education: A Review Of Empirical Studies (增强现实技术在外语教学中的应用:文献综述性研究), Shenglan Zhang 2018 Iowa State University

Augmented Reality In Foreign Language Education: A Review Of Empirical Studies (增强现实技术在外语教学中的应用:文献综述性研究), Shenglan Zhang

World Languages and Cultures Publications

This literature review examines how Augmented Reality (AR) has been used in foreign language learning. AR is a live view of reality that is augmented by computer-generated sound, image, or videos. It allows the user to interact with the real physical environment in an enhanced way. This study provides an overview of what AR is, its history, different definitions, and how it has been used in education in general. It summarizes how AR has been used in all aspects of foreign language education, including skill development (listening, speaking, reading and writing), vocabulary, grammar, culture, the aspect of affect in language ...


An Adaptive Memory-Based Reinforcement Learning Controller, Keith August Cissell 2018 Missouri State University - Springfield

An Adaptive Memory-Based Reinforcement Learning Controller, Keith August Cissell

MSU Graduate Theses

Recently, the use of autonomous robots for exploration has drastically expanded--largely due to innovations in both hardware technology and the development of new artificial intelligence methods. The wide variety of robotic agents and operating environments has led to the creation of many unique control strategies that cater to each specific agent and their goal within an environment. Most control strategies are single purpose, meaning they are built from the ground up for each given operation. Here we present a single, reinforcement learning control solution for autonomous exploration intended to work across multiple agent types, goals, and environments. The solution presented ...


Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien NGUYEN, Akshat KUMAR, Hoong Chuin LAU 2018 Singapore Management University

Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Information Systems

Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference ...


Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai 2018 California State University, San Bernardino

Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai

Electronic Theses, Projects, and Dissertations

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons ...


Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie 2018 Kennesaw State University

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 ...


Extraction Of Information Related To Adverse Drug Events From Electronic Health Record Notes: Design Of An End-To-End Model Based On Deep Learning, Fei Li, Weisong Liu, Hong Yu 2018 University of Massachusetts Medical School

Extraction Of Information Related To Adverse Drug Events From Electronic Health Record Notes: Design Of An End-To-End Model Based On Deep Learning, Fei Li, Weisong Liu, Hong Yu

Open Access Articles

BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.

OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and ...


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