Speech Interfaces And Pilot Performance: A Meta-Analysis, 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, 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, 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, 2018 University of Nebraska at Omaha
Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler
Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, 2018 Department of Compter Science
Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler
Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice E. Hall
Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, 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, 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, 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, 2018 San Jose State University
Deep Visual Recommendation System, Raksha Sunil
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, 2018 San Jose State University
Virtual Robot Climbing Using Reinforcement Learning, Ujjawal Garg
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, 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, 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, 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), 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, 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, 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, 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
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, 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 ...