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Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks 2019 Southern Methodist University

Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks

SMU Data Science Review

In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that ...


Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard 2019 Yale University

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim ...


A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas 2019 Southern Methodist University

A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas

SMU Data Science Review

There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of ...


Improving Electronic Health Record Note Comprehension With Noteaid: Randomized Trial Of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers, John P. Lalor, Beverly Woolf, Hong Yu 2019 University of Massachusetts Amherst

Improving Electronic Health Record Note Comprehension With Noteaid: Randomized Trial Of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers, John P. Lalor, Beverly Woolf, Hong Yu

Open Access Articles

BACKGROUND: Patient portals are becoming more common, and with them, the ability of patients to access their personal electronic health records (EHRs). EHRs, in particular the free-text EHR notes, often contain medical jargon and terms that are difficult for laypersons to understand. There are many Web-based resources for learning more about particular diseases or conditions, including systems that directly link to lay definitions or educational materials for medical concepts.

OBJECTIVE: Our goal is to determine whether use of one such tool, NoteAid, leads to higher EHR note comprehension ability. We use a new EHR note comprehension assessment tool instead of ...


The Benefits Of Artificial Intelligence In Cybersecurity, Ricardo Calderon 2019 La Salle University

The Benefits Of Artificial Intelligence In Cybersecurity, Ricardo Calderon

Economic Crime Forensics Capstones

Cyberthreats have increased extensively during the last decade. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and botnets are almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques are able to mine data to detect botnets’ sources. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a ...


An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari 2019 Dublin Institute of Technology

An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari

Dissertations

One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating ...


Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, Nicholas Gardiner 2019 Edith Cowan University

Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, Nicholas Gardiner

Theses : Honours

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed.

This research project seeks to examine ...


Reimagining Medical Education In The Age Of Ai, Steven A. Wartman, C. Donald Combs 2019 Old Dominion University

Reimagining Medical Education In The Age Of Ai, Steven A. Wartman, C. Donald Combs

Modeling, Simulation & Visualization Engineering Faculty Publications

Available medical knowledge exceeds the organizing capacity of the human mind, yet medical education remains based on information acquisition and application. Complicating this information overload crisis among learners is the fact that physicians' skill sets now must include collaborating with and managing artificial intelligence (AI) applications that aggregate big data, generate diagnostic and treatment recommendations, and assign confidence ratings to those recommendations. Thus, an overhaul of medical school curricula is due and should focus on knowledge management (rather than information acquisition), effective use of AI, improved communication, and empathy cultivation.


Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat 2019 Old Dominion University

Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat

VMASC Publications

Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the ...


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 ...


Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice Hall 2019 University of Missouri, St. Louis

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

Communication and Media Faculty Works

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 ...


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 ...


Machine Learning Methods For Personalized Health Monitoring Using Wearable Sensors, Annamalai Natarajan 2019 University of Massachusetts Amherst

Machine Learning Methods For Personalized Health Monitoring Using Wearable Sensors, Annamalai Natarajan

Doctoral Dissertations

Mobile health is an emerging field that allows for real-time monitoring of individuals between routine clinical visits. Among others it makes it possible to remotely gather health signals, track disease progression and provide just-in-time interventions. Consumer grade wearable sensors can remotely gather health signals and other time series data. While wearable sensors can be readily deployed on individuals, there are significant challenges in converting raw sensor data into actionable insights. In this dissertation, we develop machine learning methods and models for personalized health monitoring using wearables. Specifically, we address three challenges that arise in these settings. First, data gathered from ...


Efficient Probabilistic Reasoning Using Partial State-Space Exploration, Luis Pineda 2019 University of Massachusetts Amherst

Efficient Probabilistic Reasoning Using Partial State-Space Exploration, Luis Pineda

Doctoral Dissertations

Planning, namely the ability of an autonomous agent to make decisions leading towards a certain goal, is one of the fundamental components of intelligent behavior. In the face of uncertainty, this problem is typically modeled as a Markov Decision Process (MDP). The MDP framework is highly expressive, and has been used in a variety of applications, such as mobile robots, flow assignment in heterogeneous networks, optimizing software in mobile phones, and aircraft collision avoidance. However, its wide adoption in real-world scenarios is still impaired by the complexity of solving large MDPs. Developing effective ways to tackle this complexity barrier is ...


Learning With Aggregate Data, TAO SUN 2019 University of Massachusetts Amherst

Learning With Aggregate Data, Tao Sun

Doctoral Dissertations

Various real-world applications involve directly dealing with aggregate data. In this work, we study Learning with Aggregate Data from several perspectives and try to address their combinatorial challenges.

At first, we study the problem of learning in Collective Graphical Models (CGMs), where only noisy aggregate observations are available. Inference in CGMs is NP- hard and we proposed an approximate inference algorithm. By solving the inference problems, we are empowered to build large-scale bird migration models, and models for human mobility under the differential privacy setting.

Secondly, we consider problems given bags of instances and bag-level aggregate supervisions. Specifically, we study ...


Routing And Scheduling For A Last-Mile Transportation System, Hai WANG 2019 Singapore Management University

Routing And Scheduling For A Last-Mile Transportation System, Hai Wang

Research Collection School Of Information Systems

The last-mile problem concerns the provision of travel services from the nearest public transportation node to a passenger’s home or other destination. We study the operation of an emerging last-mile transportation system (LMTS) with batch demands that result from the arrival of groups of passengers who desire last-mile service at urban metro stations or bus stops. Routes and schedules are determined for a multivehicle fleet of delivery vehicles, with the objective of minimizing passenger waiting time and riding time. An exact mixed-integer programming (MIP) model for LMTS operations is presented first, which is difficult to solve optimally within acceptable ...


Emerging Roles Of Virtual Patients In The Age Of Ai, C. Donald Combs, P. Ford Combs 2019 Old Dominion University

Emerging Roles Of Virtual Patients In The Age Of Ai, C. Donald Combs, P. Ford Combs

Modeling, Simulation & Visualization Engineering Faculty Publications

Today's web-enabled and virtual approach to medical education is different from the 20th century's Flexner-dominated approach. Now, lectures get less emphasis and more emphasis is placed on learning via early clinical exposure, standardized patients, and other simulations. This article reviews literature on virtual patients (VPs) and their underlying virtual reality technology, examines VPs' potential through the example of psychiatric intake teaching, and identifies promises and perils posed by VP use in medical education.


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 ...


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 ...


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