Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education,
2019
Technological University Dublin
Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education, Karim Moustafa, Luca Longo
Conference papers
Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental …
On Cluster Robust Models,
2019
Claremont Graduate University
On Cluster Robust Models, José Bayoán Santiago Calderón
CGU Theses & Dissertations
Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …
Experimental Investigation On The Effects Of Website Aesthetics On User Performance In Different Virtual Tasks,
2019
Old Dominion University
Experimental Investigation On The Effects Of Website Aesthetics On User Performance In Different Virtual Tasks, Meinald T. Thielsch, Russell Haines, Leonie Flacke
Information Technology & Decision Sciences Faculty Publications
In Human-Computer Interaction research, the positive effect of aesthetics on users' subjective impressions and reactions is well-accepted. However, results regarding the influence of interface aesthetics on a user's individual performance as an objective outcome are very mixed, yet of urgent interest due to the proceeding of digitalization. In this web-based experiment (N = 331), the effect of interface aesthetics on individual performance considering three different types of tasks (search, creative, and transfer tasks) is investigated. The tasks were presented on an either aesthetic or unaesthetic website, which differed significantly in subjective aesthetics. Goal orientation (learning versus performance goals) was included …
Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey,
2019
Old Dominion University
Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey, Boming Huang, Yuziang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou
Information Technology & Decision Sciences Faculty Publications
Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and …
Computational Modeling Of Trust Factors Using Reinforcement Learning,
2019
Old Dominion University
Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …
Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions,
2019
Old Dominion University
Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …
Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data,
2019
University of Kentucky
Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data, Orhan Abar
Theses and Dissertations--Computer Science
Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of …
Learning To Map The Visual And Auditory World,
2019
University of Kentucky
Learning To Map The Visual And Auditory World, Tawfiq Salem
Theses and Dissertations--Computer Science
The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training …
Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning,
2019
University of Kentucky
Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal
Theses and Dissertations--Computer Science
Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …
Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models,
2019
Wilfrid Laurier University
Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich
Theses and Dissertations (Comprehensive)
Most of the world relies on ships for transportation, shipping, and tourism. Automatic Identification System messages are transmitted from ships and provide a wealth of positional data on these open ocean vessels. This data is being utilized to determine the optimal path for ships, as well as predicting where a ship may be going in the near future. It has only been in the past decade that Automatic Identification Systems (AIS) signals have been easily received with satellites (S-AIS) so there have been few studies that look at using available information and pairing it with the new abundance of ship …
Towards Secure And Fair Iiot-Enabled Supply Chain Management Via Blockchain-Based Smart Contracts,
2019
Wilfrid Laurier University
Towards Secure And Fair Iiot-Enabled Supply Chain Management Via Blockchain-Based Smart Contracts, Amal Eid Alahmadi
Theses and Dissertations (Comprehensive)
Integrating the Industrial Internet of Things (IIoT) into supply chain management enables flexible and efficient on-demand exchange of goods between merchants and suppliers. However, realizing a fair and transparent supply chain system remains a very challenging issue due to the lack of mutual trust among the suppliers and merchants. Furthermore, the current system often lacks the ability to transmit trade information to all participants in a timely manner, which is the most important element in supply chain management for the effective supply of goods between suppliers and the merchants. This thesis presents a blockchain-based supply chain management system in the …
Separability And Vertex Ordering Of Graphs,
2019
Wilfrid Laurier University
Separability And Vertex Ordering Of Graphs, Elizabeth Gorbonos
Theses and Dissertations (Comprehensive)
Many graph optimization problems, such as finding an optimal coloring, or a largest clique, can be solved by a divide-and-conquer approach. One such well-known technique is decomposition by clique separators where a graph is decomposed into special induced subgraphs along their clique separators. While the most common practice of this method employs minimal clique separators, in this work we study other variations as well. We strive to characterize their structure and in particular the bound on the number of atoms. In fact, we strengthen the known bounds for the general clique cutset decomposition and the minimal clique separator decomposition. Graph …
Explainable Neural Attention Recommender Systems,
2019
Wilfrid Laurier University
Explainable Neural Attention Recommender Systems, Omer Tal
Theses and Dissertations (Comprehensive)
Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented …
Generative Adversarial Networks For Online Visual Object Tracking Systems,
2019
Wilfrid Laurier University
Generative Adversarial Networks For Online Visual Object Tracking Systems, Ghsoun Zin
Theses and Dissertations (Comprehensive)
Object Tracking is one of the essential tasks in computer vision domain as it has numerous applications in various fields, such as human-computer interaction, video surveillance, augmented reality, and robotics. Object Tracking refers to the process of detecting and locating the target object in a series of frames in a video. The state-of-the-art for tracking-by-detection framework is typically made up of two steps to track the target object. The first step is drawing multiple samples near the target region of the previous frame. The second step is classifying each sample as either the target object or the background. Visual object …
Application Of Boolean Logic To Natural Language Complexity In Political Discourse,
2019
University of Kentucky
Application Of Boolean Logic To Natural Language Complexity In Political Discourse, Austin Taing
Theses and Dissertations--Computer Science
Press releases serve as a major influence on public opinion of a politician, since they are a primary means of communicating with the public and directing discussion. Thus, the public’s ability to digest them is an important factor for politicians to consider. This study employs several well-studied measures of linguistic complexity and proposes a new one to examine whether politicians change their language to become more or less difficult to parse in different situations. This study uses 27,500 press releases from the US Senate between 2004–2008 and examines election cycles and natural disasters, namely hurricanes, as situations where politicians’ language …
Law's Halo And The Moral Machine,
2019
Columbia Law School
Law's Halo And The Moral Machine, Bert I. Huang
Faculty Scholarship
How will we assess the morality of decisions made by artificial intelligence – and will our judgments be swayed by what the law says? Focusing on a moral dilemma in which a driverless car chooses to sacrifice its passenger to save more people, this study offers evidence that our moral intuitions can be influenced by the presence of the law.
Informed Trading And Cybersecurity Breaches,
2019
Columbia Law School
Informed Trading And Cybersecurity Breaches, Joshua Mitts, Eric L. Talley
Faculty Scholarship
Cybersecurity has become a significant concern in corporate and commercial settings, and for good reason: a threatened or realized cybersecurity breach can materially affect firm value for capital investors. This paper explores whether market arbitrageurs appear systematically to exploit advance knowledge of such vulnerabilities. We make use of a novel data set tracking cybersecurity breach announcements among public companies to study trading patterns in the derivatives market preceding the announcement of a breach. Using a matched sample of unaffected control firms, we find significant trading abnormalities for hacked targets, measured in terms of both open interest and volume. Our results …
Bridging Act-R And Project Malmo, Developing Models Of Behavior In Complex Environments,
2019
Bucknell University
Bridging Act-R And Project Malmo, Developing Models Of Behavior In Complex Environments, David M. Schwartz
Honors Theses
Cognitive architectures such as ACT-R provide a system for simulating the mind and human behavior. On their own they model decision making of an isolated agent. However, applying a cognitive architecture to a complex environment yields more interesting results about how people make decisions in more realistic scenarios. Furthermore, cognitive architectures enable researchers to study human behavior in dangerous tasks which cannot be tested because they would harm participants. Nonetheless, these architectures aren’t commonly applied to such environments as they don’t come with one. It is left to the researcher to develop a task environment for their model. The difficulty …
Modeling Of Laser-Induced Breakdown Spectroscopic Data Analysis By An Automatic Classifier,
2019
Delaware State University
Modeling Of Laser-Induced Breakdown Spectroscopic Data Analysis By An Automatic Classifier, David D. Pokrajac, Poopalasingam Sivakumar, Yuriy Markushin, Daniela Milovic, Gary Holness, Jinjie Liu, Noureddine Melikechi, Mukti Rana
Computer Science
Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during …
Capturing And Measuring Thematic Relatedness,
2019
IBM Ireland
Capturing And Measuring Thematic Relatedness, Magdalena Kacmajor, John D. Kelleher
Articles
In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In further experiments, we use these datasets to perform a comprehensive analysis of the performance of an extensive sample of computational models of semantic relatedness, classified according to the sources of information they exploit. We report models that are best at each of the two dimensions of semantic relatedness and those that achieve a good balance between the …
