Physical Sciences and Mathematics Commons^™

Big Networks: Analysis And Optimal Control, Hung The Nguyen

Theses and Dissertations

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world …

Novel Support Vector Machines For Diverse Learning Paradigms, Gabriella A. Melki

Theses and Dissertations

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification.

Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity.

Under the multi-instance paradigm, …

Expanding The Artificial Intelligence-Data Protection Debate, Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

Leveraging Artificial Intelligence To Improve Provider Documentation In Patient Medical Records, Evangeline C. Ozurigbo

Walden Dissertations and Doctoral Studies

Clinical documentation is at the center of a patient's medical record; this record contains all the information applicable to the care a patient receives in the hospital. The practice problem addressed in this project was the lack of clear, consistent, accurate, and complete patient medical records in a pediatric hospital. Although the occurrence of incomplete medical records has been a known issue for the project hospital, the issue was further intensified following the implementation of the 10th revision of International Classification of Diseases (ICD-10) standard for documentation, which resulted in gaps in provider documentation that needed to be filled. Based …

The Use Of Machine Learning To Detect Suckling In Pre-Weaned Calves, Sukumar Katamreddy

Theses

The weaning of cattle is a process which is known to be labour intensive and to have stressful effects on both cow and calf Common methods used in the weaning process include the temporary removal of a mother from the calf and manual observation and intervention. Early and speedy weaning is known to have a number of benefits, including health benefits for both cow and calf, additional weight gains for the calves as well as reduced labour and feed requirements. The process known as Two-Stage Weaning is recognised to be an effective low-stress approach to weaning in which the calf …

Multiclass Classification Of Risk Factors For Cervical Cancer Using Artificial Neural Networks, Abdullah Al Mamun

Electronic Theses and Dissertations

World Health Organization statistics show that cervical cancer is the fourth most frequent cancer in women with an estimated 530,000 new cases in 2012. Cervical cancer diagnosis typically involves liquid-based cytology (LBC) followed by a pathologist review. The accuracy of decision is therefore highly influenced by the expert’s skills and experience, resulting in relatively high false positive and/or false negative rates. Moreover, given the fact that the data being analyzed is highly dimensional, same reviewer’s decision is inherently affected by inconsistencies in interpreting the data. In this study, we use an Artificial Neural Network based model that aims to considerably …

Identification Of Streptococcus Pyogenes Using Raman Spectroscopy, Ehsan Majidi

Wayne State University Dissertations

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman …

A Study Of Neural Networks For The Quantum Many-Body Problem, Liam B. Schramm

Senior Projects Spring 2018

One of the fundamental problems in analytically approaching the quantum many-body problem is that the amount of information needed to describe a quantum state. As the number of particles in a system grows, the amount of information needed for a full description of the system increases exponentially. A great deal of work then has gone into finding efficient approximate representations of these systems. Among the most popular techniques are Tensor Networks and Quantum Monte Carlo methods. However, one new method with a number of promising theoretical guarantees is the Neural Quantum State. This method is an adaptation of the Restricted …

Development Of An Electronic Nose For Olfactory System Modelling Using Artificial Neural Network, Proceso L. Fernandez Jr, Mary Anne Sy Roa

Department of Information Systems & Computer Science Faculty Publications

Electronic nose (e-nose) devices have received considerable attention in the field of sensor technology because of their many potential uses such as in identification of toxic wastes, monitoring air quality, examining odors in infected wounds and in inspection of food. Notwithstanding the vast amount of literature on the usage of e-noses for specific purposes, the technology originally and ultimately aims to mimic the capability of mammals to discriminate odors from all sorts of objects. This study demonstrates the theoretical and practical feasibility of designing an e-nose towards general odor classification. A multi-sensor array hardware unit was carefully constructed for data …

Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum

Electronic Theses and Dissertations

This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.

Emotion In The Common Model Of Cognition, Othalia Larue, Robert West, Paul Rosenbloom, Christopher L. Dancy, Alexei V. Samsonovich, Dean Petters, Ion Juvina

Faculty Journal Articles

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition.

Towards A Physio-Cognitive Model Of Slow-Breathing, Chris Dancy

Faculty Conference Papers and Presentations

How may controlled breathing be beneficial, or detrimental to behavior? Computational process models are useful to specify the potential mechanisms that lead to behavioral adaptation during different breathing exercises. We present a physio-cognitive model of slow breathing implemented within a hybrid cognitive architecture, ACT-R/Φ. Comparisons to data from an experiment indicate that the physiological mechanisms are operating in a manner that is consistent with actual human function. The presented computational model provides predictions of ways that controlled breathing interacts with mechanisms of arousal to mediate cognitive behavior. The increasing use of breathing techniques to counteract effects of stressors makes it …

Towards A Physio-Cognitive Model Of The Exploration Exploitation Trade-Off., David M. Schwartz, Christopher L. Dancy

Faculty Conference Papers and Presentations

Managing the exploration vs exploitation trade-off is an important part of our everyday lives. It occurs in minor decisions such as choosing what music to listen to as well as major decisions, such as picking a research direction to pursue. The dilemma is the same despite the context: does one exploit the environment, using current knowledge to acquire a satisfactory solution, or explore other options and potentially find a better answer. An accurate cognitive model must be able to handle this trade-off because of the importance it plays in our lives. We are developing physio-cognitive models to better understand how …

Simulating Human-Ai Collaboration With Act-R And Project Malmo, Zachary M. Brill, Christopher L. Dancy

Faculty Conference Papers and Presentations

We use the ACT-R cognitive architecture (Anderson, 2007) to explore human-AI collaboration. Computational models of human and AI behavior, and their interaction, allow for more effective development of collaborative artificial intelligent agents. With these computational models and simulations, one may be better equipped to predict the situations in which certain classes of intelligent agents may be more suited to collaborate with people. One can more tractably understand and predict how different AI agents affect task behavior in these situations. To simulate human-AI collaboration, we are developing ACT-R models that work with more traditional AI agents to solve a task in …

Towards Using A Physio-Cognitive Model In Tutoring For Psychomotor Tasks., Jong W. Kim, Chris Dancy, Robert A. Sottilare

Faculty Conference Papers and Presentations

We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through …

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

Electronic Theses and Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

Electronic Theses and Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …

Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini

Electronic Theses and Dissertations

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social …

The Impact Of Cost On Feature Selection For Classifiers, Richard Clyde Mccrae

CCE Theses and Dissertations

Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic problem is formulated as a binary classification task in which trained classifiers make predictions based on a set of input features. In diagnosis, these features are typically procedures or tests with associated costs. The cost of applying a trained classifier for diagnosis may be estimated as the total cost of obtaining values for the features that serve as inputs for the classifier. Obtaining classifiers based on a low cost set of input features with acceptable classification accuracy is of interest to practitioners and researchers. What makes this …

Dictionary Learning With Structured Noise, Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Y. Edward Chang

Research Collection School Of Computing and Information Systems

Recently, lots of dictionary learning methods have been proposed and successfully applied. However, many of them assume that the noise in data is drawn from Gaussian or Laplacian distribution and therefore they typically adopt the 2 or 1 norm to characterize these two kinds of noise, respectively. Since this assumption is inconsistent with the real cases, the performance of these methods is limited. In this paper, we propose a novel dictionary learning with structured noise (DLSN) method for handling noisy data. We decompose the original data into three parts: clean data, structured noise, and Gaussian noise, and then characterize them …

Pseudorehearsal In Actor-Critic Agents With Neural Network Function Approximation, Vladimir Marochko, Leonard Johard, Manuel Mazzara, Luca Longo

Articles

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.

Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi

Electronic Theses and Dissertations

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, …

Artificial Neural Network (Ann) In A Small Dataset To Determine Neutrality In The Pronunciation Of English As A Foreign Language In Filipino Call Center Agents, Proceso L. Fernandez Jr, Rey Benjamin M. Baquirin

Department of Information Systems & Computer Science Faculty Publications

Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an ANN with a small dataset to accurately classify whether Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were …

Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth

Computer Science Faculty Publications

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller

Computer Science Faculty Publications

In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]

Bibliography For Interstices 2018: Beyond Human: Emotion And Ai, Kristin Laughtin-Dunker

Library Displays and Bibliographies

An annotated list of materials in the Leatherby Libraries to accompany the Interstices 2018: Beyond Human: Emotion and AI event held at Chapman University in February 2018. The event featured Lisa Joy, co-creator and executive producer of HBO’s Emmy winning hit series Westworld, Jon Gratch, Director for Virtual Human Research at the University of Southern California’s (USC) Institute for Creative Technologies and Caroline Bainbridge, a Professor of Psychoanalysis and Culture in the Department of Media, Culture and Language at the University of Roehampton London. The Leatherby Libraries also hosted two book club discussions of The Positronic …

Evaluating Flexibility Metrics On Simple Temporal Networks With Reinforcement Learning, Hamzah I. Khan

HMC Senior Theses

Simple Temporal Networks (STNs) were introduced by Tsamardinos (2002) as a means of describing graphically the temporal constraints for scheduling problems. Since then, many variations on the concept have been used to develop and analyze algorithms for multi-agent robotic scheduling problems. Many of these algorithms for STNs utilize a flexibility metric, which measures the slack remaining in an STN under execution. Various metrics have been proposed by Hunsberger (2002); Wilson et al. (2014); Lloyd et al. (2018). This thesis explores how adequately these metrics convey the desired information by using them to build a reward function in a reinforcement learning …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Intelligent And Secure Underwater Acoustic Communication Networks, Chaofeng Wang

Dissertations, Master's Theses and Master's Reports

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions.

First, a RL-based algorithm is developed for adaptive transmission in …