Physical Sciences and Mathematics Commons^™

Rationality, Parapsychology, And Artificial Intelligence In Military And Intelligence Research By The United States Government In The Cold War, Guy M. Lomeo

Theses and Dissertations

A study analyzing the roles of rationality, parapsychology, and artificial intelligence in military and intelligence research by the United States Government in the Cold War. An examination of the methodology behind the decisions to pursue research in two fields that were initially considered irrational.

Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers

Military Cyber Affairs

Today’s defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyber Operations (DCO) is lagging. This article provides an introduction to machine learning concepts with a focus on the unique challenges to using machine learning for DCO. Traditional machine learning evaluation methods are challenged in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and sensitivity threshold selection …

Real-Time Online Chinese Character Recognition, Wenlong Zhang

Master's Projects

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network.

Argumentation For Knowledge Representation, Conflict Resolution, Defeasible Inference And Its Integration With Machine Learning, Luca Longo

Conference papers

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible …

Review Classification, Balraj Aujla

Computer Science and Software Engineering

The goal of this project is to find a way to analyze reviews and determine the sentiment of a review. It uses various machine learning techniques in order to achieve its goals such as SVMs and Naive Bayes. Overall the purpose is to learn many different machine learning techniques, determine which ones would be useful for the project, then compare the results. Research is the foremost goal of the project, and it is able to determine the better algorithm for review classification, naive bayes or an SVM. In addition, an SVM which actually gave review’s scores rather than just classifying …

Investigating High Speed Localization Microscopy Through Experimental Methods, Data Processing Methods, And Applications Of Localization Microscopy To Biological Questions, Andrew J. Nelson

Electronic Theses and Dissertations

Fluorescence Photoactivation Localization Microscopy(FPALM) and other super resolution localization microscopy techniques can resolve structures with nanoscale resolution. Unlike techniques of electron microscopy, they are also compatible with live cell and live animal studies, making FPALM and related techniques ideal for answering questions about the dynamic nature of molecular biology in living systems. Many processes in biology occur on rapid sub second time scales requiring the imaging technique to be capable of resolving these processes not just with a high enough spatial resolution, but with an appropriate temporal resolution. To that end, this Dissertation in part investigates high speed FPALM as …

An Agent-Based Approach To Human Migration Movement, Larry Lin, Kathleen M. Carley, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale.

Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the Generalized OP, the Arc OP, …

Validating Social Media Data For Automatic Persona Generation, Jisun An, Haewoon Kwak, Bernard J Jansen

Research Collection School Of Computing and Information Systems

Using personas during interactive design has considerable potential for product and content development. Unfortunately, personas have typically been a fairly static technique. In this research, we validate an approach for creating personas in real time, based on analysis of actual social media data in an effort to automate the generation of personas. We validate that social media data can be implemented as an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation that produces online digital content. Using the organization's YouTube channel, we collect demographic data, customer interactions, and topical …

Managing Egress Of Crowd During Infrastructure Disruption, Teck Hou Teng, Shih-Fen Cheng, Trong-Nghia Truong, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In a large indoor environment such as a sports arena or convention center, smooth egress of crowd after an event can be seriously affected if infrastructure such as elevators and escalators break down. In this paper, we propose a novel crowd simulator known as SIM-DISRUPT for simulating egress scenarios in non-emergency situations. To surface the impact of disrupted infrastructure on the egress of crowd, SIM-DISRUPT includes features that allow users to specify selective disruptions as well as strategies for controlling the distribution and egress choices of crowd. Using SIM-DISRUPT, we investigate effects of crowd distribution, egress choices and infrastructure disruptions …

Towards Building A Review Recommendation System That Trains Novices By Leveraging The Actions Of Experts, Shilpa Khanal

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find …

On Path Consistency For Binary Constraint Satisfaction Problems, Christopher G. Reeson

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Constraint satisfaction problems (CSPs) provide a flexible and powerful framework for modeling and solving many decision problems of practical importance. Consistency properties and the algorithms for enforcing them on a problem instance are at the heart of Constraint Processing and best distinguish this area from other areas concerned with the same combinatorial problems. In this thesis, we study path consistency (PC) and investigate several algorithms for enforcing it on binary finite CSPs. We also study algorithms for enforcing consistency properties that are related to PC but are stronger or weaker than PC.

We identify and correct errors in the literature …

Automatic Assessment Of Environmental Hazards For Fall Prevention Using Smart-Cameras, Jeffrey Kutchka

Graduate Theses and Dissertations

As technology advances in the field of Computer Vision, new applications will emerge. One device that has emerged is the smart-camera, a camera attached to an embedded system that can perform routines a regular camera could not, such as object or event detection. In this thesis we describe a smart-camera system we designed, implemented, and evaluated for fall prevention monitoring of at-risk people while in bed, whether it be for a hospital patient, nursing home resident, or at home elderly resident. The camera will give a nurse or caregiver environmental awareness of the at-risk person and notify them when that …

Zero++: Harnessing The Power Of Zero Appearances To Detect Anomalies In Large-Scale Data Sets, Guansong Pang, Kai Ming Ting, David Albrecht, Huidong Jin

Research Collection School Of Computing and Information Systems

This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data; whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequencybased algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric …

Lexicon Knowledge Extraction With Sentiment Polarity Computation, Zhaoxia Wang, Vincent Joo Chuan Tong, Pingcheng Ruan, Fang Li

Research Collection School Of Computing and Information Systems

Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value …

Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

Duplicate record, see https://ink.library.smu.edu.sg/sis_research/3271. The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the …

Indoor Scene Localization To Fight Sex Trafficking In Hotels, Abigail Stylianou

McKelvey School of Engineering Theses & Dissertations

Images are key to fighting sex trafficking. They are: (a) used to advertise for sex services,(b) shared among criminal networks, and (c) connect a person in an image to the place where the image was taken. This work explores the ability to link images to indoor places in order to support the investigation and prosecution of sex trafficking. We propose and develop a framework that includes a database of open-source information available on the Internet, a crowd-sourcing approach to gathering additional images, and explore a variety of matching approaches based both on hand-tuned features such as SIFT and learned features …

Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. Macarthur, Manuel Trujillo-Silva, Thomas Macgillivray, Chris Ripa, Peter A. Hancock

Keith Reid MacArthur

Detecting Anomalously Similar Entities In Unlabeled Data, Lisa D. Friedland

Doctoral Dissertations

In this work, the goal is to detect closely-linked entities within a data set. The entities of interest have a tie causing them to be similar, such as a shared origin or a channel of influence. Given a collection of people or other entities with their attributes or behavior, we identify unusually similar pairs, and we pose the question: Are these two people linked, or can their similarity be explained by chance? Computing similarities is a core operation in many domains, but two constraints differentiate our version of the problem. First, the score assigned to a pair should account for …

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …

Stochastic Network Design: Models And Scalable Algorithms, Xiaojian Wu

Doctoral Dissertations

Many natural and social phenomena occur in networks. Examples include the spread of information, ideas, and opinions through a social network, the propagation of an infectious disease among people, and the spread of species within an interconnected habitat network. The ability to modify a phenomenon towards some desired outcomes has widely recognized benefits to our society and the economy. The outcome of a phenomenon is largely determined by the topology or properties of its underlying network. A decision maker can take management actions to modify a network and, therefore, change the outcome of the phenomenon. A management action is an …

Learning From Pairwise Proximity Data, Hamid Dadkhahi

Doctoral Dissertations

In many areas of machine learning, the characterization of the input data is given by a form of proximity measure between data points. Examples of such representations are pairwise differences, pairwise distances, and pairwise comparisons. In this work, we investigate different learning problems on data represented in terms of such pairwise proximities. More specifically, we consider three problems: masking (feature selection) for dimensionality reduction, extension of the dimensionality reduction for time series, and online collaborative filtering. For each of these problems, we start with a form of pairwise proximity which is relevant in the problem at hand. We evaluate the …

Large Scale Data Mining For It Service Management, Chunqiu Zeng

FIU Electronic Theses and Dissertations

More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure.

Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automation operation are intertwined. Often the system administrators' roles are limited to helping triage tickets to the processing …

On The Promotion Of The Social Web Intelligence, Taraneh Khazaei

Electronic Thesis and Dissertation Repository

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and …

Formal Performance Guarantees For Behavior-Based Localization Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software.

Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two different missions, each using each method, demonstrating the accuracy …

Reducing Adaptation Latency For Multi-Concept Visual Perception In Outdoor Environments, Maggie Wigness, John G. Rogers, Luis Ernesto Navarro-Serment, Arne Suppe, Bruce A. Draper

Research Collection School Of Computing and Information Systems

Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires new or additional labeling to adapt visual perception. The time is takes for a human to label new data is what we call adaptation latency. High adaptation latency is not simply undesirable but may be infeasible for scenarios with limited labeling time and resources. In this paper, …

A Decomposition Method For Estimating Recursive Logit Based Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger

Research Collection School Of Computing and Information Systems

Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks …

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly. Once all data has been trained in …

Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari

Vijayan K. Asari

The initial framework for an electroencephalography (EEG) thought recognition software suite is developed, built, and tested. This suite is designed to recognize human thoughts and pair them to actions for controlling a robotic arm. Raw EEG brain activity data is collected using an Emotiv EPOC headset. The EEG data is processed through linear discriminant analysis (LDA), where an intended action is identified. The EEG classification suite is being developed to increase the number of distinct actions that can be identified compared to the Emotiv recognition software. The EEG classifier was able to correctly distinguish between two separate physical movements. Future …

Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration And Sensor Fusion - A Feasibility Study, Utsav Pardasani

Electronic Thesis and Dissertation Repository

Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery.

In spite of the potential for tracked ultrasound to improve many neurosurgical procedures, it …