Computer Sciences Commons^™

Optimization Of Neural Network Architecture For Classification Of Radar Jamming Fm Signals, Alberto Soto

Open Access Theses & Dissertations

Radar jamming signal classification is valuable when situational awareness of radar systems is sought out for timely deployment of electronic support measures. Our Thesis shows that artificial neural networks can be utilized for effective and efficient signal classification. The goal is to optimize an artificial Neural Network (NN) approach capable of distinguishing between two common radar waveforms, namely bandlimited white Gaussian jamming noise (BWGN) and the ubiquitous linearly frequency modulated (LFM) signal. This is made possible by creating a theoretical framework for NN architecture testing that leads to a high probability of detection (PD) and a low probability of false …

Decision Process In Mcdm With Large Number Of Criteria And Heterogeneous Risk Preferences, Jian Liu, Hong Kuan Zhao, Zhao Bin Li, Si Feng Liu

Electrical and Computer Engineering Faculty Research & Creative Works

A new decision process is proposed to address the challenge that a large number of criteria in the multi-criteria decision making (MCDM) problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion) …

Artificial Intelligence And Its Potential Adverse Impacts On The Philippine Economy, Krista Danielle Yu, Caesar Cororaton, Joel P. Ilao, Charibeth K. Cheng, Kathleen Aviso, Christina D. Cayamanda, Michael Angelo B. Promentilla, Ringgold P. Atienza, Roman Julio B. Infante, Raymond R. Tan

Angelo King Institute for Economic and Business Studies (AKI)

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017).

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …

An Introduction To The Theory And Applications Of Bayesian Networks, Anant Jaitha

CMC Senior Theses

Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. This paper discusses the theory and applications of …

2d Vector Map And Database Design For Indoor Assisted Navigation, Luciano Caraciolo Albuquerque

Dissertations and Theses

In this paper we implemented a 2D Vector Map, map editor and Database design intended to provide an efficient way to convert cad files from indoor environments to a set of vectors representing hallways, doors, exits, elevators, and other entities embedded in a floor plan, and save them in a database for use by other applications, such as assisted navigation for blind people.

A graphical application as developed in C++ to allow the user to input a CAD DXF file, process the file to automatically obtain nodes and edges, and save the nodes and edges to a database for posterior …

Return On Investment Of The Cftp Framework With And Without Risk Assessment, Anne Lim Lee

Walden Dissertations and Doctoral Studies

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk …

Cognition-Based Approaches For High-Precision Text Mining, George John Shannon

Doctoral Dissertations

"This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in.

Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital …

Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li

Electrical & Computer Engineering Faculty Publications

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …

Towards A Continuous Assessment Of Cognitive Workload For Smartphone Multitasking Users, Angel Jimenez-Molina, Hernan Lira

H-Workload 2017: Models and Applications (Works in Progress)

The intermeshing of Smartphone interactions and daily activities depletes the availability of cognitive resources. This excessive demand may lead to several undesirable cognitive states, which can be avoided by continuously assessing the user cognitive workload. Recently, many attempts have emerged to assess this workload by using psycho physiological signals. This paper provides evidence that it is possible to train models that accurately identify in short time windows such cognitive workload by processing heart rate and blood oxygen saturation signals. This assessment could be applied in Smartphone notification delivery, interface adaptations or cognitive capabilities evaluation.

Human Performance Modelling In Manufacturing: Mental Workload And Task Complexity, Maria Chiara Leva, Lorenzo Comberti, Micaela Demichela, Rebecca Duane

H-Workload 2017: Models and Applications (Works in Progress)

No abstract provided.

Distress And Worry As Mediators In The Relationship Between Psychosocial Risks And Upper Body Musculosketal Complaints In Highly Automated Manufacturing, Fiona Wixted, Leonard O'Sullivan

H-Workload 2017: Models and Applications (Works in Progress)

As a result of an upward trend in automation, the requirement for supervisory monitoring and consequently, cognitive demand has increased in automated manufacturing. The incidence of musculoskeletal disorders has also increased in the manufacturing sector. A model was developed based on survey data to test if distress and worry mediate the relationship between psychosocial factors (job control, cognitive demand, social isolation and skill discretion), stress states and upper body musculoskeletal complaints in highly automated manufacturing companies (n=235). Cognitive demand was shown to be related to higher distress in employees. The data raise the question about the link between job control …

Managing Operator Mental Workload With Standards Based Decision Support, Maurice Wilkins

H-Workload 2017: Models and Applications (Works in Progress)

H-Workload 2017: The first international symposium on human mental workload, Dublin Institute of Technology, Dublin, Ireland, June 28-30.

Smart Workload Balancing, Ferdinand Coster

H-Workload 2017: Models and Applications (Works in Progress)

The cognitive workload of operators working with automated systems should neither be too high nor too low. A static level of automation is unable to cope with systems that produce large fluctuations in cognitive workload, therefore a method for adaptive automation is proposed that could balance workload by intelligently choosing what to automate and when. To this end the concept of the Cognitive Workload Value factor is introduced, which takes into account both workload and situation awareness. This initial work introduces a possible framework for categorizing and using different workload and situation awareness measures.

Facing Human Workload: The Resilient Ego: A Psychoanalytic Point Of View, Glauco Maria Genga, Maria Gabriella Pediconii

H-Workload 2017: Models and Applications (Works in Progress)

The paper aims to show new connections among Human Factors, Human Workload and Resilience. We intend: 1) to highlight the role of subject in facing the human workload, inflected as demanding tasks and emergency situations; 2) to show how psychoanalysis can provide novel insights, not only into human errors, but also into human resilience. They have a common denominator, at least in part: the role of subjective contributions even in demanding situations. Human workload includes a work for satisfaction. We recall also the case study of US Airways Flight 1549 water landing (the so called “Miracle on the Hudson”), which …

A Validated Description Of How Crew Manage Flight Operations For Two-Pilot And Reduced Crew Operations, Nick Mcdonnell, Alison Kay, Margaret Ryan, Rabea Morrison, Rolf Zon

H-Workload 2017: Models and Applications (Works in Progress)

This research provides a rich validated description of how crew manage workload for both two-pilot and reduced crew operations. It outlines flight operations modelling, operational narratives, requirements and scenarios validated with expert advisers from the EU-FP7 ACROSS Project. The crew are considered to be the managers of the operation who receive integrated technical support to help them manage flight operations across of three configurations i.e. 1) standard two-crew configuration, 2) reduced crew under normal operations 3) reduced-crew under non-normal operations developed within the FP7 EU-funded ACROSS (Advanced Cockpit for the Reduction Of Stress and Workload) project.

Petrochemical Plant Console Operator Workload:The Issues, David A. Strobhar

H-Workload 2017: Models and Applications (Works in Progress)

The console operators of certain petrochemical processes must maintain high levels of performance during process upsets or endanger personnel safety and the environment. Mismanagement of an upset can result in explosions, fires, and the release of hazardous chemicals to the environment. The change in workload from steady state to upset operation is significant, with alarms and control changes that are of an order of magnitude. This paper describes the state of console activity in process plants, particularly the increase with key upsets. Quantitative data on the nature of the console operator’s position, its workload during normal operation, and the requirements …

A Day In The Life Of A Sim: Making Meaning Of Video Game Avatars And Behaviors, Jessica Stark

Antioch University Dissertations & Theses

With video game usage--and criticism on its activity--on the rise, it may be helpful for the psychological community to understand what it actually means to play video games, and what the lived experience entails. This qualitative, phenomenological study specifically explores user behaviors and decisions in the simulated life video game, The Sims. Ten participants completed one- to two-hour long semi-structured interviews, and the data was transcribed, organized into 1,988 codes, which were clustered into 30 categories, and from which six themes ultimately emerged. These resulting themes are: self-representation; past, present, and future; purpose for play; self-reflection; co-creation; and familiarity. The …

Security Analytics: Using Deep Learning To Detect Cyber Attacks, Glenn M. Lambert Ii

UNF Graduate Theses and Dissertations

Security attacks are becoming more prevalent as cyber attackers exploit system vulnerabilities for financial gain. The resulting loss of revenue and reputation can have deleterious effects on governments and businesses alike. Signature recognition and anomaly detection are the most common security detection techniques in use today. These techniques provide a strong defense. However, they fall short of detecting complicated or sophisticated attacks. Recent literature suggests using security analytics to differentiate between normal and malicious user activities.

The goal of this research is to develop a repeatable process to detect cyber attacks that is fast, accurate, comprehensive, and scalable. A model …

Designing 2d Interfaces For 3d Gesture Retrieval Utilizing Deep Learning, Spencer Southard

UNF Graduate Theses and Dissertations

Gesture retrieval can be defined as the process of retrieving the correct meaning of the hand movement from a pre-assembled gesture dataset. The purpose of the research discussed here is to design and implement a gesture interface system that facilitates retrieval for an American Sign Language gesture set using a mobile device. The principal challenge discussed here will be the normalization of 2D gestures generated from the mobile device interface and the 3D gestures captured from video samples into a common data structure that can be utilized by deep learning networks. This thesis covers convolutional neural networks and auto encoders …

Representing And Inferring Mental Workload Via Defeasible Reasoning: A Comparison With The Nasa Task Load Index And The Workload Profile, Lucas Middeldorf Rizzo, Luca Longo

Conference papers

The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type …

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 …

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

McKelvey School of Engineering Graduate Student 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 …

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

School of Computing: 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 …

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

School of Computing: 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 …

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 …