Computer Engineering Commons^™

Uncovering Exceptional Predictions Using Exploratory Analysis Of Second Stage Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Nowadays, algorithmic systems for making decisions are widely used to facilitate decisions in a variety of fields such as medicine, banking, applying for universities or network security. However, many machine learning algorithms are well-known for their complex mathematical internal workings which turn them into black boxes and makes their decision-making process usually difficult to understand even for experts. In this thesis, we try to develop a methodology to explain why a certain exceptional machine learned decision was made incorrectly by using the interpretability of the decision tree classifier. Our approach can provide insights about potential flaws in feature definition or …

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.

Data Driven Discovery Of Materials Properties., Fadoua Khmaissia

Electronic Theses and Dissertations

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning …

Bayesian Optimization For Refining Object Proposals, Anthony D. Rhodes, Jordan Witte, Melanie Mitchell, Bruno Jedynak

Computer Science Faculty Publications and Presentations

We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal’s offset distance from a target object. Online, this model is used in a Bayesian active search to …

Using Mediawiki, The Semantic Mediawiki Extension And Relfinder For Heritage Data Management: A Rock Art Case Study, Robert A. Haubt

Staff Scholarship - Australia & Dubai

This paper looks at the usefulness of the MediaWiki and relevant wiki extensions for rock art data management and research. The paper follows a framework provided by the Global Rock Art Database www.rockartdatabase.com and examines a new database model for a semantic data management approach. Through practical examples it explores the usefulness of wikis in the data management life cycle from data planning to collection, storage and dissemination and examines interconnections within each phase of the cycle. The paper proposes a combined use of the Semantic MediaWiki extension and the RelFinder for visualization of semantic relationships, which could assist researchers …

Hoeffding Tree Algorithms For Anomaly Detection In Streaming Datasets: A Survey, Asmah Muallem, Sachin Shetty, Jan W. Pan, Juan Zhao, Biswajit Biswal

Computational Modeling & Simulation Engineering Faculty Publications

This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions …

A Comparison Of Feature Extraction Techniques For Malware Analysis, Mohammad Imran, Muhammad Tanvir Afzal, Muhammad Abdul Qadir

Turkish Journal of Electrical Engineering and Computer Sciences

The manifold growth of malware in recent years has resulted in extensive research being conducted in the domain of malware analysis and detection, and theories from a wide variety of scientific knowledge domains have been applied to solve this problem. The algorithms from the machine learning paradigm have been particularly explored, and many feature extraction methods have been proposed in the literature for representing malware as feature vectors to be used in machine learning algorithms. In this paper we present a comparison of several feature extraction techniques by first applying them on system call logs of real malware, and then …

Late Fusion Of Facial Dynamics For Automatic Expression Recognition, Alessandra Bandrabur, Laura Florea, Cornel Florea, Matei Mancas

Turkish Journal of Electrical Engineering and Computer Sciences

Installment of a facial expression is associated with contractions and extensions of specific facial muscles. Noting that expression is about changes, we present a model for expression classification based on facial landmarks dynamics. Our model isolates the trajectory of facial fiducial points by wrapping them up in relevant features and discriminating among various alternatives with a machine learning classification system. The used features are geometric and temporal-based and the classification system is represented by a late fusion framework that combines several neural networks with binary responses. The proposed method is robust, being able to handle complex expression classes.

Psychophysiological Analysis Of A Pedagogical Agent And Robotic Peer For Individuals With Autism Spectrum Disorders., Mohammad Nasser Saadatzi

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, …

Analog Spiking Neuromorphic Circuits And Systems For Brain- And Nanotechnology-Inspired Cognitive Computing, Xinyu Wu

Boise State University Theses and Dissertations

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves …

Learning In The Real World: Constraints On Cost, Space, And Privacy, Matt J. Kusner

McKelvey School of Engineering Graduate Student Theses & Dissertations

The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand.

However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model …

Subspace Methods For Portfolio Design, Onur Yilmaz

Dissertations

Financial signal processing (FSP) is one of the emerging areas in the field of signal processing. It is comprised of mathematical finance and signal processing. Signal processing engineers consider speech, image, video, and price of a stock as signals of interest for the given application. The information that they will infer from raw data is different for each application. Financial engineers develop new solutions for financial problems using their knowledge base in signal processing. The goal of financial engineers is to process the harvested financial signal to get meaningful information for the purpose.

Designing investment portfolios have always been at …

Acoustic Sequences In Non-Human Animals: A Tutorial Review And Prospectus, Arik Kershenbaum, Daniel T. Blumstein, Marie A. Roch, Çağlar Akçay, Gregory Backus, Mark A. Bee, Kirsten Bohn, Yan Cao, Gerald Carter, Cristiane Cäsar, Michael Coen, Stacy L. Deruiter, Laurance Doyle, Shimon Edelman, Ramon Ferreri Cancho, Todd M. Freeberg, Ellen C. Garland, Morgan Gustison, Heidi E. Harley, Chloé Huetz, Melissa Hughes, Julia Hyland Bruno, Amiyaal Ilany, Dezhe Z. Jin, Michael T. Johnson, Chenghui Ju, Jeremy Karnowski, Bernard Lohr, Marta B. Manser, Brenda Mccowan, Eduardo Mercado Iii, Peter M. Narins, Alex Piel, Megan Rice, Roberta Salmi, Kazutoshi Sasahara, Laela Sayigh, Yu Shiu, Charles Taylor, Edgar E. Vallejo, Sara Waller, Veronica Zamora Gutierrez

Electrical and Computer Engineering Faculty Research and Publications

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our …

Determining Unique Agents By Evaluating Web Form Interaction, Ben Cooley

College of Graduate Studies: Theses & Dissertations

Because of the inherent risks in today’s online activities, it becomes imperative to identify a malicious user masquerading as someone else. Incorporating biometric analysis enhances the confidence of authenticating valid users over the Internet while providing additional layers of security with no hindrance to the end user. Through the analysis of traffic patterns and HTTP Header analysis, the detection and early refusal of robot agents plays a great role in reducing fraudulent login attempts.

Direct L2 Support Vector Machine, Ljiljana Zigic

Theses and Dissertations

This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative.

Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art …

Removal Of Impulse Noise In Digital Images With Na\"Ive Bayes Classifier Method, Cafer Budak, Mustafa Türk, Abdullah Toprak

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Feature Selection For Movie Recommendation, Zehra Çataltepe, Mahi̇ye Uluyağmur, Esengül Tayfur

Turkish Journal of Electrical Engineering and Computer Sciences

TV users have an abundance of different movies they could choose from, and with the quantity and quality of data available both on user behavior and content, better recommenders are possible. In this paper, we evaluate and combine different content-based and collaborative recommendation methods for a Turkish movie recommendation system. Our recommendation methods can make use of user behavior, different types of content features, and other users' behavior to predict movie ratings. We gather different types of data on movies, such as the description, actors, directors, year, and genre. We use natural language processing methods to convert the Turkish movie …

A Mapreduce-Based Distributed Svm Algorithm For Binary Classification, Ferhat Özgür Çatak, Mehmet Erdal Balaban

Turkish Journal of Electrical Engineering and Computer Sciences

Although the support vector machine (SVM) algorithm has a high generalization property for classifying unseen examples after the training phase~and a small loss value, the algorithm is not suitable for real-life classification and regression problems. SVMs cannot solve hundreds of thousands of examples in a training dataset. In previous studies on distributed machine-learning algorithms, the SVM was trained in a costly and preconfigured computer environment. In this research, we present a MapReduce-based distributed parallel SVM training algorithm for binary classification problems. This work shows how to distribute optimization problems over cloud computing systems with the MapReduce technique. In the second …

An Investigation Into Off-Link Ipv6 Host Enumeration Search Methods, Clinton Carpene

Theses: Doctorates and Masters

This research investigated search methods for enumerating networked devices on off-link 64 bit Internet Protocol version 6 (IPv6) subnetworks. IPv6 host enumeration is an emerging research area involving strategies to enable detection of networked devices on IPv6 networks. Host enumeration is an integral component in vulnerability assessments (VAs), and can be used to strengthen the security profile of a system. Recently, host enumeration has been applied to Internet-wide VAs in an effort to detect devices that are vulnerable to specific threats. These host enumeration exercises rely on the fact that the existing Internet Protocol version 4 (IPv4) can be exhaustively …

How Can We Build A Moral Robot?, Kristen E. Clark

Capstones

Artificial intelligence is already starting to drive our cars and make choices that affect the world economy. One day soon, we’ll have robots that can take care of our sick and elderly, and even rescue us in rescue us in emergencies. But as robots start to make decisions that matter—it’s raising questions that go far beyond engineering. We’re stating to think about ethics.

Bertram Malle and Matthias Scheutz are part of a team funded by the department of defense. It's their job to answer a question that seems straight out of a sci-fi novel: How can we build a moral …

Distributed Approach For Peptide Identification, Naga V K Abhinav Vedanbhatla

Masters Theses & Specialist Projects

A crucial step in protein identification is peptide identification. The Peptide Spectrum Match (PSM) information set is enormous. Hence, it is a time-consuming procedure to work on a single machine. PSMs are situated by a cross connection, a factual score, or a probability that the match between the trial and speculative is right and original. This procedure takes quite a while to execute. So, there is demand for enhancement of the performance to handle extensive peptide information sets. Development of appropriate distributed frameworks are expected to lessen the processing time.

The designed framework uses a peptide handling algorithm named C-Ranker, …

An Understanding Of Student Satisfaction, Lorraine Sweeney

Dissertations

Retention is a challenge for all third level institutions and retention rates remain higher than colleges would like them to be, this has intensified in recent years as participants in higher education has increased and diversified. Third level institutions which would not only benefit from increased fees but also through low cost word of mouth promotion and an enhanced reputation. As such, an important concern for colleges is retaining students and understanding the reasons why students may choose to leave a program. While student satisfaction and retention is a well researched topic there remains questions to be answered in terms …

Detecting, Modeling, And Predicting User Temporal Intention, Hany M. Salaheldeen

Computer Science Theses & Dissertations

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, …

Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh

Dissertations

This thesis compares the ability of an implementation of Defeasible Reasoning (via Argumentation Theory) to model a construct (mental workload) with Machine Learning. In order to perform this comparison a defeasible reasoning system was designed and implemented in software. This software was used to elicit a knowledge base from an expert in an experiment which was then compared with machine learning. The central findings of this thesis were that the knowledge based approach was better at predicting an objective performance measure, time, than machine learning. However, machine learning was better equiped to identify another object measure task membership. The knowledge …

Energy Cost Forecasting For Event Venues, Katarina Grolinger, Andrea Zagar, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning …

Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong

Research Collection School Of Computing and Information Systems

The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. …

Avatar Captcha : Telling Computers And Humans Apart Via Face Classification And Mouse Dynamics., Darryl Felix D’Souza

Electronic Theses and Dissertations

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human …

Temporal Contextual Descriptors And Applications To Emotion Analysis., Haythem Balti

Electronic Theses and Dissertations

The current trends in technology suggest that the next generation of services and devices allows smarter customization and automatic context recognition. Computers learn the behavior of the users and can offer them customized services depending on the context, location, and preferences. One of the most important challenges in human-machine interaction is the proper understanding of human emotions by machines and automated systems. In the recent years, the progress made in machine learning and pattern recognition led to the development of algorithms that are able to learn the detection and identification of human emotions from experience. These algorithms use different modalities …

Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong

Research Collection School Of Computing and Information Systems

Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of …

Model-Free Method Of Reinforcement Learning For Visual Tasks, Jeff S. Soldate, Jonghoon Jin, Eugenio Culurciello

The Summer Undergraduate Research Fellowship (SURF) Symposium

There has been success in recent years for neural networks in applications requiring high level intelligence such as categorization and assessment. In this work, we present a neural network model to learn control policies using reinforcement learning. It takes a raw pixel representation of the current state and outputs an approximation of a Q value function made with a neural network that represents the expected reward for each possible state-action pair. The action is chosen an \epsilon-greedy policy, choosing the highest expected reward with a small chance of random action. We used gradient descent to update the weights and biases …