Digital Commons Network^™

Methodology For Behavioral-Based Malware Analysis And Detection Using Random Projections And K-Nearest Neighbors Classifiers, Jozsef Hegedus, Yoan Miche, Alexander Ilin, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, a Two-Stage Methodology to Analyze and Detect Behavioral-Based Malware is Presented. in the First Stage, a Random Projection is Decreasing the Variable Dimensionality of the Problem and is Simultaneously Reducing the Computational Time of the Classification Task by Several Orders of Magnitude. in the Second Stage, a Modified K-Nearest Neighbors Classifier is Used with Virus total Labeling of the File Samples. This Methodology is Applied to a Large Number of File Samples Provided by F-Secure Corporation, for Which a Dynamic Feature Has Been Extracted during Deep guard Sandbox Execution. as a Result, the Files Classified as False …

Software Process Evaluation: A Machine Learning Approach, Ning Chen, Steven C. H. Hoi, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Software process evaluation is essential to improve software development and the quality of software products in an organization. Conventional approaches based on manual qualitative evaluations (e.g., artifacts inspection) are deficient in the sense that (i) they are time-consuming, (ii) they suffer from the authority constraints, and (iii) they are often subjective. To overcome these limitations, this paper presents a novel semi-automated approach to software process evaluation using machine learning techniques. In particular, we formulate the problem as a sequence classification task, which is solved by applying machine learning algorithms. Based on the framework, we define a new quantitative indicator to …

Development Of Advanced Algorithms To Detect, Characterize And Forecast Solar Activities, Yuan Yuan

Dissertations

Study of the solar activity is an important part of space weather research. It is facing serious challenges because of large data volume, which requires application of state-of-the-art machine learning and computer vision techniques. This dissertation targets at two essential aspects in space weather research: automatic feature detection and forecasting of eruptive events.

Feature detection includes solar filament detection and solar fibril tracing. A solar filament consists of a mass of gas suspended over the chromosphere by magnetic fields and seen as a dark, ribbon-shaped feature on the bright solar disk in Hα (Hydrogen-alpha) full-disk solar images. In this dissertation, …

An Evaluation Of The Eeg Alpha-To-Theta And Theta-To-Alpha Band Ratios As Indexes Of Mental Workload, Bujar Raufi, Luca Longo

Articles

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. …

Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning, Matt Bogard

Economics Faculty Publications

This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.

Machine Learning And Mapping Algorithms Applied To Proteomics Problems, William Shane Sanders

Theses and Dissertations

Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which …

Modeling Teacher Ratings Of Online Resources: A Human-Machine Approach To Quality, Mimi Recker, Heather Leary, Andrew Walker, Anne R. Diekama, Philipp Wetzler, Tamara Sumner, James Martin

Instructional Technology and Learning Sciences Faculty Publications

In education, the scalable deployment of media-rich online resources supports peer production in ways that promise to radically transform teaching and learning (CRA, 2005; Pea et al., 2008). Online educational repositories such as the Digital Library for Earth Systems Education (DLESE.org) and the National Science Digital Library (NSDL.org) collect and curate online learning resources created for a wide range of educational audiences and subject areas (McArthur & Zia, 2008). Through a simple, web-based authoring tool, called the Instructional Architect (IA.usu.edu) teachers locate and share educational resources and activities in an IA project. These IA projects can then be viewed, copied, …

Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal

Electrical & Computer Engineering Theses & Dissertations

Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method …

Findsite-Metal: Integrating Evolutionary Information And Machine Learning For Structure-Based Metal-Binding Site Prediction At The Proteome Level, Michal Brylinski, Jeffrey Skolnick

Faculty Publications

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the …

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, …

Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers

Electronic Theses and Dissertations

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our …

On The Effect Of Topology On Learning And Generalization In Random Automata Networks, Alireza Goudarzi

Dissertations and Theses

We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical …

Hierarchical Temporal Memory Cortical Learning Algorithm For Pattern Recognition On Multi-Core Architectures, Ryan William Price

Dissertations and Theses

Strongly inspired by an understanding of mammalian cortical structure and function, the Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a promising new approach to problems of recognition and inference in space and time. Only a subset of the theoretical framework of this algorithm has been studied, but it is already clear that there is a need for more information about the performance of HTM CLA with real data and the associated computational costs. For the work presented here, a complete implementation of Numenta's current algorithm was done in C++. In validating the implementation, first and higher order sequence …

A Contextual Approach To Learning Collaborative Behavior Via Observation, Cynthia L. Johnson

Electronic Theses and Dissertations

This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other …

Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation we present three topics critical to the document level classification of the narrative in medical reports: the use of preferred terminology in light of the presence of synonymous terms, the less than optimal performance of classification systems when presented with a non-uniform distribution of classes, and the problems associated with scarcity of labeled data when presented with an imbalance of classes in the data sets.

Effective Task Transfer Through Indirect Encoding, Phillip Verbancsics

Electronic Theses and Dissertations

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea …

Combining Natural Language Processing And Statistical Text Mining: A Study Of Specialized Versus Common Languages, Jay Jarman

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms, such as association rule mining and decision tree induction, are used to discover classification rules for specific targets. This multi-stage pipeline approach is contrasted with traditional statistical text mining (STM) methods based on term counts and term-by-document frequencies. The aim is to create effective text analytic processes by adapting and combining individual …

Assessing Data Quality In A Sensor Network For Environmental Monitoring, Gesuri Ramirez

Open Access Theses & Dissertations

Assessing the quality of sensor data in environmental monitoring applications is important, as erroneous readings produced by malfunctioning sensors, calibration drift, and problematic climatic conditions, such as icing or dust, are common.Traditional data quality checking and correction is a painstaking manual process, so the development of automatic systems for this task is highly desirable.

This study investigates machine learning methods to identify and clean incorrect data from a real-world environmental sensor network, the Jornada Experimental Range, located in Southern New Mexico. We evaluated several learning algorithms and data replacement schemes, and developed a method to identify the problematic sensor. The …

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea

Open Access Theses & Dissertations

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …

Features Extraction Using Random Matrix Theory., Viktoria Borisovna Rojkova

Electronic Theses and Dissertations

Representing the complex data in a concise and accurate way is a special stage in data mining methodology. Redundant and noisy data affects generalization power of any classification algorithm, undermines the results of any clustering algorithm and finally encumbers the monitoring of large dynamic systems. This work provides several efficient approaches to all aforementioned sides of the analysis. We established, that notable difference can be made, if the results from the theory of ensembles of random matrices are employed. Particularly important result of our study is a discovered family of methods based on projecting the data set on different subsets …

Three Algorithms For Causal Learning, Roshan Ram Rammohan

Computer Science ETDs

The field of causal learning has grown in the past decade, establishing itself as a major focus in artificial intelligence research. Traditionally, approaches to causal learning are split into two areas. One area involves the learning of structures from observational data alone and the second, involves the methodologies of conducting and learning from experiments. In this dissertation, I investigate three different aspects of causal learning, all of which are based on the causal Bayesian network framework. Constraint based structure search algorithms that learn partially directed acyclic graphs as causal models from observational data rely on the faithfulness assumption, which is …

Identification And Optimization Of Classifier Genes From Multi-Class Earthworm Microarray Dataset, Ying Li, Nan Wang, Chaoyang Zhang, Ping Gong

Faculty Publications

Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that …

Event-Driven Similarity And Classification Of Scanpaths, Thomas Grindinger

All Dissertations

Eye tracking experiments often involve recording the pattern of deployment of visual attention over the stimulus as viewers perform a given task (e.g., visual search). It is useful in training applications, for example, to make available an expert's sequence of eye movements, or scanpath, to novices for their inspection and subsequent learning. It may also be potentially useful to be able to assess the conformance of the novice's scanpath to that of the expert. A computational tool is proposed that provides a framework for performing such classification, based on the use of a probabilistic machine learning algorithm. The approach was …

Vowel Recognition From Continuous Articulatory Movements For Speaker-Dependent Applications, Jun Wang, Jordan R. Green, Ashok Samal, Tom D. Carrell

Department of Special Education and Communication Disorders: Faculty Publications

A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples …

Autocalibration Of Integrated Lake Water Quality Models For The Lake Winnipeg Basin Initiative, Markiyan Sloboda, David Swayne, William Booty, Craig Mccrimmon, Isaac Wong

International Congress on Environmental Modelling and Software

Machine learning (ML) and genetic algorithm (GA) are well known techniques which can be used to calibrate environmental models. This paper investigates the calibration of the 2-D horizontal, vertically mixed lake models OneLay and PolTra using ML and GA routines. A GA was used jointly with the Open Modelling Interface (OpenMI) wrapper approach on a single powerful server. Explicit and implicit gridded approaches with the Probably Approximately Correct (PAC) learning were used as ML techniques. Monte Carlo simulations were used to generate the model input parameters for explicit and implicit gridding. Parallel computing using Shared Hierarchical Academic Computing Network (SHARCNET) …

New Software Methods In Radar Ornithology Using Wsr-88d Weather Data And Potential Application To Monitoring Effects Of Climate Change On Bird Migration, Reginald Mead, John Paxton, Richard S. Sojda

International Congress on Environmental Modelling and Software

Radar ornithology has provided tools for studying the movement of birds,especially related to migration. Researchers have presented qualitative evidence suggestingthat birds, or at least migration events, can be identified using large broad scale radars suchas the WSR-88D used in the NEXRAD weather surveillance system. This is potentially aboon for ornithologists because such data cover a large portion of the United States, areconstantly being produced, are freely available, and have been archived since the early1990s. A major obstacle to this research, however, has been that identifying birds inNEXRAD data has required a trained technician to manually inspect a graphically renderedradar sweep. …

Autocalibration Of Integrated Lake Water Quality Models For The Lake Winnipeg Basin Initiative, Markiyan Sloboda, David Swayne, William Booty, Craig Mccrimmon, Isaac Wong

International Congress on Environmental Modelling and Software

Machine learning (ML) and genetic algorithm (GA) are well known techniques which can be used to calibrate environmental models. This paper investigates the calibration of the 2-D horizontal, vertically mixed lake models OneLay and PolTra using ML and GA routines. A GA was used jointly with the Open Modelling Interface (OpenMI) wrapper approach on a single powerful server. Explicit and implicit gridded approaches with the Probably Approximately Correct (PAC) learning were used as ML techniques. Monte Carlo simulations were used to generate the model input parameters for explicit and implicit gridding. Parallel computing using Shared Hierarchical Academic Computing Network (SHARCNET) …

New Software Methods In Radar Ornithology Using Wsr-88d Weather Data And Potential Application To Monitoring Effects Of Climate Change On Bird Migration, Reginald Mead, John Paxton, Richard S. Sojda

International Congress on Environmental Modelling and Software

Radar ornithology has provided tools for studying the movement of birds,especially related to migration. Researchers have presented qualitative evidence suggestingthat birds, or at least migration events, can be identified using large broad scale radars suchas the WSR-88D used in the NEXRAD weather surveillance system. This is potentially aboon for ornithologists because such data cover a large portion of the United States, areconstantly being produced, are freely available, and have been archived since the early1990s. A major obstacle to this research, however, has been that identifying birds inNEXRAD data has required a trained technician to manually inspect a graphically renderedradar sweep. …

Transformation Learning: Modeling Transferable Transformations In High-Dimensional Data, Christopher R. Wilson

Theses and Dissertations

The goal of learning transfer is to apply knowledge gained from one problem to a separate related problem. Transformation learning is a proposed approach to computational learning transfer that focuses on modeling high-level transformations that are well suited for transfer. By using a high-level representation of transferable data, transformation learning facilitates both shallow transfer (intra-domain) and deep transfer (inter-domain) scenarios. Transformations can be discovered in data using manifold learning to order data instances according to the transformations they represent. For high-dimensional data representable with coordinate systems, such as images and sounds, data instances can be decomposed into small sub-instances based …