Digital Commons Network^™

On The Automatic Recognition Of Human Activities Using Heterogeneous Wearable Sensors, Oscar David Lara Yejas

USF Tampa Graduate Theses and Dissertations

Delivering accurate and opportune information on people's activities and behaviors has become one of the most important tasks within pervasive computing. Its wide spectrum of potential applications in medical, entertainment, and tactical scenarios, motivates further

research and development of new strategies to improve accuracy, pervasiveness, and eciency.

This dissertation addresses the recognition of human activities (HAR) with wearable sensors in three main regards: In the rst place, physiological signals have been incorporated as a new source of information to improve the recognition accuracy achieved by conventional approaches, which rely on accelerometer signals solely. A new HAR system, Centinela, was born …

Bayesian And Related Methods: Techniques Based On Bayes' Theorem, Mehmet Vurkaç

Systems Science Friday Noon Seminar Series

Bayes' theorem is a simple algebraic consequence of conditional probability. Yet, its consequences are critical to philosophy, society, and technology. Starting from its simple derivation, we will show how its interpretation in terms of base rates (priors) and class-conditional likelihoods illuminates everyday problems in medicine and law, and provides signal processing, communications, machine learning, model selection, and other applications of statistics with powerful classification and estimation tools. Next, we will briefly examine some of the ways in which this theorem can be adopted to include multiple attributes, contexts, hypotheses, and levels of risk. Methods derived from or related to Bayes’ …

Bayesian Nonparametric Clusterings In Relational And High-Dimensional Settings With Applications In Bioinformatics., Dazhuo Li

Electronic Theses and Dissertations

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address …

Electroencephalography (Eeg)-Based Brain Computer Interfaces For Rehabilitation, Dandan Huang

Theses and Dissertations

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of …

The Glass Is Half-Full: Overestimating The Quality Of A Novel Environment Is Advantageous, Oded Berger-Tal, Tal Avgar

Wildland Resources Faculty Publications

According to optimal foraging theory, foraging decisions are based on the forager's current estimate of the quality of its environment. However, in a novel environment, a forager does not possess information regarding the quality of the environment, and may make a decision based on a biased estimate. We show, using a simple simulation model, that when facing uncertainty in heterogeneous environments it is better to overestimate the quality of the environment (to be an “optimist”) than underestimate it, as optimistic animals learn the true value of the environment faster due to higher exploration rate. Moreover, we show that when the …

A Review Of Situation Identification Techniques In Pervasive Computing, Juan Ye, Simon Dobson, Susan Mckeever

Articles

Pervasive systems must offer an open, extensible, and evolving portfolio of services which integrate sensor data from a diverse range of sources. The core challenge is to provide appropriate and consistent adaptive behaviours for these services in the face of huge volumes of sensor data exhibiting varying degrees of precision, accuracy and dynamism. Situation identification is an enabling technology that resolves noisy sensor data and abstracts it into higher-level concepts that are interesting to applications. We provide a comprehensive analysis of the nature and characteristics of situations, discuss the complexities of situation identification, and review the techniques that are most …

Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao

Dissertations

Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to …

A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood

Electronic Theses and Dissertations

The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point …

Ensemble Methods For Malware Diagnosis Based On One-Class Svms, Xing An

LSU Master's Theses

Malware diagnosis is one of today’s most popular topics of machine learning. Instead of simply applying all the classical classification algorithms to the problem and claim the highest accuracy as the result of prediction, which is the typical approach adopted by studies of this kind, we stick to the Support Vector Machine (SVM) classifier and based on our observation of some principles of learning, characteristics of statistics and the behavior of SVM, we employed a number of the potential preprocessing or ensemble methods including rescaling, bagging and clustering that may enhance the performance to the classical algorithm. We implemented the …

Topic Modeling And Figurative Language, Lisa M. Rhody

Publications and Research

Located at the center of Jorie Graham’s collection The End of Beauty, “Self Portrait as Hurray and Delay” crafts a portrait of the artist, poised at a precarious moment in which thought begins to take shape. Like Penelope, Graham entertains the illusion, if only momentarily, of a choice between bringing a creative impulse into form or allowing it to come undone. A weaver of language, Graham subtly, deftly, but unsuccessfully attempts to delay the inevitable moment in poetic creation in which complexity of thought adopts form through language, and so realized is also reduced. In The End of Beauty, the …

Probabilistic Framework For Image Understanding Applications Using Bayesian Networks, Mustafa Jaber

Theses

Machine learning algorithms have been successfully utilized in various systems/devices. They have the ability to improve the usability/quality of such systems in terms of intelligent user interface, fast performance, and more importantly, high accuracy. In this research, machine learning techniques are used in the field of image understanding, which is a common research area between image analysis and computer vision, to involve higher processing level of a target image to "make sense" of the scene captured in it. A general probabilistic framework for image understanding where topics associated with (i) collection of images to generate a comprehensive and valid database, …

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 …

Active Multiple Kernel Learning For Interactive 3d Object Retrieval Systems, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. …

Active Multiple Kernel Learning For Interactive 3d Object Retrieval Systems, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. …

Click Fraud : How To Spot It, How To Stop It?, Chamila Kumara Walgampaya

Electronic Theses and Dissertations

Online search advertising is currently the greatest source of revenue for many Internet giants such as Google™, Yahoo!™, and Bing™. The increased number of specialized websites and modern profiling techniques have all contributed to an explosion of the income of ad brokers from online advertising. The single biggest threat to this growth is however click fraud. Trained botnets and even individuals are hired by click-fraud specialists in order to maximize the revenue of certain users from the ads they publish on their websites, or to launch an attack between competing businesses. Most academics and consultants who study online advertising estimate …

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. …

Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom

Systems Science Friday Noon Seminar Series

In this presentation I will describe the latest version of the Numenta HTM Cortical Learning Algorithm and why it is interesting for doing research into radical new computer architectures. Then I will discuss the hardware acceleration research we are doing, and briefly look at some preliminary applications development.

Image Annotation And Retrieval Based On Multi-Modal Feature Clustering And Similarity Propagation., Mohamed Maher Ben Ismail 1979-

Electronic Theses and Dissertations

The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user's high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic …

Narrative Analysis And Computational Model To Predict Interestingness Of Narratives, Laxman Thapa

Theses and Dissertations - UTB/UTPA

In this research, I present results demonstrating the classification of the specially generated narratives by a machine agent by listening to human subject describing the same sets of the events. These classifications are based on human ratings of interestingness for many different recountings of the same stories. The classification is performed on various features selected after analyzing the different possible feature that affect on the interestingness of narratives. The features were extracted from the surface text as well as from annotations of how each narration relates to the content of the known story. I present the annotation process and resulting …

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 …

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 …

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, …

An Exploration Of Multi-Agent Learning Within The Game Of Sheephead, Brady Brau

All Graduate Theses, Dissertations, and Other Capstone Projects

In this paper, we examine a machine learning technique presented by Ishii et al. used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of …

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 …

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 …

Optimal Detour Planning Around Blocked Construction Zones, Mutasem Jardaneh

Electronic Theses and Dissertations

Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners as they are required to plan vehicle routes around construction zones in such a way that …