Digital Commons Network^™

Egonet: Identification Of Human Disease Ego-Network Modules, Rendong Yang, Yun Bai, Zhaohui Qin, Tianwei Yu

PCOM Scholarly Works

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: …

An Adaptive Hybrid Method For Link Prediction In Multi-Modal Directed Complex Networks Using The Graph Traversal Pattern, William Lyon

Graduate Student Theses, Dissertations, & Professional Papers

The paper examines the link prediction problem for directed multi-modal complex networks. Specically, a hybrid method combining collaborative filtering and Triadic Closeness methods is developed. The methods are applied to a sample of the GitHub network. Implementation details are discussed, with a focus on design of a scalable system for handilng large data sets. Finally, results of this new method are discussed with no significant improvement over current methods.

Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu

Research Collection School Of Computing and Information Systems

This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve …

Cooperative Target Tracking Enhanced With The Sequence Memoizer, Everett A. Bryan

Theses and Dissertations

Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are …

Geometric Modeling Of Non-Rigid 3d Shapes : Theory And Application To Object Recognition., Mostafa Abdelrahman

Electronic Theses and Dissertations

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual …

Can Clustering Improve Requirements Traceability? A Tracelab-Enabled Study, Brett Taylor Armstrong

Master's Theses

Software permeates every aspect of our modern lives. In many applications, such in the software for airplane flight controls, or nuclear power control systems software failures can have catastrophic consequences. As we place so much trust in software, how can we know if it is trustworthy? Through software assurance, we can attempt to quantify just that.

Building complex, high assurance software is no simple task. The difficult information landscape of a software engineering project can make verification and validation, the process by which the assurance of a software is assessed, very difficult. In order to manage the inevitable information overload …

What You Want Is Not What You Get: Predicting Sharing Policies For Text-Based Content On Facebook, Arunesh Sinha, Li Yan, Lujo Bauer

Research Collection Lee Kong Chian School Of Business

As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand the privacy features offered by social networks. A way to mitigate these problems is to develop automated tools to assist users in correctly setting their policy. This paper explores the viability of one such approach: we examine the extent to which machine learning can be used to deduce users' sharing preferences for content posted on Facebook. To generate data on which to evaluate …

Variable Importance And Prediction Methods For Longitudinal Problems With Missing Variables, Ivan Diaz, Alan E. Hubbard, Anna Decker, Mitchell Cohen

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper we present prediction and variable importance (VIM) methods for longitudinal data sets containing both continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can thus provide a tool to make care decisions informed by the high-dimensional patient’s physiological and clinical history. Our VIM parameters can be causally interpreted …

New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan

Open Access Dissertations

When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …

Enabling Richer Insight Into Runtime Executions Of Systems, Karthik Swaminathan Nagaraj

Open Access Dissertations

Systems software of very large scales are being heavily used today in various important scenarios such as online retail, banking, content services, web search and social networks. As the scale of functionality and complexity grows in these software, managing the implementations becomes a considerable challenge for developers, designers and maintainers. Software needs to be constantly monitored and tuned for optimal efficiency and user satisfaction. With large scale, these systems incorporate significant degrees of asynchrony, parallelism and distributed executions, reducing the manageability of software including performance management. Adding to the complexity, developers are under pressure between developing new functionality for customers …

Asymptotically Unbiased Estimator Of The Informational Energy With Knn, Angel Caţaron, Răzvan Andonie, Chinmei Y. Chueh

All Faculty Scholarship for the College of the Sciences

Motivated by machine learning applications (e.g., classification, function approximation, feature extraction), in previous work, we have introduced a non- parametric estimator of Onicescu’s informational energy. Our method was based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. In the present contribution, we discuss mathematical properties of this estimator. We show that our estimator is asymptotically unbiased and consistent. We provide further experimental results which illustrate the convergence of the estimator for standard distributions.

Application Of Machine Learning And Parametric Nurbs Geometry To Mode Shape Identification, Robert Mceuen Porter

Theses and Dissertations

In any design, the dynamic characteristics of a part are dependent on its geometric and material properties. Identifying vibrational mode shapes within an iterative design process becomes difficult and time consuming due to frequently changing part definition. Although research has been done to improve the process, visual inspection of analysis results is still the current means of identifying each vibrational mode determined by a modal analysis. This research investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning.In the developed method, displacement results from finite element modal analysis are used to create …

Practical Cost-Conscious Active Learning For Data Annotation In Annotator-Initiated Environments, Robbie A. Haertel

Theses and Dissertations

Many projects exist whose purpose is to augment raw data with annotations that increase the usefulness of the data. The number of these projects is rapidly growing and in the age of “big data” the amount of data to be annotated is likewise growing within each project. One common use of such data is in supervised machine learning, which requires labeled data to train a predictive model. Annotation is often a very expensive proposition, particularly for structured data. The purpose of this dissertation is to explore methods of reducing the cost of creating such data sets, including annotated text corpora.We …

The Gc3 Framework : Grid Density Based Clustering For Classification Of Streaming Data With Concept Drift., Tegjyot Singh Sethi

Electronic Theses and Dissertations

Data mining is the process of discovering patterns in large sets of data. In recent years there has been a paradigm shift in how the data is viewed. Instead of considering the data as static and available in databases, data is now regarded as a stream as it continuously flows into the system. One of the challenges posed by the stream is its dynamic nature, which leads to a phenomenon known as Concept Drift. This causes a need for stream mining algorithms which are adaptive incremental learners capable of evolving and adjusting to the changes in the stream. Several models …

Segmentation And Model Generation For Large-Scale Cyber Attacks, Steven E. Strapp

Theses

Raw Cyber attack traffic can present more questions than answers to security analysts. Especially with large-scale observables it is difficult to identify which packets are relevant and what attack behaviors are present. Many existing works in Host or Flow Clustering attempt to group similar behaviors to expedite analysis; these works often phrase the problem directly as offline unsupervised machine learning. This work proposes online processing to simultaneously model coordinating actors and segment traffic that is relevant to a target of interest, all while it is being received. The goal is not just to aggregate similar attack behaviors, but to provide …

Real-Time Bioacoustics Monitoring And Automated Species Identification, T. Mitchell Aide, Carlos Corrada-Bravo, Marconi Campos-Cerqueira, Carlos Milan, Giovany Vega, Rafael Alvarez

Bioacoustics Collection

Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. …

Interactive Exploration Of Multitask Dependency Networks, Diane Oyen

Computer Science ETDs

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input …

Computer Sketch Recognition, Richard Steigerwald

Master's Theses

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy.

In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset …

Efindsite: Improved Prediction Of Ligand Binding Sites In Protein Models Using Meta-Threading, Machine Learning And Auxiliary Ligands, Michal Brylinski, Wei P. Feinstein

Faculty Publications

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % …

Mind Change Speed-Up For Learning Languages From Positive Data, Sanjay Jain, Efim Kinber

School of Computer Science & Engineering Faculty Publications

Within the frameworks of learning in the limit of indexed classes of recursive languages from positive data and automatic learning in the limit of indexed classes of regular languages (with automatically computable sets of indices), we study the problem of minimizing the maximum number of mind changes by a learner on all languages with indices not exceeding . For inductive inference of recursive languages, we establish two conditions under which can be made smaller than any recursive unbounded non-decreasing function. We also establish how is affected if at least one of these two conditions does not hold. In the case …

Document Classification, Shane K. Panter

Boise State University Theses and Dissertations

We present an overview of the document classification process and present research conducted against the newly constructed SBIR-STTR corpus. Specifically, the current methods in use for annotation, corpus construction, feature construction, feature weighting, and classifier algorithms are surveyed. We introduce a new dataset derived from public data downloaded from sbir.gov and the Text Annotation Toolkit (TAT) ₁ for use in classification research.

TAT is a collection of independent components packaged together into one open source software application. TAT was engineered to support the document classification process and workflow. Tracking of changes in a working corpus, saving data used in the …

A Memristor-Based Neuromorphic Computing Application, Adrian Rothenbuhler

Boise State University Theses and Dissertations

Artificial neural networks have recently received renewed interest because of the discovery of the memristor. The memristor is the fourth basic circuit element, hypothesized to exist by Leon Chua in 1971 and physically realized in 2008. The two-terminal device acts like a resistor with memory and is therefore of great interest for use as a synapse in hardware ANNs. Recent advances in memristor technology allowed these devices to migrate from the experimental stage to the application stage.

This Master's thesis presents the development of a threshold logic gate (TLG), which is a special case of an ANN, implemented with discrete …

Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young

Graduate Theses and Dissertations

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is …

Probabilistic Explicit Topic Modeling, Joshua Aaron Hansen

Theses and Dissertations

Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suffers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isolated from enriching information from knowledge sources such as Wikipedia and is difficult for humans to interpret due to a lack of meaningful topic labels. This thesis introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Static Topic-Word Distributions (LDA-STWD), and Explicit Dirichlet Allocation (EDA). LDA-STWD …

Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange

Dissertations

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …

Object Detection And Recognition In Natural Settings, George William Dittmar

Dissertations and Theses

Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can the GLIMPSE-based object detection system replicate …

Dissection Of Stress Response Networks Regulating Multiple Stresses In Rice, Rafi Shaik

Dissertations, Master's Theses and Master's Reports - Open

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes …

Life Long Learning In Sparse Learning Environments, John Reeder

Electronic Theses and Dissertations

Life long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an algorithm and framework for life long learning among a network of agents in a sparse real-time learning environment. This work will utilize the robust …

Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz

Electronic Theses and Dissertations

As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video …

Optimal Performance Counter Events For Detecting Android Malware, Hunter Stinson

Theses

This work uses salience testing techniques to identify the best performance counter events for detecting malware on Android devices. Modifications were made to a Linux kernel module to enable monitoring and logging of performance counter events. Numerous experiments combining different performance counter events were conducted, and a variety of data aggregation and classification techniques were evaluated. Experiment results were analyzed as to determine how well certain combinations of performance counter events classify applications as malware or non-malware. Results indicate that there are combinations of performance counter events that do much better at detecting malware than those presented in prior work.