Digital Commons Network^™

Designing Human-Centered Collective Intelligence, Ivor Addo

Dissertations (1934 -)

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting …

Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li

Computer Science Theses & Dissertations

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …

A Generalized Computer Vision Approach To Mapping Crop Fields In Heterogeneous Agricultural Landscapes, Stephanie R. Debats, Dee Luo, Lyndon Estes, Thomas J. Fuchs, Kelly K. Caylor

Geography

Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize …

Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach, Can Cui, Teresa Wu, Mengqi Hu, Jeffery D. Weir, Xiwang Li

Faculty Publications

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which …

Determining The Effectiveness Of Soil Treatment On Plant Stress Using Smart-Phone Cameras, Anurag Panwar

USF Tampa Graduate Theses and Dissertations

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this thesis, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficienceies in soil using smartphone cameras, …

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

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 …

Mining Knowledge Bases For Question & Answers Websites, Eduardo Coelho De Lima

Theses

We studied the problem of searching answers for questions on a Question-and-Answer Website from knowledge bases. A number of research efforts had been developed using Stack Overflow data, which is available for the public. Surprisingly, only a few papers tried to improve the search for better answers. Furthermore, current approaches for searching a Question-and-Answer Website are usually limited to the question database, which is usually the website own content. We showed it is feasible to use knowledge bases as sources for answers. We implemented both vector-space and topic-space representations for our datasets and compared these distinct techniques. Finally, we proposed …

Revelation Of Yin-Yang Balance In Microbial Cell Factories By Data Mining, Flux Modeling, And Metabolic Engineering, Gang Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

The long-held assumption of never-ending rapid growth in biotechnology and especially in synthetic biology has been recently questioned, due to lack of substantial return of investment. One of the main reasons for failures in synthetic biology and metabolic engineering is the metabolic burdens that result in resource losses. Metabolic burden is defined as the portion of a host cells resources either energy molecules (e.g., NADH, NADPH and ATP) or carbon building blocks (e.g., amino acids) that is used to maintain the engineered components (e.g., pathways). As a result, the effectiveness of synthetic biology tools heavily dependents on cell capability to …

A General Framework Of Large-Scale Convex Optimization Using Jensen Surrogates And Acceleration Techniques, Soysal Degirmenci

McKelvey School of Engineering Graduate Student Theses & Dissertations

In a world where data rates are growing faster than computing power, algorithmic acceleration based on developments in mathematical optimization plays a crucial role in narrowing the gap between the two. As the scale of optimization problems in many fields is getting larger, we need faster optimization methods that not only work well in theory, but also work well in practice by exploiting underlying state-of-the-art computing technology.

In this document, we introduce a unified framework of large-scale convex optimization using Jensen surrogates, an iterative optimization method that has been used in different fields since the 1970s. After this general treatment, …

Surface Chemistry With Machine Learning And Quantum Mechanics, Venkatesh Botu

Doctoral Dissertations

Surface chemistry is a phenomenon manifesting itself in several key areas; catalysis, materials fabrication, and emissions mitigation, to name a few. At the present time, atomistic computational driven efforts to study such processes are dominated by models based on quantum mechanics. Their flexibility in studying diverse chemistries, along with the ability to predict accurate thermodynamic and kinetic insights of surface processes, makes them increasingly popular. From ultra-low temperature and pressure to normal operating conditions these methods are now commonly utilized. Nevertheless, the computational burden inherent in the method renders it insufficient to keep up with the current need for quick …

Comparison Of Machine Learning Algorithms In Suggesting Candidate Edges To Construct A Query On Heterogeneous Graphs, Rohit Ravi Kumar Bhoopalam

Computer Science and Engineering Theses - Archive

Querying graph data can be difficult as it requires the user to have knowledge of the underlying schema and the query language. Visual query builders allow users to formulate the intended query by drawing nodes and edges of the query graph, which can be translated into a database query. Visual query builders help users formulate the query without requiring the user to have knowledge of the query language and the underlying schema. To the best of our knowledge, none of the currently available visual query builders suggest users what nodes/edges to include into their query graph. We provide suggestions to …

Human Robot Interaction Using Knowledge Base Approach, Utsav Shah

Mechanical and Aerospace Engineering Theses - Archive

This research investigated Human Robot Interaction modalities. The performance of a robotic prosthetic hand (RPH) was used as a test bed robot. A user wearable glove was fitted with sensors to provide tele-control of the RPH. An open hardware control and easily expandable research platform based on LabVIEW software and myRIO control hardware was developed. LabVIEW graphical programming platform provides the tools for the development to customized interfaces for visualization purposes which is desired in research. The research platform was used to calibrate and control the operation of the artificial hand using various modalities such as open loop, glove master-slave …

A Comparative Approach To Question Answering Systems, Josue Balandrano Coronel

Theses and Dissertations

In this paper I will analyze three different algorithms and approaches to implement Question Answering Systems (QA-Systems). I will analyze the efficiency, strengths, and weaknesses of multiple algorithms by explaining them in detail and comparing them with each other. The overarching aim of this thesis is to explore ideas that can be used to create a truly open context QA-System. Open context QA-Systems remain an open problem.

The various algorithms and approaches presented in this work will be focused on complex questions. Complex questions are usually verbose and the context of the question is equally important to answer the query …

Assessing Gender Inequality From Large Scale Online Student Reviews, Souradeep Sinha

Dissertations - ALL

Career growth in academia is often dependent on student reviews of university profes-

sors. A growing concern is how evaluation of teaching has been affected by gender biases

throughout the reviewing process. However, pinpointing the exact causes and consequen-

tial effects of this form of gender inequality has been a hard task.

Current work focusses on university-wide student reviewing system, that depends on

objective responses on a Likert scale to measure various aspects of an instructor’s qual-

ity. Through our work, we access online student review data which are not limited by

geographies, universities, or disciplines.

Thereafter, we come up …

Sparse Feature Learning For Image Analysis In Segmentation, Classification, And Disease Diagnosis., Ehsan Hosseini-Asl

Electronic Theses and Dissertations

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep …

Exploring Privacy Leakage From The Resource Usage Patterns Of Mobile Apps, Amin Rois Sinung Nugroho

Graduate Theses and Dissertations

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile …

Mobile Big Data Analytics Using Deep Learning And Apache Spark, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han

Research Collection School Of Computing and Information Systems

The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, …

Memory And Information Processing In Recurrent Neural Networks, Alireza Goudarzi, Sarah Marzen, Peter Banda, Guy Feldman, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic

Electrical and Computer Engineering Faculty Publications and Presentations

Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function-structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of …

Gait And Tremor Monitoring System For Patients With Parkinson’S Disease Using Wearable Sensors, Shyam Vignesh Perumal

USF Tampa Graduate Theses and Dissertations

Typically, a Parkinson’s disease (PD) patient would display instances of tremor and bradykinesia (slowness of movement) at an early stage of the disease and later develop gait disturbances and postural instability. So, it is important to measure the tremor occurrences in subjects to detect the onset of PD. Also, it is equally essential to monitor the gait impairments that the patient displays, as the order at which the PD symptoms appear in subjects vary from one to another.

The primary goal of this thesis is to develop a monitoring system for PD patients using wearable sensors. To achieve that objective, …

A Mathematical Formalization Of Hierarchical Temporal Memory's Spatial Pooler For Use In Machine Learning, James W. Mnatzaganian

Theses

Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, consists of two primary components, namely the spatial pooler (SP) and the temporal memory (TM). The SP is utilized to map similar inputs into generalized sparse distributed representations (SDRs). Those SDRs are then utilized by the TM, which performs sequence learning and prediction. One challenge with HTM is ensuring that proper SDRs are generated from the SP. If the SDRs are not generalizable, the TM will not be able to make …

Bridging Statistical Learning And Formal Reasoning For Cyber Attack Detection, Kexin Pei

Open Access Theses

Current cyber-infrastructures are facing increasingly stealthy attacks that implant malicious payloads under the cover of benign programs. Current attack detection approaches based on statistical learning methods may generate misleading decision boundaries when processing noisy data with such a mixture of benign and malicious behaviors. On the other hand, attack detection based on formal program analysis may lack completeness or adaptivity when modeling attack behaviors. In light of these limitations, we have developed LEAPS, an attack detection system based on supervised statistical learning to classify benign and malicious system events. Furthermore, we leverage control flow graphs inferred from the system event …

Learning Robust And Discriminative Manifold Representations For Pattern Recognition, Sriram Kumar

Theses

Face and object recognition find applications in domains such as biometrics, surveillance and human computer interaction. An important component in any recognition pipeline is to learn pertinent image representations that will help the system to discriminate one image class from another. These representations enable the system to learn a discriminative function that can classify a wide range of images. In practical situations, the images acquired are often corrupted with occlusions and noise. Thus, a robust and discriminative learning is necessary for good classification performance.

This thesis explores two scenarios where robust and discriminative manifold representations help recognize face and object …

Predicting Changes To Source Code, Justin James Roll

Master's Theses

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement …

Cross-Subject Continuous Analytic Workload Profiling Using Stochastic Discrete Event Simulation, Joseph J. Giametta

Theses and Dissertations

Operator functional state (OFS) in remotely piloted aircraft (RPA) simulations is modeled using electroencephalograph (EEG) physiological data and continuous analytic workload profiles (CAWPs). A framework is proposed that provides solutions to the limitations that stem from lengthy training data collection and labeling techniques associated with generating CAWPs for multiple operators/trials. The framework focuses on the creation of scalable machine learning models using two generalization methods: 1) the stochastic generation of CAWPs and 2) the use of cross-subject physiological training data to calibrate machine learning models. Cross-subject workload models are used to infer OFS on new subjects, reducing the need to …

Methods To Address Extreme Class Imbalance In Machine Learning Based Network Intrusion Detection Systems, Russell W. Walter

Theses and Dissertations

Despite the considerable academic interest in using machine learning methods to detect cyber attacks and malicious network traffic, there is little evidence that modern organizations employ such systems. Due to the targeted nature of attacks and cybercriminals’ constantly changing behavior, valid observations of attack traffic suitable for training a classifier are extremely rare. Rare positive cases combined with the fact that the overwhelming majority of network traffic is benign create an extreme class imbalance problem. Using publically available datasets, this research examines the class imbalance problem by using small samples of the attack observations to create multiple training sets that …

A Novel Stacking Method For Multi-Label Classification, Abdulaziz Alali

Open Access Dissertations

Classical machine learning algorithms were tailored to automatically classify examples that belong to mutually exclusive classes; each example may belong to one class out of a finite set of classes. In realistic applications, however, examples often belong to more than one class at the same time. For example, a text document that belongs to Geography may also be labeled as Geology. Perhaps due to the popularity of its applications, targeting this category of problems has garnered great research interest over the past decade. A widely popular approach, called Binary Relevance (BR), is to induce a separate classifier for each class; …

An Artificial Neural Networks Based Temperature Prediction Framework For Network-On-Chip Based Multicore Platform, Sandeep Aswath Narayana

Theses

Continuous improvement in silicon process technologies has made possible the integration of hundreds of cores on a single chip. However, power and heat have become dominant constraints in designing these massive multicore chips causing issues with reliability, timing variations and reduced lifetime of the chips. Dynamic Thermal Management (DTM) is a solution to avoid high temperatures on the die. Typical DTM schemes only address core level thermal issues. However, the Network-on-chip (NoC) paradigm, which has emerged as an enabling methodology for integrating hundreds to thousands of cores on the same die can contribute significantly to the thermal issues. Moreover, the …

Topic Modeling Based Cross-Source Consumer Opinions Discovery & Integration, Fan Liu

Theses and Dissertations

Online reviews can easily be accessed from e-commerce websites, dedicated web portals, blogs, discussion groups, forums, and other social media. It provides affluent information about products or services for consumers, merchants, and business analysts. It brings convenience, and also confusion. People feel overwhelmed by the information they receive from online reviews. "Is this product real good, or just so so? What are the opinions of the other consumers who had experience on the item? Why the reviews from different sites read different?" These questions are frequently asked by the review readers. This dissertation answers the above questions by first investigating …

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 …