Computer Sciences Commons^™

Data Mining Based Hybridization Of Meta-Raps, Fatemah Al-Duoli, Ghaith Rabadi

Engineering Management & Systems Engineering Faculty Publications

Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm …

German Foreign Policy In The Cyber Age, Patrick Schmitz 14

Honor Scholar Theses

None

An Efficient Similarity Digests Database Lookup -- A Logarithmic Divide And Conquer Approach, Frank Breitinger, Christian Rathgeb, Harald Baier

Electrical & Computer Engineering and Computer Science Faculty Publications

Investigating seized devices within digital forensics represents a challenging task due to the increasing amount of data. Common procedures utilize automated file identification, which reduces the amount of data an investigator has to examine manually. In the past years the research field of approximate matching arises to detect similar data. However, if n denotes the number of similarity digests in a database, then the lookup for a single similarity digest is of complexity of O(n). This paper presents a concept to extend existing approximate matching algorithms, which reduces the lookup complexity from O(n) to O(log(n)). Our proposed approach is based …

Privacy And Trustworthiness Management In Moving Object Environments, Sashi Gurung

Doctoral Dissertations

"The use of location-based services (LBS) (e.g., Intel's Thing Finder) is expanding. Besides the traditional centralized location-based services, distributed ones are also emerging due to the development of Vehicular Ad-hoc Networks (VANETs), a dynamic network which allows vehicles to communicate with one another. Due to the nature of the need of tracking users' locations, LBS have raised increasing concerns on users' location privacy. Although many research has been carried out for users to submit their locations anonymously, the collected anonymous location data may still be mapped to individuals when the adversary has related background knowledge.

To improve location privacy, in …

Access Control Delegation In The Clouds, Pavani Gorantla

Masters Theses

"Current market trends need solutions/products to be developed at high speed. To meet those requirements sometimes it requires collaboration between the organizations. Modern workforce is increasingly distributed, mobile and virtual which will incur hurdles for communication and effective collaboration within organizations. One of the greatest benefits of cloud computing has to do with improvements to organizations communication and collaboration, both internally and externally. Because of the efficient services that are being offered by the cloud service providers today, many business organizations started taking advantage of cloud services. Specifically, Cloud computing enables a new form of service in that a service …

A Comparative Study Of Reservoir Computing For Temporal Signal Processing, Alireza Goudarzi, Peter Banda, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic

Computer Science Faculty Publications and Presentations

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir's state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who study the task-solving performance and computational power of RC. In addition, in contrast to traditional computation models, the reservoir is a dynamical system in which computation and memory are inseparable, and therefore hard to analyze. Here, we compare …

Guiding Data-Driven Transportation Decisions, Kristin A. Tufte, Basem Elazzabi, Nathan Hall, Morgan Harvey, Kath Knobe, David Maier, Veronika Margaret Megler

Computer Science Faculty Publications and Presentations

Urban transportation professionals are under increasing pressure to perform data-driven decision making and to provide data-driven performance metrics. This pressure comes from sources including the federal government and is driven, in part, by the increased volume and variety of transportation data available. This sudden increase of data is partially a result of improved technology for sensors and mobile devices as well as reduced device and storage costs. However, using this proliferation of data for decisions and performance metrics is proving to be difficult. In this paper, we describe a proposed structure for a system to support data-driven decision making. A …

Evolving Decision Trees For The Categorization Of Software, Jasenko Hosic

Masters Theses

"Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. An automated method to categorize applications was developed in order to quickly determine pertinent characteristics. Prior work in this area has had some success, but a major strength of the approach detailed in this thesis is that it produces heuristics that can be reused for quick analysis of new data. The method relies on a genetic programming algorithm to evolve decision trees which can be used to categorize software. The terminals, or leaf nodes, within the trees each contain values based on selected features from one …

Privacy Preservation Using Spherical Chord, Doyal Tapan Mukherjee

Masters Theses

"Structured overlay networks are primarily used in data storage and data lookup, but they are vulnerable against many kinds of attacks. Within the realm of security, overlay networks have demonstrated applicability in providing privacy, availability, integrity, along with scalability. The thesis first analyses the Chord and the SALSA protocols which are organized in structured overlays to provide data with a certain degree of privacy, and then defines a new protocol called Spherical Chord which provides data lookup with privacy, while also being scalable, and addresses critical existing weaknesses in Chord and SALSA protocols. Spherical Chord is a variant of the …

Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …

Dynamic Symbolic Data Structure Repair And Evaluation Of Program Analysis Tools With The Rugrat Random Program Generator, Ishtiaque Hussain

Computer Science and Engineering Dissertations - Archive

Generic automatic repair of complex data structures is a new and exciting area of research. Existing approaches can integrate with good software engineering practices such as program assertions. But in practice there is a wide variety of assertions and not all of them satisfy the style rules imposed by existing repair techniques. That is, a badly written assertion may render generic repair inefficient or ineffective. Moreover, the performance of existing approaches may depend on the location of an error in a corrupted data structure. This dissertation shows that generic automatic data structure repair can be implemented with full dynamic symbolic …

Technology Initiative Assessment Through Acceptance And Satisfaction: A Case Study, Virginia Otto

All Graduate Theses, Dissertations, and Other Capstone Projects

This case study examines a University-wide tablet program to assess the primary users’ (students) acceptance and satisfaction of the implemented technology. Technology Acceptance Model (TAM) and user satisfaction research acted as the theoretical foundation that directed how to assess students’ attitudes and beliefs toward this newly adopted technology. Wixom & Todd’s (2005) Integrated Model of User Satisfaction and Technology Acceptance, served as the conceptual model to examine how students’ acceptance and satisfaction of the tablet related. Online surveys were distributed to examine if perceived usefulness and ease of use can predict user satisfaction. Multiple regression tests found that the combination …

Social Data Analytics Using Tensors And Sparse Techniques, Miao Zhang

Computer Science and Engineering Dissertations - Archive

The development of internet and mobile technologies is driving an earthshaking social media revolution. They bring the internet world a huge amount of social media content, such as images, videos, comments, etc. Those massive media content and complicate social structures require the analytic expertise to transform those flood of information into actionable strategies, because mining those data can help organizations take control of those data, therefore organizations can improve customer satisfaction, identify patterns and trends, and make smarter marketing strategies. Mining those data can also help the consumers to grasp the most important and convenient information from the overwhelming data …

Greendss Tool For Data Center Management, Michael Pawlish, Aparna Varde, Stefan Robila, Cynthia Alvarez, Christopher Fleischl, Genesis Serviano

Department of Computer Science Faculty Scholarship and Creative Works

As society shifts towards the Internet to conduct a greater share of communications and commerce, the demand for storage and processing of information is increasing. This is represented by the growth in data centers and energy usage. Traditionally, energy usage with respect to the greening of data centers has been a secondary concern. However, with the escalating total cost of ownership, and the ability to use smart meters this has become more important to monitor for increased savings. This paper describes the research conducted leading to the development of a software tool called GreenDSS (Decision Support System for Green Data …

Evolutionary Algorithm Based Approach For Modeling Autonomously Trading Agents, Anil Yaman, Stephen Lucci, Izidor Gertner

Publications and Research

The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent …

Intelligent Selection Techniques For Virtual Environments, Jeffrey Cashion

Electronic Theses and Dissertations

Selection in 3D games and simulations is a well-studied problem. Many techniques have been created to address many of the typical scenarios a user could experience. For any single scenario with consistent conditions, there is likely a technique which is well suited. If there isn't, then there is an opportunity for one to be created to best suit the expected conditions of that new scenario. It is critical that the user be given an appropriate technique to interact with their environment. Without it, the entire experience is at risk of becoming burdensome and not enjoyable. With all of the different …

The Information Complexity Of Hamming Distance, E. Blais, Joshua Brody, B. Ghazi

Computer Science Faculty Works

The Hamming distance function Ham_{n,d} returns 1 on all pairs of inputs x and y that differ in at most d coordinates and returns 0 otherwise. We initiate the study of the information complexity of the Hamming distance function. We give a new optimal lower bound for the information complexity of the Ham_{n,d} function in the small-error regime where the protocol is required to err with probability at most epsilon < d/n. We also give a new conditional lower bound for the information complexity of Ham_{n,d} that is optimal in all regimes. These results imply the first new lower bounds on the communication complexity of the Hamming distance function for the shared randomness two-way communication model since Pang and El-Gamal (1986). These results also imply new lower bounds in the areas of property testing and parity decision tree complexity.

Virtualization-Based System Hardening Against Untrusted Kernels, Yueqiang Cheng

Dissertations and Theses Collection (Open Access)

Applications are integral to our daily lives to help us processing sensitive I/O data, such as individual passwords and camera streams, and private application data, such as financial information and medical reports. However, applications and sensitive data all surfer from the attacks from kernel rootkits in the traditional architecture, where the commodity OS that is supposed to be the secure foothold of the system is routinely compromised due to the large code base and the broad attack surface. Fortunately, the virtualization technology has significantly reshaped the landscape of the modern computer system, and provides a variety of new opportunities for …

An Investigation Of The User Satisfaction Of Customer Relationship Management Program, Sangeun Lee

Senior Honors Theses and Projects

The thesis investigates user satisfaction for Microsoft Dynamics CRM 2011 by conduction surveys to graduate level students. The training manual was developed to guide the way to follow instructions to create an order and an invoice.

Visualizing And Predicting The Effects Of Rheumatoid Arthritis On Hands, Radu P. Mihail

Theses and Dissertations--Computer Science

This dissertation was inspired by difficult decisions patients of chronic diseases have to make about about treatment options in light of uncertainty. We look at rheumatoid arthritis (RA), a chronic, autoimmune disease that primarily affects the synovial joints of the hands and causes pain and deformities. In this work, we focus on several parts of a computer-based decision tool that patients can interact with using gestures, ask questions about the disease, and visualize possible futures. We propose a hand gesture based interaction method that is easily setup in a doctor's office and can be trained using a custom set of …

The Effects Of Using Chaotic Map On Improving The Performance Of Multiobjective Evolutionary Algorithms, Hui Lu, Xiaoteng Wang, Zongming Fei, Meikang Qiu

Computer Science Faculty Publications

Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects on EAs. This paper focuses on exploring the effects of chaotic maps and giving comprehensive guidance for improving multiobjective evolutionary algorithms (MOEAs) by series of experiments. NSGA-II algorithm, a representative of MOEAs using the nondominated sorting and elitist strategy, is taken as the framework to study the effect of chaotic maps. Ten chaotic maps are applied in MOEAs in three phases, that is, initial population, crossover, and mutation operator. …

Run-Time Compilation And Dynamic Memory Use Analysis For Gpus, Derek White

Computer Science and Engineering Dissertations - Archive

Powerful Graphics Processing Units (commonly called GPUs) are proliferatingrapidly and are becoming a viable choice for a wide range of user applications. Forperforming computationally intensive tasks on these processors, it is highly desirable to have software tools that can facilitate writing effective programming codewhile taking advantage of the full potential offered by these processors. Multi-levelmemory hierarchy, extensive data transfer, and the utilization of a large number ofprocessing cores are daunting challenges in writing code for data-parallel computingtasks. The programming experience becomes more cumbersome due to the significantrestrictions imposed by the OpenCL specification including the inability to allocatememory dynamically. The contribution …

Graph Embedding Discriminative Unsupervised Dimensionality Reduction, Yun Liu

Computer Science and Engineering Theses - Archive

In this thesis, a novel graph embedding unsupervised dimensionality reduction method was proposed. Simultaneously, we assigned the adaptive and optimal neighbors on the basis of the projected local distances, thus we developed the dimensionality reduction along with the graph construction. The clustering results could be directly exhibited from the learnt graph which has the explicit block diagonal structure.The analysis of experimental result on different databases also determines that the proposed dimensionality reduction method is superior to other related dimensionality reduction methods, like PCA and LPP. In this study, we use synthetic data and real-world benchmark data sets. Also experimental results …

A Hmm-Based Prediction Model For Spatio-Temporal Trajectories, Sakthi Kumaran Shanmuganathan

Computer Science and Engineering Theses - Archive

Spatio-temporal trajectories are time series data that represent movement of an object over the time. Hidden Markov Models (HMM), a variant of Markov Models (MM), were first applied at a large scale to speech recognition but have also been used in time series prediction by analyzing trends in historical time series data. In this research, we propose a storm prediction model using a HMM built from overall storm trajectories derived from raw rainfall data. This HMM is built by assuming the states are associated with clusters created by clustering the locations of each storm from the overall storm trajectories. Then …

Improving Tor Performance By Modifying Path Selection, Mehrdad Amirabadi

Computer Science and Engineering Theses - Archive

Tor is a popular volunteer-based overlay network that provides anonymity andprivacy for Internet users. Using the Onion Proxy (OP) client, users connect to anetwork of Onion Routers (ORs) and send their traffic through an encrypted path ofthree ORs. One of the main problems of the Tor network is its slow performance, anda key cause of this is the Tor path selection algorithm. In Tor, ORs are selected basedprimarily on their bandwidth. In this work, we improve on the Tor path selectionalgorithm by proposing a new algorithm that besides bandwidth, uses distance as afactor to help reduce propagation delay. In our …

Linking Entity Profiles, Ramesh Venkataraman

Computer Science and Engineering Theses - Archive

Entity linking allows one to have collections of data from multiple sources as a global dataset and then query those data. Entity linking allows us to do knowledge discovery on this global dataset which might result in the discovery of some interesting facts and information. Microsoft Academic Search (MAS) is a free public search engine for academic papers and contains the bibliographic information for papers published in journals, conference proceedings and respective citations. As of February 2014, it has indexed over 40 million publications and 20 million authors. LinkedIn is a social networking service used for professional networking. LinkedIn has …

Discovery Of Anomalous Patterns Within Multidimensional, Asynchronous Time-Series With An Emphasis On The "Internet Of Things", Stephen P. Emmons

Computer Science and Engineering Dissertations - Archive

In this dissertation we examine ``Internet-scale'' systems that present us with multidimensional time-series data characterized by many sources sending symbols at irregular intervals over a common channel. We explore a unique method for the discovery of hidden populations of similar sources and their previously-unknown behavioral patterns, and using these discoveries, reveal anomalous sources and/or time-frames based on their statistical properties. To do so, we employ several well-studied mechanisms, such as k-means and Principle Component Analysis (PCA), and bring to bear analysis tools from other disciplines, such as the use of n-grams and "motifs," that have not previously been considered in …

Analysis And Modeling Techniques For Geo-Spatial And Spatio-Temporal Datasets, Kulsawasd Jitkajornwanich

Computer Science and Engineering Dissertations - Archive

In recent years, spatio-temporal data has received a lot of attention and increasingly plays an important role in our everyday lives as we can witness from the fast-growing mobile technologies and its location-based application development. By spatio-temporal data, we mean data that is associated with specific spatial locations that change over time. For example, a cellphone or car with GPS will generate the object location at regular time intervals. Another example would be the track of a storm center as it moves. Spatio-temporal data could be thought of as a huge data warehouse, which contains hidden and meaningful information. However, …

Estimation Myopia: Tinkering With Perception In Software Estimation And Placebo Estimation In Edw, Hazem Hasan Yassin

Computer Science and Engineering Theses - Archive

The goal of this study is to explore an effective way to provide timely and accurate size estimates for software and for an enterprise data warehouse (EDW). Several research papers attempt to adapt function point (FP) analysis to EDW, but there is not much research in comprehensive techniques to estimate large EDW projects. Despite the generality of FP, it is challenging to employ in an EDW environment. This thesis describes such a technique. Additionally, the thesis provides an overview of general estimating approaches, techniques, models, and tools.This work presents a software tool that is a custom built estimation utility that …

Online Efficient And Effective Search In Large And Noisy Sequence Databases, Alexios Kotsifakos

Computer Science and Engineering Dissertations - Archive

This thesis investigates the problem of similarity search in large and noisy sequence databases. A key application domain of interest in this work is the very challenging Query-By-Humming (QBH) problem, according to which, given a hummed part of a song, we would like to identify the closest matches in a large music repository. The problem of selecting the most appropriate, based on each specific query, distance measure out of a pool of measures for classification in time series data is also investigated. In addition, searching time series databases via examples, which may be either time series or models, is also …