Computer Sciences Commons^™

Efficient Algorithms For Analyzing Large Scale Network Dynamics: Centrality, Community And Predictability, Sima Das

Doctoral Dissertations

"Large scale networks are an indispensable part of our daily life; be it biological network, smart grids, academic collaboration networks, social networks, vehicular networks, or the networks as part of various smart environments, they are fast becoming ubiquitous. The successful realization of applications and services over them depend on efficient solution to their computational challenges that are compounded with network dynamics. The core challenges underlying large scale networks, for example: determining central (influential) nodes (and edges), interactions and contacts among nodes, are the basis behind the success of applications and services. Though at first glance these challenges seem to be …

Classification Of Basal Cell Carcinoma Using Telangiectatic Vessels And Machine Learning, Hemanth Yadav Aradhyula

Masters Theses

“Basal cell carcinoma (BCC) is one of the most common types of skin cancer in the United States. Early detection of BCC by noninvasive techniques can decrease delay in treatment and save cost. A recent study estimated that 5.4 million cases of non-melanocytic skin cancer (NMSC) occur each year in the US. BCC accounts for 50% of NMSC cases. Telangiectasia, which appears in most BCCs is an important feature for identification of BCC for an automatic diagnostic system. In this thesis, three methods for detection of telangiectasia present in dermoscopy lesion image (DI) were proposed. Detected telangiectasia in DI was …

The Viability Of Advantg Deterministic Method For Synthetic Radiography Generation, Andrew Albert Bingham

Masters Theses

"Time sensitive and high resolution image simulations are needed for synthetic radiography generation. The standard stochastic approach requires lengthy run times with poor statistics at higher resolutions. The investigation of the viability of a deterministic approach to synthetic radiography image generation was explored. The aim was to analyze a computational time decrease over the stochastic method. ADVANTG was compared to MCNP in multiple scenarios including a Benchtop CT prototype, to simulate high resolution radiography images. By using ADVANTG deterministic code to simulate radiography images the computational time was found to decrease over 10 times compared to the MCNP stochastic approach"--Abstract, …

Cyber-Physical Security Of A Chemical Plant, Prakash Rao Dunaka

Masters Theses

"The increasing number of cyber attacks on industries demands immediate attention for providing more secure mechanisms to safeguard industries and minimize risks. A supervisory control and data acquisition (SCADA) system employing the distributed networks of sensors and actuators that interact with the physical environment is vulnerable to attacks that target the interface between the cyber and physical subsystems. These cyber attacks are typically malicious actions that cause undesired results in the cyber physical world, for example, the Stuxnet attack that targeted Iran's nuclear centrifuges. An attack that hijacks the sensors in an attempt to provide false readings to the controller …

Personalizing Education With Algorithmic Course Selection, Tyler Morrow

Masters Theses

"The work presented in this thesis utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, topics or modules) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The original research contribution of this thesis is an algorithm that can generate a schedule of courses with consideration of a student's profile, minimization of cost, and complete adherence to institution requirements. The research problem at hand - a constrained optimization problem with potentially conflicting objectives - is solved by first identifying a minimal sets of courses a student …

Decodable Network Coding In Wireless Network, Junwei Su

Masters Theses

"Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this …

Multi Stage Recovery From Large Scale Failure In Interdependent Networks, Maria Angelin John Bosco

Masters Theses

"Node and link failures that usually cause limited damage in a single network, may cascade into large scale disasters in the case of interdependent networks, due to the dependencies that exist between them. Recovery from such failures may require multiple stages or steps for complete restoration of connection or flow between them. When critical services are disrupted, the order in which the broken elements are repaired affects the earliest possible recovery time of vital services. In a flow network, one order of restoration may restore more flow at an earlier stage than another. The paper aims to model an efficient …

Multiple Security Domain Model Of A Vehicle In An Automated Vehicle System, Uday Ganesh Kanteti

Masters Theses

"This thesis focuses on the security of automated vehicle platoons. Specifically, it examines the vulnerabilities that occur via disruptions of the information flows among the different types of sensors, the communications network and the control unit in each vehicle of a platoon. Multiple security domain nondeducibility is employed to determine whether the system can detect attacks. The information flows among the various domains provide insights into the vulnerabilities that exist in the system by showing if an attacker’s actions cannot be deduced. If nondeducibility is found to be true, then an attacker can create an undetectable attack. Defeating nondeducibility requires …

Multiple Security Domain Nondeducibility Air Traffic Surveillance Systems, Anusha Thudimilla

Masters Theses

"Traditional security models partition the security universe into two distinct and completely separate worlds: high and low level. However, this partition is absolute and complete. The partition of security domains into high and low is too simplistic for more complex cyber-physical systems (CPS). Absolute divisions are conceptually clean, but they do not reflect the real world. Security partitions often overlap, frequently provide for the high level to have complete access to the low level, and are more complex than an impervious wall. The traditional models that handle situations where the security domains are complex or the threat space is ill …

A New Reinforcement Learning Algorithm With Fixed Exploration For Semi-Markov Decision Processes, Angelo Michael Encapera

Masters Theses

"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition …

A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead

Masters Theses

"This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve Markov and semi-Markov decision processes (or problems) in the field of airline revenue management (ARM). The ARM problem is one of control optimization in which a decision-maker must accept or reject a customer based on a requested fare. This thesis focuses on the so-called single-leg version of the ARM problem, which can be cast as a semi-Markov decision process (SMDP). Large-scale Markov decision processes (MDPs) and SMDPs suffer from the curses of dimensionality and modeling, making it difficult to create the transition probability matrices (TPMs) …

Uface: Your Universal Password No One Can See, Nicholas Steven Hilbert

Masters Theses

"With the advantage of not having to memorize long passwords, facial authentication has become a topic of interest among researchers. However, since many users store images containing their face on social networking sites, a new challenge emerges in preventing attackers from impersonating these users by using these online photos. Another problem with most current facial authentication protocols is that they require an unencrypted image of each registered user's face to compare against. Moreover, they might require the user's device to execute computationally expensive multiparty protocols which presents a problem for mobile devices with limited processing power. Finally, these authentication protocols …

Decision Process In Mcdm With Large Number Of Criteria And Heterogeneous Risk Preferences, Jian Liu, Hong Kuan Zhao, Zhao Bin Li, Si Feng Liu

Electrical and Computer Engineering Faculty Research & Creative Works

A new decision process is proposed to address the challenge that a large number of criteria in the multi-criteria decision making (MCDM) problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion) …

Fusion Of Non-Visual And Visual Sensors For Human Tracking, Wenchao Jiang

Doctoral Dissertations

"Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. Thus, we propose to combine information from other sensors with the surveillance cameras to persistently localize and track humans, which is becoming more promising with the pervasiveness of mobile devices such as cellphones, smart watches and smart glasses embedded with all kinds of sensors including accelerometers, gyroscopes, magnetometers, GPS, WiFi modules and so on. In this …

Cognition-Based Approaches For High-Precision Text Mining, George John Shannon

Doctoral Dissertations

"This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in.

Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital …

Data Analytics Methods For Attack Detection And Localization In Wireless Networks, Yi Ling

Doctoral Dissertations

"Wireless ad hoc network operates without any fixed infrastructure and centralized administration. It is a group of wirelessly connected nodes having the capability to work as host and router. Due to its features of open communication medium, dynamic changing topology, and cooperative algorithm, security is the primary concern when designing wireless networks. Compared to the traditional wired network, a clean division of layers may be sacrificed for performance in wireless ad hoc networks. As a result, they are vulnerable to various types of attacks at different layers of the protocol stack. In this paper, I present real-time series data analysis …

Discrete Ordinates Ct Organ Dose Simulator (Doctors), Edward T. Norris

Doctoral Dissertations

"Computed tomography (CT) has become pervasive in medical diagnostics as improved imaging techniques and processing algorithms provide higher quality information to doctors. However, the exponentially increasing usage of CT has raised concerns regarding long term low-dose radiological risks.

Currently, the dose to patients is computed using Monte Carlo methods and experimental tests. In other areas of radiation transport, deterministic codes have been shown to be much faster than Monte Carlo codes.

Currently, no deterministic methodology exists to automatically generate a spatially distributed dose profile from a CT voxel phantom. This work proposes a new code, Discrete Ordinate CT Organ Dose …

Multimodal Spontaneous Emotion Corpus For Human Behavior Analysis, Zheng Zhang, Jeffrey M. Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andrew Horowitz, Huiyuan Yang, Jeffrey F. Cohn, Qiang Ji, Lijun Yin

Computer Science Faculty Research & Creative Works

Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both …

On The Vulnerabilities Of Voronoi-Based Approaches To Mobile Sensor Deployment, Novella Bartolini, Stefano Ciavarella, Simone Silvestri, Thomas La Porta

Computer Science Faculty Research & Creative Works

Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above …

Cloud-Enhanced Robotic System For Smart City Crowd Control, Akhlaqur Rahman, Jiong Jin, Antonio Cricenti, Ashfaqur Rahman, Marimuthu Palaniswami, Tony Tie Luo

Computer Science Faculty Research & Creative Works

Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a …

A Hypothesis Testing Approach For Topology Error Detection In Power Grids, Wei Biao Wu, Maggie X. Cheng, Bei Gou

Computer Science Faculty Research & Creative Works

When the grid topology is changed due to incidents and the state estimator is not updated with the topological change, it is considered a topology error. In this paper, we develop a new method for detecting topology errors in power grids. The proposed method considers the measurement data as a nonstationary Gaussian process, explores the dependence structure of the underlying process. It detects errors by testing the hypothesis of whether the mean vector of a nonstationary Gaussian process is zero and does not rely on the convergence of the standard weighted least-squares (WLS) state estimation algorithm. It is very effective …

Fine-Grained Multitask Allocation For Participatory Sensing With A Shared Budget, Jiangtao Wang, Yasha Wang, Daqing Zhang, Leye Wang, Haoyi Xiong, Abdelsalam Helal, Yuanduo He, Feng Wang

Computer Science Faculty Research & Creative Works

For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the …

Mixed-Criticality Scheduling To Minimize Makespan, Sanjoy K. Baruah, Arvind Easwaran, Zhishan Guo

Computer Science Faculty Research & Creative Works

In the mixed-criticality job model, each job is characterized by two execution time parameters, representing a smaller (less conservative) estimate and a larger (more conservative) estimate on its actual, unknown, execution time. Each job is further classified as being either less critical or more critical. The desired execution semantics are that all jobs should execute correctly provided all jobs complete upon being allowed to execute for up to the smaller of their execution time estimates, whereas if some jobs need to execute beyond their smaller execution time estimates (but not beyond their larger execution time estimates), then only the jobs …

Passive Rifting Of Thick Lithosphere In The Southern East African Rift: Evidence From Mantle Transition Zone Discontinuity Topography, Cory A. Reed, Kelly H. Liu, Patrick R. N. Chindandali, Belarmino Massingue, Hassan Mdala, Daniel Mutamina, Youqiang Yu, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To investigate the mechanisms for the initiation and early-stage evolution of the nonvolcanic southernmost segments of the East African Rift System (EARS), we installed and operated 35 broadband seismic stations across the Malawi and Luangwa rift zones over a 2 year period from mid-2012 to mid-2014. Stacking of over 1900 high-quality receiver functions provides the first regional-scale image of the 410 and 660 km seismic discontinuities bounding the mantle transition zone (MTZ) within the vicinity of the rift zones. When a 1-D standard Earth model is used for time-depth conversion, a normal MTZ thickness of 250 km is found beneath …

Mantle Structure Beneath The Incipient Okavango Rift Zone In Southern Africa, Youqiang Yu, Kelly H. Liu, Zhouchuan Huang, Dapeng Zhao, Cory A. Reed, Moikwathai Moidaki, Jianshe Lei, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Numerous investigations of the mature segments of the East African rift system (EARS) have significantly improved our understanding of the structure and processes associated with well-developed continental rifts. In contrast, knowledge of rifting processes at their early stage is still significantly limited. Here we present results from a teleseismic P-wave tomography investigation of the incipient Okavango rift zone (ORZ), which is located at the southwestern terminus of the EARS. P-wave relative travel-time residuals recorded by 17 recently deployed portable seismic stations were manually picked and inverted for three-dimensional upper-mantle and mantle transition-zone tomographic images beneath the ORZ and its adjacent …

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their …

On Selective Activation In Dense Femtocell Networks, Michael Lin, Simone Silvestri, Novella Bartolini, Thomas F. La Porta

Computer Science Faculty Research & Creative Works

Over-provisioned femtocell networks can be used to serve indoor locations that see high peak loads, such as airports or train stations. However, networks designed for high peak loads are mostly under-utilized, which is wasteful from an energy-use perspective. This paper introduces a femtocell selective activation problem. We motivate the use of selective activation in femtocell networks using real femtocell power measurements. We formally define the selective activation problem, and introduce GreenFemto, a distributed femtocell selective activation algorithm. We prove that GreenFemto converges to a locally Pareto optimal solution. Detailed simulations of an LTE wireless system are used to demonstrate the …

Data Analytics For Fault Localization In Complex Networks, Maggie X. Cheng, Wei Biao Wu

Computer Science Faculty Research & Creative Works

We consider the problem of identifying the source of failure in a network after receiving alarms or having observed symptoms. To locate the root cause accurately and timely in a large communication system is challenging because a single fault can often result in a large number of alarms, and multiple faults can occur concurrently. In this paper, we present a new fault localization method using a machine-learning approach. We propose to use logistic regression to study the correlation among network events based on end-to-end measurements. Then based on the regression model, we develop fault hypothesis that best explains the observed …

A Dual-Porosity-Stokes Model And Finite Element Method For Coupling Dual-Porosity Flow And Free Flow, Jiangyong Hou, Meilan Qiu, Xiaoming He, Chaohua Guo, Mingzhen Wei, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we propose and numerically solve a new model considering confined flow in dual-porosity media coupled with free flow in embedded macrofractures and conduits. Such situation arises, for example, for fluid flows in hydraulic fractured tight/shale oil/gas reservoirs. The flow in dual-porosity media, which consists of both matrix and microfractures, is described by a dual-porosity model. And the flow in the macrofractures and conduits is governed by the Stokes equation. Then the two models are coupled through four physically valid interface conditions on the interface between dual-porosity media and macrofractures/conduits, which play a key role in a physically …

Quantum Critical Behavior Of The Superfluid-Mott Glass Transition, Thomas Vojta, Jack Crewse, Martin Puschmann, Daniel Arovas, Yury Kiselev

Physics Faculty Research & Creative Works

We investigate the zero-temperature superfluid to insulator transitions in a diluted two-dimensional quantum rotor model with particle-hole symmetry. We map the Hamiltonian onto a classical (2+1)-dimensional XY model with columnar disorder which we analyze by means of large-scale Monte Carlo simulations. For dilutions below the lattice percolation threshold, the system undergoes a generic superfluid-Mott glass transition. In contrast to other quantum phase transitions in disordered systems, its critical behavior is of conventional power-law type with universal (dilution-independent) critical exponents z=1.52(3), ν =1.16(5), ß/ν =0.48(2), γ/ν=2.52(4), and η = -0.52(4). These values agree with and improve upon earlier Monte Carlo results …