Physical Sciences and Mathematics Commons^™

Os2: Oblivious Similarity Based Searching For Encrypted Data Outsourced To An Untrusted Domain, Zeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Naeem Ramzan, Wajahat Ali Khan

All Works

© 2017 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by …

Generalized Differential Calculus And Applications To Optimization, R. Blake Rector

Dissertations and Theses

This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations …

Crowd Data Analytics And Optimization, Habibur Rahman

Computer Science and Engineering Dissertations - Archive

Crowdsourcing can be defined as outsourcing with crowd, where crowd refers to the online workers who are willing to complete simple tasks for small monetary compensation. The overwhelming reach of internet has enabled us to exploit crowd in an unprecedented way. Crowdsourcing, nowadays, is considered as a tool to solve both simple tasks (such as labeling ground truth, image recognition etc.) and complex tasks (such as collaborative writing, citizen journalism etc.). Furthermore, it is also used to solve computational problems such as Entity Resolution, Top-k, Group-by etc. While crowdsourcing provides us with plenty of opportunities, it also presents us with …

Satellite Detection And Ranging System, A S M Sarwar Zahan

Theses and Dissertations

The thrust on Nano-satellite technologies is increasing day by day. Research is going on to detect satellites in austere environment. However, the technology of tracking the satellite in both in-formation and out of formation is yet to discover. I propose a novel algorithm to detect satellites in regular orbit and even in different close operations, i.e. docking, inspection and rendezvous. I aim to modify two existing and promising image processing algorithms and apply towards my own satellite detection problem. Various image processing algorithms have incorporated to enable tracking of the Target Flight Unit (T-FU) from the Chase Flight Unit (C-FU). …

Certifying Loop Pipelining Transformations In Behavioral Synthesis, Disha Puri

Dissertations and Theses

Due to the rapidly increasing complexity in hardware designs and competitive time to market trends in the industry, there is an inherent need to move designs to a higher level of abstraction. Behavioral Synthesis is the process of automatically compiling such Electronic System Level (ESL) designs written in high-level languages such as C, C++ or SystemC into Register-Transfer Level (RTL) implementation in hardware description languages such as Verilog or VHDL. However, the adoption of this flow is dependent on designers' faith in the correctness of behavioral synthesis tools.

Loop pipelining is a critical transformation employed in behavioral synthesis process, and …

A Statistical Method For The Conservative Adjustment Of False Discovery Rate (Q-Value)., Yinglei Lai

Epidemiology Faculty Publications

BACKGROUND: q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation.

RESULTS: We proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p-value by a permutation procedure. …

Computational Algorithms For Improved Representation Of The Model Error Covariance In Weak-Constraint 4d-Var, Jeremy A. Shaw

Dissertations and Theses

Four-dimensional variational data assimilation (4D-Var) provides an estimate to the state of a dynamical system through the minimization of a cost functional that measures the distance to a prior state (background) estimate and observations over a time window. The analysis fit to each information input component is determined by the specification of the error covariance matrices in the data assimilation system (DAS). Weak-constraint 4D-Var (w4D-Var) provides a theoretical framework to account for modeling errors in the analysis scheme. In addition to the specification of the background error covariance matrix, the w4D-Var formulation requires information on the model error statistics and …

Incorporating A Spatial Prior Into Nonlinear D-Bar Eit Imaging For Complex Admittivities, Sarah J. Hamilton, Jennifer L. Mueller, Melody Alsaker

Mathematics, Statistics and Computer Science Faculty Research and Publications

Electrical Impedance Tomography (EIT) aims to recover the internal conductivity and permittivity distributions of a body from electrical measurements taken on electrodes on the surface of the body. The reconstruction task is a severely ill-posed nonlinear inverse problem that is highly sensitive to measurement noise and modeling errors. Regularized D-bar methods have shown great promise in producing noise-robust algorithms by employing a low-pass filtering of nonlinear (nonphysical) Fourier transform data specific to the EIT problem. Including prior data with the approximate locations of major organ boundaries in the scattering transform provides a means of extending the radius of the low-pass …

Normal Surfaces And 3-Manifold Algorithms, Josh D. Hews

Honors Theses

This survey will develop the theory of normal surfaces as they apply to the S3 recognition algorithm. Sections 2 and 3 provide necessary background on manifold theory. Section 4 presents the theory of normal surfaces in triangulations of 3-manifolds. Section 6 discusses issues related to implementing algorithms based on normal surfaces, as well as an overview of the Regina, a program that implements many 3-manifold algorithms. Finally section 7 presents the proof of the 3-sphere recognition algorithm and discusses how Regina implements the algorithm.

Advances On Software Defined Wireless Networking, D. B. Rawat, M. Song, C. Xin

Electrical & Computer Engineering Faculty Publications

Software defined wireless networking is regarded as an emerging technology to enhance spectrum efficiency and improve the overall network performance. In this paper, we summarise the special issue on recent advances in software defined wireless networking. Specifically, this special issue publishes following findings: i) a novel context aware medium access control scheme for multichannel buffer-aided cognitive networks to reduce the delay by exploiting the packets’ contexts; ii) a utility-based uplink scheduling algorithm that accommodates different performance metrics and adapts its decisions based on user-specified profiles by incorporating an intermediary layer between the MAC and network layer; iii) an opportunistic spectrum …

Siam Data Mining "Brings It" To Annual Meeting, Jeremy Kepner, Sanjukta Bhowmick, Aydın Buluç, Rajmonda Caceres, R. Jordan Crouser, Vijay Gadepally, Ben Miller, Jennifer Webster

Computer Science: Faculty Publications

The Data Mining Activity Group is one of SIAM's most vibrant and dynamic activity groups. To better share our enthusiasm for data mining with the broader SIAM community, our activity group organized six minisymposia at the 2016 Annual Meeting. These minisymposia included 48 talks organized by 11 SIAM members on - GraphBLAS (Aydın Buluç) - Algorithms and statistical methods for noisy network analysis (Sanjukta Bhowmick & Ben Miller) - Inferring networks from non-network data (Rajmonda Caceres, Ivan Brugere & Tanya Y. Berger-Wolf) - Visual analytics (Jordan Crouser) - Mining in graph data (Jennifer Webster, Mahantesh Halappanavar & Emilie Hogan) - …

Algorithm For Premature Ventricular Contraction Detection From A Subcutaneous Electrocardiogram Signal, Iris Lynn Shelly

Dissertations and Theses

Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat.

An implantable cardiac …

Massively Parallel Algorithm For Solving The Eikonal Equation On Multiple Accelerator Platforms, Anup Shrestha

Boise State University Theses and Dissertations

The research presented in this thesis investigates parallel implementations of the Fast Sweeping Method (FSM) for Graphics Processing Unit (GPU)-based computational plat forms and proposes a new parallel algorithm for distributed computing platforms with accelerators. Hardware accelerators such as GPUs and co-processors have emerged as general- purpose processors in today’s high performance computing (HPC) platforms, thereby increasing platforms’ performance capabilities. This trend has allowed greater parallelism and substantial acceleration of scientific simulation software. In order to leverage the power of new HPC platforms, scientific applications must be written in specific lower-level programming languages, which used to be platform specific. Newer …

A Survey On Wireless Indoor Localization From The Device Perspective, Jiang Xiao, Zimu Zhou, Youwen Yi, Lionel M. Ni

Research Collection School Of Computing and Information Systems

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while …

Some Contemporary Issues In Software Reliability., Vignesh Subrahmaniam Dr.

Doctoral Theses

No abstract provided.

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 …

Privacy-Aware Relevant Data Access With Semantically Enriched Search Queries For Untrusted Cloud Storage Services, Zeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Sungyoung Lee, Tae Choong Chung

All Works

© 2016 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify …

A Dynamic Run-Profile Energy-Aware Approach For Scheduling Computationally Intensive Bioinformatics Applications, Sachin Pawaskar, Hesham Ali

Computer Science Faculty Proceedings & Presentations

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In …

Novel Monte Carlo Methods For Large-Scale Linear Algebra Operations, Hao Ji

Computer Science Theses & Dissertations

Linear algebra operations play an important role in scientific computing and data analysis. With increasing data volume and complexity in the "Big Data" era, linear algebra operations are important tools to process massive datasets. On one hand, the advent of modern high-performance computing architectures with increasing computing power has greatly enhanced our capability to deal with a large volume of data. One the other hand, many classical, deterministic numerical linear algebra algorithms have difficulty to scale to handle large data sets.

Monte Carlo methods, which are based on statistical sampling, exhibit many attractive properties in dealing with large volume of …

Partitions Of Finite Frames, James Michael Rosado

Theses and Dissertations

An open question stated by Marcus, Spielman, and Srivastava [10] asks "whether one can design an efficient algorithm to find the partitions guaranteed by Corollary 1.5." This corollary states that given a set of vectors in C whose outer products sum to the identity there exists a partition of these vectors such that norms of the outer-product sums of each subset satisfy an inequality bound. Here particular types of vector sets called finite frames are analyzed and constructed to satisfy the inequality described in Corollary 1.5. In this thesis, rigorous proofs and formulations of outer-product norms are utilized to find …

The Dc Algorithm & The Constrained Fermat-Torricelli Problem, Nathan Peron Lawrence, George Blikas

Student Research Symposium

The theory of functions expressible as the Difference of Convex (DC) functions has led to the development of a rich field in applied mathematics known as DC Programming.We survey the work of Pham Dinh Tao and Le Thi Hoai An in order to understand the DC Algorithm (DCA) and its use in solving clustering problems. Further, we present several other methods that generalize the DCA for any norm. These powerful tools enable researchers to reformulate objective functions, not necessarily convex, into DC Programs.

The Fermat-Torricelli problem is visited in light of convex analysis and various norms. Pierre de Fermat proposed …

A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

We introduce horizon decomposition in the context of Dantzig-Wolfe decomposition, and apply it to the capacitated lot-sizing problem with setup times. We partition the problem horizon in contiguous overlapping intervals and create subproblems identical to the original problem, but of smaller size. The user has the flexibility to regulate the size of the master problem and the subproblem via two scalar parameters. We investigate empirically which parameter configurations are efficient, and assess their robustness at different problem classes. Our branch-and-price algorithm outperforms state-of-the-art branch-and-cut solvers when tested to a new data set of challenging instances that we generated. Our methodology …

On Effective Location-Aware Music Recommendation, Zhiyong Cheng, Jialie Shen

Research Collection School Of Computing and Information Systems

Rapid advances in mobile devices and cloud-based music service now allow consumers to enjoy music any-time and anywhere. Consequently, there has been an increasing demand in studying intelligent techniques to facilitate context-aware music recommendation. However, one important context that is generally overlooked is user's venue, which often includes surrounding atmosphere, correlates with activities, and greatly influences the user's music preferences. In this article, we present a novel venue-aware music recommender system called VenueMusic to effectively identify suitable songs for various types of popular venues in our daily lives. Toward this goal, a Location-aware Topic Model (LTM) is proposed to (i) …

Opinion Question Answering By Sentiment Clip Localization, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This article considers multimedia question answering beyond factoid and how-to questions. We are interested in searching videos for answering opinion-oriented questions that are controversial and hotly debated. Examples of questions include "Should Edward Snowden be pardoned?" and "Obamacare-unconstitutional or not?". These questions often invoke emotional response, either positively or negatively, hence are likely to be better answered by videos than texts, due to the vivid display of emotional signals visible through facial expression and speaking tone. Nevertheless, a potential answer of duration 60s may be embedded in a video of 10min, resulting in degraded user experience compared to reading the …

Digital Circles And Balls: Characterization, Properties, And Applications To Image Analysis., Sahadev Bera Dr.

Doctoral Theses

In this thesis, we have reported some new theoretical findings, empirical formulations, useful heuristics, and efficient algorithms related to digital circle, digital disc, and digital sphere, along with their practical applications to the analysis of geometric information embedded in a digital image. Detecting digital circles and circular arcs from a digital image is very important in shape recognition. Several image processing techniques were proposed over the years to extract circles and circular arc from a digital image and to interpret related issues. We have proposed a novel technique for the segmentation of a digital circle, which is based on a …

A Feature Selection Algorithm To Compute Gene Centric Methylation From Probe Level Methylation Data, Brittany Baur, Serdar Bozdag

Mathematics, Statistics and Computer Science Faculty Research and Publications

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe …

The Log-Exponential Smoothing Technique And Nesterov’S Accelerated Gradient Method For Generalized Sylvester Problems, N. T. An, Daniel J. Giles, Nguyen Mau Nam, R. Blake Rector

Mathematics and Statistics Faculty Publications and Presentations

The Sylvester or smallest enclosing circle problem involves finding the smallest circle enclosing a finite number of points in the plane. We consider generalized versions of the Sylvester problem in which the points are replaced by sets. Based on the log-exponential smoothing technique and Nesterov’s accelerated gradient method, we present an effective numerical algorithm for solving these problems.

Minimizing Differences Of Convex Functions With Applications To Facility Location And Clustering, Mau Nam Nguyen, R. Blake Rector, Daniel J. Giles

Mathematics and Statistics Faculty Publications and Presentations

In this paper we develop algorithms to solve generalized Fermat-Torricelli problems with both positive and negative weights and multifacility location problems involving distances generated by Minkowski gauges. We also introduce a new model of clustering based on squared distances to convex sets. Using the Nesterov smoothing technique and an algorithm for minimizing differences of convex functions called the DCA introduced by Tao and An, we develop effective algorithms for solving these problems. We demonstrate the algorithms with a variety of numerical examples.

Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li

Research Collection School Of Computing and Information Systems

The problem of finding matches of a regular expression (RE) on a string exists in many applications such as text editing, biosequence search, and shell commands. Existing techniques first identify candidates using substrings in the RE, then verify each of them using an automaton. These techniques become inefficient when there are many candidate occurrences that need to be verified. In this paper we propose a novel technique that prunes false negatives by utilizing negative factors, which are substrings that cannot appear in an answer. A main advantage of the technique is that it can be integrated with many existing algorithms …

Some Results On Analysis And Implementation Of Hc-128 Stream Cipher., Shashwat Raizada Dr.

Doctoral Theses

The HC-128 stream cipher is a successful entrant in the eStream candidate list (software profile) and is the lighter variant of HC-256 stream cipher. Apart from the analysis by the designer of the cipher (Hongjun Wu) to conjecture the security of this cipher, there are only a few other observations on this cipher despite being the focus of researchers during the three phases of eStream evaluation and later efforts in the community. Till date none of the security claims in favor of HC-128 by the designer could be broken. One may expect HC-128 stream cipher to be popular in commercial …