Computer Sciences Commons^™

Building Patterned Shapes In Robot Swarms With Uniform Control Signals, David Caballero, Angel A. Cantu, Timothy Gomez, Austin Luchsinger, Robert Schweller, Tim Wylie

Computer Science Faculty Publications

This paper investigates a restricted version of robot motion planning, in which particles on a board uniformly respond to global signals that cause them to move one unit distance in a particular direction. We look at the problem of assembling patterns within this model. We first derive upper and lower bounds on the worst-case number of steps needed to reconfigure a general purpose board into a target pattern. We then show that the construction of k-colored patterns of size-n requires Ω(n log k) steps in general, and Ω(n log k + √ k) steps if the constructed shape must always …

Hardness Of Sparse Sets And Minimal Circuit Size Problem, Bin Fu

Computer Science Faculty Publications

We study the magnification of hardness of sparse sets in nondeterministic time complexity classes on a randomized streaming model. One of our results shows that if there exists a 2no(1) -sparse set in NDTIME(2no(1)) that does not have any randomized streaming algorithm with no(1) updating time, and no(1) space, then NEXP≠BPP , where a f(n)-sparse set is a language that has at most f(n) strings of length n. We also show that if MCSP is ZPP -hard under polynomial time truth-table reductions, then EXP≠ZPP .

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad A. Al-Ramahi, Cherie Noteboom

Computer Information Systems Faculty Publications (Archived)

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic's MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

A Framework To Support Automatic Certification For Self-Adaptive Systems, Ioannis Nearchou

Masters Theses

Presently, cyber-physical systems are increasingly being integrated into societies, from the economic sector to the nuclear energy sector. Cyber-physical systems are systems that combine physical, digital, human, and other components, which operate through physical means and software. When system errors occur, the consequences of malfunction could negatively impact human life. Academic studies have relied on the MAPE-K feedback loop model to develop various system components to satisfy the self-adaptive features, such that violation of the safety requirements can be minimized. Assurance of system requirement satisfaction is argued through an industrial standard form, called an assurance case, which is usually applied …

Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson

Graduate Theses/Dissertations

Cyberbullying is an ongoing and devastating issue in today's online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards …

Application Of Software Engineering Principles To Synthetic Biology And Emerging Regulatory Concerns, Justin Firestone

School of Computing: Dissertations, Theses, and Student Research

As the science of synthetic biology matures, engineers have begun to deliver real-world applications which are the beginning of what could radically transform our lives. Recent progress indicates synthetic biology will produce transformative breakthroughs. Examples include: 1) synthesizing chemicals for medicines which are expensive and difficult to produce; 2) producing protein alternatives; 3) altering genomes to combat deadly diseases; 4) killing antibiotic-resistant pathogens; and 5) speeding up vaccine production.

Although synthetic biology promises great benefits, many stakeholders have expressed concerns over safety and security risks from creating biological behavior never seen before in nature. As with any emerging technology, there …

Applying Deep Learning For Cell Detection In Time-Lapse Microscopic Images, Jay Patel

Honors Theses

The budding yeast Saccharomyces cerevisiae is an effective model for studying cellular aging. We can measure the lifespan of yeast cells in two ways: replicative and chronological lifespans. Chronological focuses on the time that a cell can survive. The replicative lifespan (RLS) is the number of cell divisions that a single mother cell can go through before ceases to be dividing. RLS is a measurement of individual cells and is more informative on the aging process than in chronological lifespan. Many genes that influence yeast RLS have been shown to be highly conserved and have a similar effect on aging …

Colleague To Banner Migration: Data Conversion Guide For Institutional Research, Laura Osborn

Masters Theses & Doctoral Dissertations

When the SDBOR decided to migrate their current student information system into a shared system with HR and Finance, adjustments needed to be made to accommodate for current Banner settings and work around tables that were already populated with HRFIS data. The change in data type of the student identifier from that of a 7-digit numeric field to a 9- digit alpha-numeric field poses problems for running aggregate data calculations. Additional complications include having some information such as first-generation status that was not migrated between the systems, and cases such as college coding where tables that were designed for student …

A Gis-Based Method For Archival And Visualization Of Microstructural Data From Drill Core Samples., Elliott Holmes

Electronic Theses and Dissertations

Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and explore the available microstructural data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed procedures for: 1. A GIS-based approach for spatially referenced visualization and storage of microstructural data from drill core billet samples; and 2. Producing 3D models of …

Computational Complexity Characterization Of Protecting Elections From Bribery, Lin Chen, Ahmed Sunny, Lei Xu, Shouhuai Xu, Zhimin Gao, Yang Lu, Weidong Shi, Nolan Shah

Computer Science Faculty Publications

The bribery problem in election has received considerable attention in the literature, upon which various algorithmic and complexity results have been obtained. It is thus natural to ask whether we can protect an election from potential bribery. We assume that the protector can protect a voter with some cost (e.g., by isolating the voter from potential bribers). A protected voter cannot be bribed. Under this setting, we consider the following bi-level decision problem: Is it possible for the protector to protect a proper subset of voters such that no briber with a fixed budget on bribery can alter the election …

Hand-Over-Face Segmentation, Sakher Ghanem

Computer Science and Engineering Dissertations - Archive

Accurate hand segmentation is vital in many applications in which the hands play a central role, such as sign language recognition, action recognition, and gesture recognition. A relatively unexplored obstacle to correct hand segmentation is when the hand overlaps the face. The shortage of a dataset for this research area has been one motivation for this work. However, this dissertation investigates and proposes improvements for the hand-over-face segmentation task. Toward an in-depth study of the hand segmentation problem, the work presented in this dissertation will yield several contributions. First, it introduces a survey on sign language recognition systems using mobile …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Graduate Student Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Diffusion Of Falsehoods On Social Media, Kelvin Kizito King

Theses and Dissertations

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher …

Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective, James Gitau Wairimu

Theses and Dissertations

This dissertation investigates the influence of performance feedback in user motivation and creativity development in idea crowdsourcing engagement. Creativity occurs when users of idea crowdsourcing communities engage in direct and indirect interactions that expose them to a pool of knowledge that enhances their cognitive development leading to the contribution of novel ideas for innovation in organizations. Additionally, participant motivation to engage in ideas crowdsourcing is increased through rewards and conditions that make the ideation process more inclusive and enjoyable. An idea network design is developed by applying social network analysis principles. The idea network design consists of mechanisms for motivating …

Analysis Of The Functional Relationship Of Protein Kinase Families Using Phospho-Proteomics Data, David A. Parra Peña

Theses and Dissertations

As cancer research advances, Mass-spectrometry based proteomics is becoming a widely used technique for proteome characterization. Phosphoproteomics is a specific type of proteomics that characterizes proteins with the reversible post-translational modification of phosphorylation PTM), which has allowed the identifications of thousands of phosphorylation sites. These phosphorylation sites, also known as substrates, are known to interact with a protein type named kinases. Studies have shown that abnormal phosphorylation activity is related to cancer diseases. Moreover, these kinases are divided into families, based on the similarity of their catalytic domain, as this part of their amino acid sequence determines a large part …

A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to …

A Fast Anderson-Chebyshev Acceleration For Nonlinear Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations $x_{t+1}=G(x_t)$, e.g., gradient descent can be viewed as iteratively applying the operation $G(x) \triangleq x-\alpha\nabla f(x)$. It is known that Anderson acceleration is quite efficient in practice and can be viewed as an extension of Krylov subspace methods for nonlinear problems. In this paper, we show that Anderson acceleration with Chebyshev polynomial can achieve the optimal convergence rate $O(\sqrt{\kappa}\ln\frac{1}{\epsilon})$, which improves the previous result $O(\kappa\ln\frac{1}{\epsilon})$ provided by (Toth and Kelley, 2015) for quadratic functions. Moreover, we provide a convergence analysis for minimizing general nonlinear problems. Besides, …

Refactoring From 9 To 5? What And When Employees And Volunteers Contribute To Oss, Luiz Felipe Dias, Caio Barbosa, Gustavo Pinto, Igor Steinmacher, Baldoino Fonseca, Márcio Ribeiro, Christoph Treude, Daniel Alencar Da Costa

Research Collection School Of Computing and Information Systems

In this paper we characterize the contributions made by employees (developers that work for GitHub, the company) and volunteers (developers that use GitHub, the platform) to OSS projects maintained by GitHub (the company) on GitHub (the platform). By mining activities performed in five well-known company-owned OSS projects, we investigate what they do and when they do it. We found that the majority of the volunteers' contributions are related to reengineering (e.g., refactoring), while employees focus more on management (e.g., documentation). When it comes to the working hours, we found that contributions are made mostly from 9am-5pm, even for the volunteers.

Privacy Preserving Search Services Against Online Attack, Yi Zhao, Jianting Nian, Kaitai Liang, Yanqi Zhao, Liqun Chen, Bo Yang

Research Collection School Of Computing and Information Systems

Searchable functionality is provided in many online services such as mail services or outsourced data storage. To protect users privacy, data in these services is usually stored after being encrypted using searchable encryption. This enables the data user to securely search encrypted data from a remote server without leaking data and query information. Public key encryption with keyword search is one of the research branches of searchable encryption; this provides privacy-preserving searchable functionality for applications such as encrypted email systems. However, it has an inherent vulnerability in that the information of a query may be leaked using a keyword guessing …

Three-Dimensional (3d) Reconstruction Of Ultrasound Foetal Images Using Visualisation Toolkit (Vtk), Henry Dusim Grace Anabela

Student Works (2020-2029)

Three-dimensional (3D) reconstruction of two-dimensional (2D) ultrasound images has been used for easier analysing of anatomy related to abnormalities. 3D image reconstruction is required as it helps doctors interpret results and assess any defects quickly. Not only does it improve the accuracy of diagnosis, but it also saves a lot of time. In this thesis, 295 slices of 2D ultrasound images of a foetal are used to do 3D reconstruction using surface rendering, volume rendering, and multiplanar rendering technique. A similar software known as 3D Slicer is also used to reconstruct the ultrasound image for comparison purpose. VTK can easily …

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

Cloud Resource Prediction Using Explainable And Cooperative Artificial Neural Networks, Nathan R. Nelson

Graduate Theses/Dissertations

This work proposes a system for predicting cloud resource utilization by using runtime assembled cooperative artificial neural networks (RACANN). RACANN breaks up the problem into smaller contexts, each represented by a small-scale artificial neural network (ANN). The relevant ANNs are joined together at runtime when the context is present in the data for training and predictions. By analyzing the structure of a complete ANN, the influence of inputs is calculated and used to create linguistic descriptions (LD) of model behavior, so RACANN becomes explainable (eRACANN). The predictive results of eRACANN are compared against its prototype and a single deep ANN …

Controlling Framing Effects For Web-Based Group Decision Support Systems, Badria Sulaiman Alfurhood

Theses and Dissertations

The way the proposal and the available alternatives are presented or framed for a group decision may alter the ultimate group decision. Strategic framing is an attack method used to manipulate people into irrationally preferring a particular alternative. The adverse effects of framing threaten the trustworthiness and results of the group decision support systems (GDSSs). Nonetheless, the avoidance or reduction of framing effects is possible. Negative framing effects arise when users are not consistent with their choices if framing is applied for a collaborative decision using GDSSs. In this interdisciplinary research, human-computer interaction (HCI) informed methods, cognitive science, and political …

Polyhedra Circuits And Their Applications, Bin Fu, Pengfei Gu, Yuming Zhao

Computer Science Faculty Publications

To better compute the volume and count the lattice points in geometric objects, we propose polyhedral circuits. Each polyhedral circuit characterizes a geometric region in Rd . They can be applied to represent a rich class of geometric objects, which include all polyhedra and the union of a finite number of polyhedron. They can be also used to approximate a large class of d-dimensional manifolds in Rd . Barvinok [3] developed polynomial time algorithms to compute the volume of a rational polyhedron, and to count the number of lattice points in a rational polyhedron in Rd with a fixed dimensional …

Quantum And Classical Multilevel Algorithms For (Hyper)Graphs, Ruslan Shaydulin

All Dissertations

Combinatorial optimization problems on (hyper)graphs are ubiquitous in science and industry. Because many of these problems are NP-hard, development of sophisticated heuristics is of utmost importance for practical problems. In recent years, the emergence of Noisy Intermediate-Scale Quantum (NISQ) computers has opened up the opportunity to dramaticaly speedup combinatorial optimization. However, the adoption of NISQ devices is impeded by their severe limitations, both in terms of the number of qubits, as well as in their quality. NISQ devices are widely expected to have no more than hundreds to thousands of qubits with very limited error-correction, imposing a strict limit on …

Spark: Spatial-Aware Online Incremental Attack Against Visual Tracking, Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu

Research Collection School Of Computing and Information Systems

Adversarial attacks of deep neural networks have been intensively studied on image, audio, and natural language classification tasks. Nevertheless, as a typical while important real-world application, the adversarial attacks of online video tracking that traces an object’s moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and …

Meta-Learning On Heterogeneous Information Networks For Cold-Start Recommendation, Yuanfu Lu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of …

An Exact Algorithm For Agile Earth Observation Satellite Scheduling With Time-Dependent Profits, Guansheng Peng, Guopeng Song, Lining Xing, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The scheduling of an Agile Earth Observation Satellite (AEOS) consists of selecting and scheduling a subset of possible targets for observation in order to maximize the collected profit related to the images while satisfying its operational constraints. In this problem, a set of candidate targets for observation is given, each with a time-dependent profit and a visible time window. The exact profit of a target depends on the start time of its observation, reaching its maximum at the midpoint of its visible time window. This time dependency stems from the fact that the image quality is determined by the look …

Tenet: Triple Excitation Network For Video Salient Object Detection, Sucheng Ren, Chu Han, Xin Yang, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning ambiguities at the beginning of training by selectively exciting feature activations using ground truth. Then we gradually reduce the weight of ground truth excitations by a curriculum rate and replace it by a curriculum complementary map for better and faster convergence. In particular, the spatial excitation strengthens feature activations for clear object …

Interface Design Of Web-Based Educational Platforms For Young Students, Lurong He, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

With the outbreak of the Covid-19 pandemic, almost all the school programs in the United States are closed. Most educational programs have moved online. Both teachers and students are facing challenges adapting to this new teaching and learning mode. This paper will focus on the interface design of webbased educational platforms for students and teachers from elementary and middle schools. The age of students from elementary and middle schools typically ranges from 5 to 13 years old. Their computer literacy level is still preliminary. So traditional online teaching platforms may not be appropriate for them. The design of learning systems …