Computer Sciences Commons^™

Women In It: Be The Change, Marcia L. Dority Baker

Information Technology Services: Publications

The influence of established women in IT — specifically Florence Hudson and Melissa Woo — encouraged a librarian to apply for a position in Information Technology Services at the University of Nebraska–Lincoln.

A hands-on approach to planning the IT Leadership conference developed a strong collaborative network that also helped grow attendance.

Paying attention to the pros and cons of the 2016 conference guided the planners in modifying their approach to the upcoming October 2017 conference and its focus on diversity and inclusion.

A main goal for future IT Leadership conferences focusing on women and diversity in IT is providing attendees …

Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show …

Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing …

An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.

Performance Verification For Robot Missions In Uncertain Environments, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract—Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results …

A Robust Framework For Mining Youtube Data, Zifeng Tian

Theses, Dissertations and Capstones

YouTube is currently the most popular and successful video sharing website. As YouTube has broad and profound social impact, YouTube analytics has become a hot research area. The videos on YouTube have become a treasure of data. However, getting access to the immense and massive YouTube data is a challenge. Previous research, studies, and analysis so far, are only conducted on very small volumes of YouTube video data. To date, there exists no mechanism to systematically and continuously collect, process and store the rich set of YouTube data. This thesis presents a methodology to systematically and continuously mine and store …

Mass Transfer Effects Of Particle Size On Brewing Espresso, Sichen Zhong, Lauren Elizabeth Stork

Rose-Hulman Undergraduate Research Publications

The extraction process for coffee is complicated due to the nature of the coffee. In this paper, we studied the particle size distribution for coffee grinds and further analyzed that with the help of an inverted microscope and a scanning electron microscope. We drew a conclusion that the coffee grinds can be divided into two parts: cell fragments with smaller particles size and intact coffee cells with larger particles. The intact coffee cell was found to be a porous media. Therefore, we tried to brew the espresso with both normal grind size coffee and sieved coffee to study the extraction …

Virtuo-Its: An Interactive Tutoring System To Teach Virtual Memory Concepts Of An Operating System, Venkata Krishna Kanth Musunuru

Browse all Theses and Dissertations

Interactive tutoring systems are software applications that help individuals to learn difficult concepts. They can allow students to interact with ideas from essential mathematics to more complicated subjects like software engineering. This thesis concentrates on one such interactive tutoring system (ITS) designed for teaching concepts related to operating system virtual memory. Operating system concepts can be troublesome to learn without having someone or something to explain them. Even when an instructor is able to provide detailed explanations, it is still exceptionally difficult for students without a computer science foundation to comprehend these concepts. Students require a sophisticated set of mental …

Intelligent Tutoring System Effects On The Learning Process, Ali Talib Qasim Al-Aqbi

Browse all Theses and Dissertations

The traditional education systems that have been used for several centuries have evolved very slowly and might be ineffective for addressing diverse learning styles and levels of preparation. This system is characterized by many students interacting with a single teacher, who is unable to address the individual needs of every student. Therefore, some students can become frustrated and fail to reach their educational potential. An Intelligent Tutoring System (ITS), which is a computer application used to provide students with one-to-one supplemental tutoring tailored to the student's learning style and pace, is of interest to educators for improving student learning. To …

Collaboratively Detecting Http-Based Distributed Denial Of Service Attack Using Software Defined Network, Ademola A. Ikusan

Browse all Theses and Dissertations

Over the years, there have been a lot of attacks on the web servers of critical companies and organization. As much as these organizations tighten their security, it always tends not to be enough to stop all the attacks towards their servers. These attack vectors are so difficult to stop because of the technicalities behind the attack. Furthermore, there are different classes of distributed denial of service(DDoS) attacks such as the high rates (SYN Flood attacks, ARP Flood attacks, etc), low-rate(HTTP-GET, HTTP-POST). Although there are different ways to stop high rate DDoS attacks by traffic sampling and detecting malformed packets …

Finding Street Gang Member Profiles On Twitter, Lakshika Balasuriya

Browse all Theses and Dissertations

The crime and violence street gangs introduce into neighborhoods is a growing epidemic in cities around the world. Today, over 1.4 million people, belonging to more than 33,000 gangs, are active in the United States, of which 88% identify themselves as being members of a street gang. With the recent popularity of social media, street gang members have established online presences coinciding with their physical occupation of neighborhoods. Recent studies report that approximately 45% of gang members participate in online offending activities such as threatening, harassing individuals, posting violent videos or attacking someone on the street for something they said …

Model Preparation And User Interface Aspects For Microsoft Hololens Medical Tutorial Applications, Andrew J. Mcnutt

Browse all Theses and Dissertations

Augmented Reality (AR) is the combination of computer vision and graphics to allow a device to derive context about the environment and to display relevant information to the user. The Microsoft Hololens is the latest wearable AR device that includes a series of infrared sensors to perform hand tracking and Simultaneous Localization and Mapping, a wave guide optics system to project holograms, and an independent untethered operating system. This thesis will explore the hardware and software capabilities of the device, prepare 3D graphical models, and understand the user interface. Specifically, in our research the target application is a tutorial on …

Establishing A-Priori Performance Guarantees For Robot Missions That Include Localization Software, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically analyze the possible behaviors of the system a-priori and determine performance guarantees. Thea authors have applied this second approach to automatically derive performance guarantees for behaviorbased, multi-robot critical mission software using an innovative approach to formal verification for robotic software. Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. Several …

Distance Learning And Attribute Importance Analysis By Linear Regression On Idealized Distance Functions, Rupesh Kumar Singh

Browse all Theses and Dissertations

A good distance metric is instrumental on the performance of many tasks including classification and data retrieval. However, designing an optimal distance function is very challenging, especially when the data has high dimensions.Recently, a number of algorithms have been proposed to learn an optimal distance function in a supervised manner, using data with class labels. In this thesis we proposed methods to learn an optimal distance function that can also indicate the importance of attributes. Specifically, we present several ways to define idealized distance functions, two of which involving distance error correction involving KNN classification, and another involving a two-constant …

Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song

Faculty Publications

Background Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. …

Data-Driven Network-Centric Threat Assessment, Dae Wook Kim

Browse all Theses and Dissertations

As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in …

High Performance Techniques Applied In Partial Differential Equations Library, Shilei Lin

All College Thesis Program, 2016-2019

This thesis explores various Trilinos packages to determine a method for updating the deal.ii library, which specializes in solving partial differential equations by finite element methods. It begins with introducing related concepts and general goals, followed by exploring computational and mathematical methods which are analytical solutions of one-dimensional Boussinesq equations and developing newer prototypes for solvers in deal.ii based on Trilinos packages. After demonstrating the methods, it indicates the reducing solving time in newer prototypes. Based on results from the prototype, similar methods are applied to update the deal.ii library. In the end, a testing program is exploited to demonstrate …

Drop (Drone Open Source Parser) Your Drone: Forensic Analysis Of The Dji Phantom Iii, Devon R. Clark, Christopher S. Meffert, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

The DJI Phantom III drone has already been used for malicious activities (to drop bombs, remote surveillance and plane watching) in 2016 and 2017. At the time of writing, DJI was the drone manufacturer with the largest market share. Our work presents the primary thorough forensic analysis of the DJI Phantom III drone, and the primary account for proprietary file structures stored by the examined drone. It also presents the forensically sound open source tool DRone Open source Parser (DROP) that parses proprietary DAT files extracted from the drone's nonvolatile internal storage. These DAT files are encrypted and encoded. The …

Characterizing Concepts In Taxonomy For Entity Recommendations, Siva Kumar Cheekula

Browse all Theses and Dissertations

Entity recommendation systems are enormously popular on the Web. These systems harness manually crafted taxonomies for improving recommendations. For example, Yahoo created the Open Directory Project for search and recommendation, and Amazon utilizes its own product taxonomy. While these taxonomies are of high quality, it is a labor and time-intensive process to manually create and keep them up to date. Instead, in this era ofWeb 2.0 where users collaboratively create large amounts of information on the Web, it is possible to utilize user-generated content to automatically generate good quality taxonomies. However, harnessing such taxonomies for entity recommendations has not been …

Detection Of Ddos Attacks Against The Sdn Controller Using Statistical Approaches, Basheer Husham Ali Al-Mafrachi

Browse all Theses and Dissertations

In traditional networks, switches and routers are very expensive, complex, and inflexible because forwarding and handling of packets are in the same device. However, Software Defined Networking (SDN) makes networks design more flexible, cheaper, and programmable because it separates the control plane from the data plane. SDN gives administrators of networks more flexibility to handle the whole network by using one device which is the controller. Unfortunately, SDN faces a lot of security problems that may severely affect the network operations if not properly addressed. Threat vectors may target main components of SDN such as the control plane, the data …

Gait Analysis From Wearable Devices Using Image And Signal Processing, Bradley A. Schneider

Browse all Theses and Dissertations

We present the results of analyzing gait motion in-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera and are validated using the ground truth dataset. We perform an additional data collection with both the camera and a body-worn accelerometer …

Web-Delivered Assembly Language Interactive Training And Its Sequence Identification For Software Reverse Engineering, Sunethra Thimmapuram

Browse all Theses and Dissertations

The purpose of this research is to help the beginners of software reverse engineering, the process of extracting knowledge or design information from a product and reproducing it on basis of extracted information. Beginner users often find it difficult to use, as the task may be found daunting at first sight due to a lack of existing learning resources. We need a better way to present information to the novice reverse engineer about how to understand low-level sequences of assembly instructions. This is akin to how the key to learning a foreign language is based upon a fundamental knowledge of …

An Annotated Corpus With Nanomedicine And Pharmacokinetic Parameters, Nastassja Lewinski, Ivan Jimenez, Bridget Mcinnes

Chemical and Life Science Engineering Publications

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the …

Parsing Metamap Files In Hadoop, Amy Olex, Alberto Cano, Bridget T. Mcinnes

Computer Science Publications

The UMLS::Association CUICollector module identifies UMLS Concept Unique Identifier bigrams and their frequencies in a biomedical text corpus. CUICollector was re-implemented in Hadoop MapReduce to improve algorithm speed, flexibility, and scalability. Evaluation of the Hadoop implementation compared to the serial module produced equivalent results and achieved a 28x speedup on a single-node Hadoop system.

Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

Computer Science and Engineering Faculty Publications

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms …

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and …

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering …

Visualization Of Carbon Monoxide Particles Released From Firearms, Sadan Suneesh Menon

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A number of soldiers have come forward to report discomfort, irritation and respiratory problems after taking part in a live firing session. These problems are caused due to the fumes and particulates emitted from the gun upon firing. There exists substantial research work focused on lead and other harmful metallic particulates expelled from a firearm, since they are the most harmful among the other emissions. However, our research focuses on visualizing the carbon monoxide (CO) particles released from a firearm in order to help understand adverse effects they may have on the human body. We use data provided by researchers …

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to …