Computer Sciences Commons^™

Comparative Analyses Of De Novo Transcriptome Assembly Pipelines For Diploid Wheat, Natasha Pavlovikj

School of Computing: Dissertations, Theses, and Student Research

Gene expression and transcriptome analysis are currently one of the main focuses of research for a great number of scientists. However, the assembly of raw sequence data to obtain a draft transcriptome of an organism is a complex multi-stage process usually composed of pre-processing, assembling, and post-processing. Each of these stages includes multiple steps such as data cleaning, error correction and assembly validation. Different combinations of steps, as well as different computational methods for the same step, generate transcriptome assemblies with different accuracy. Thus, using a combination that generates more accurate assemblies is crucial for any novel biological discoveries. Implementing …

Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao

School of Computing: Dissertations, Theses, and Student Research

Exhaustive testing is an important type of simulation, where a user exhaustively simulates a protocol for all possible cases with respect to some uncertain factors, such as all possible packet delays or packet headers. It is useful for completely evaluating the protocol performance, finding the worst-case performance, and detecting possible design or implementation bugs of a protocol. It is, however, time consuming to use the brute force method with current NS-3, a widely used network simulator, for exhaustive testing. In this paper, we present our work on Sym-NS-3 for more efficient exhaustive testing, which leverages a powerful program analysis technique …

Balancing Data- Vs. Art-Driven Decisions In Video Game Design, Jaden D. Goter

Honors Program: Senior Projects (Public)

Video games, like software, need to be designed. Video game development studios tend to use data-driven or art-driven decision-making to design their games. Data-driven decision-making is where active and passive data is collected in order to make informed decisions about the design of a game. Art-driven decision-making is when designers use their artistic intuition to design games, potentially ignoring player data. This paper elaborates on the advantages and disadvantages of both approaches and provides case studies of games designed under both approaches. Based on these studies, for a game to be successful, a combined approach of data- and art-driven decision-making …

Systems-Based Approach For Optimization Of Assembly-Free Bacterial Mlst Mapping, Natasha Pavlovikj, Joao Carlos Gomes-Neto, Jitender Deogun, Andrew Benson

School of Computing: Faculty Publications

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turnaround, while aiming at generating two main outcomes: (1) species-level identification and (2) variant mapping at different levels of genotypic resolution for population-based tracking and surveillance, in addition to predicting traits such as antimicrobial resistance (AMR). Multilocus sequence typing (MLST) aids this process by identifying sequence types (ST) based on seven ubiquitous genome-scattered loci. In this paper, we selected one assembly-dependent and one assembly-free method for ST mapping and applied them with the default settings and ST schemes they are distributed with, and systematically assessed their accuracy and …

Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta

School of Computing: Dissertations, Theses, and Student Research

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a …

Characterizing And Predicting Human Visual Perception Of Unmanned Aerial Vehicle Gestures, Paul Fletcher

School of Computing: Dissertations, Theses, and Student Research

Unmanned Aerial Vehicles (UAVs) are being used in public domains and hazardous environments where effective communication strategies are critical. UAV gesture techniques have been shown to communicate meaning to human observers and may be ideal in contexts that require lightweight systems such as unmanned aerial flight, however, this work may be limited to an idealized range of viewer perspectives. As gesture is a visual communication technique it is necessary to consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This thesis presents the results of three online user-studies that examine participants’ ability …

Ubjective Information And Survival In A Simulated Biological System, Tyler S. Barker, Massimiliano Pierobon, Peter J. Thomas

School of Computing: Faculty Publications

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. …

Collaborative Computing Support For Analysis Facilities Exploiting Software As Infrastructure Techniques, Maria Acosta Flechas, Garhan Attebury, Kenneth Bloom, Brian Bockelman, Lindsey Gray, Burt Holzman, Carl Lundstedt, Oksana Shadura, Nicholas Smith, John Thiltges

Holland Computing Center: Faculty Publications

Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: “Coffea-casa” at University of Nebraska Lincoln and the “Elastic …

Split Classification Model For Complex Clustered Data, Katherine Gerot

Honors Program: Senior Projects (Public)

Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.

Pitfalls And Guidelines For Using Time-Based Git Data, Samuel W. Flint, Jigyasa Chauhan, Robert Dyer

School of Computing: Faculty Publications

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

Rethinking Sampled-Data Control For Unmanned Aircraft Systems, Xinkai Zhang, Justin M. Bradley

School of Computing: Faculty Publications

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar …

Security, Trust And Privacy For Cloud, Fog And Internet Of Things, Chien-Ming Chen, Shehzad Ashraf Chaudhry, Kuo-Hui Yeh, Muhammad Naveed Aman

School of Computing: Faculty Publications

No abstract provided.

Quasi-Spherical Absorbing Receiver Model Of Glioblastoma Cells For Exosome-Based Molecular Communications, Caio Fonseca, Michael Taynan Barros, Andreani Odysseos, Srivatsan Kidambi, Sasitharan Balasubramaniam

School of Computing: Faculty Publications

In this paper, we propose a mathematical and computational model for the GBM initiating of recurring focus as a quasi-spherical absorbing receiver considering the irregular shape as a Bernoulli trial process that accounts for the uncontrollable tumor growth over an initial spherical surface. Our results show that when GBM grow to irregular quasi-sphere shapes, they will increase the channel capacity, which is fully aligned with the evolution and configuration of GSC niches in GBM cultures.

Real-Time Dynamic Map With Crowdsourcing Vehicles In Edge Computing, Qiang Liu, Tao Han, Jiang (Linda) Xie, Baekgyu Kim

School of Computing: Faculty Publications

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We …

High Throughput Analysis Of Leaf Chlorophyll Content In Sorghum Using Rgb, Hyperspectral, And Fluorescence Imaging And Sensor Fusion, Huichun Zhang, Yufeng Ge, Xinyan Xie, Abbas Atefi, Nuwan Wijewardane,, Suresh Thapa

School of Computing: Faculty Publications

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity …

Using Deep Learning To Detect Digitally Encoded Dna Trigger For Trojan Malware In Bio‑Cyber Attacks, M. S. Islam, S. Ivanov, H. Awan, J. Drohan, Sasitharan Balasubramaniam, L. Coffey, Srivatsan Kidambi, W. Sri-Saan

School of Computing: Faculty Publications

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will …

Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper applies data mining of weight measures to discover possible long-distance trade routes among Bronze Age civilizations from the Mediterranean area to India. As a result, a new northern route via the Black Sea is discovered between the Minoan and the Indus Valley civilizations. This discovery enhances the growing set of evidence for a strong and vibrant connection among Bronze Age civilizations.

Mr-Pipa: An Integrated Multi-Level Rram (Hfox) Based Processing-In-Pixel Accelerator, Minhaz Abedin, Arman Roohi, Maximilian Liehr, Nathaniel Cady, Shaahin Angizi

School of Computing: Faculty Publications

This work paves the way to realize a processing-in-pixel accelerator based on a multi-level HfO_x RRAM as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing at edge devices. The proposed design intrinsically implements and supports a coarse-grained convolution operation in low-bit-width neural networks leveraging a novel compute-pixel with non-volatile weight storage at the sensor side. Our evaluations show that such a design can remarkably reduce the power consumption of data conversion and transmission to an off-chip processor maintaining accuracy compared with the recent in-sensor computing designs. Our proposed design, namely MR-PIPA, achieves a frame rate …

Decision-Theoretic Planning With Communication In Open Multiagent Systems, Anirudh Kakarlapudi, Gayathri Anil, Adam Eck, Prashant Doshi, Leen-Kiat Soh

School of Computing: Faculty Publications

In open multiagent systems, the set of agents operating in the environment changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. Because an agent’s optimal action depends on the actions of others, each agent must not only predict the actions of its peers, but, before that, reason whether they are even present to perform an action. Addressing openness thus requires agents to model each other’s presence, which can be enhanced through agents communicating about …

Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu

School of Computing: Faculty Publications

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each …

Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu

School of Computing: Faculty Publications

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each …

Aerial Flight Paths For Communication, Alisha Bevins, Brittany Duncan

School of Computing: Faculty Publications

This article presents an understanding of naive users’ perception of the communicative nature of unmanned aerial vehicle (UAV) motions refined through an iterative series of studies. This includes both what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area prioritized gestures from participants to the vehicle or augmenting the vehicle with additional communication modalities, rather than communicating without clear definitions of the states attempting to be conveyed. In an attempt to elicit more concrete states and better understand specific motion perception, this work includes …

Optimal Container Migration For Mobile Edge Computing: Algorithm, System Design And Implementation, Taewoon Kim, Motassem Al-Tarazi, Jenn-Wei Lin, Wooyeol Choi

School of Computing: Faculty Publications

Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end- users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, …

Computational Solutions To Exosomal Microrna Biomarker Detection In Pancreatic Cancer, Thuy T. An

School of Computing: Dissertations, Theses, and Student Research

Pancreatic cancer is the fourth leading cause of cancer death in the United States and the 5-year survival rate is only 5% to 10%. There are only a few non-specific symptoms associated with the early-stage cancer, therefore most patients are diagnosed in a late stage. Due to the lack of effective treatments and the fact that the early stage has a 39% 5-year survival rate, the biggest hope to control this disease is early detection. Therefore, discovery of effective and reliable non-invasive biomarkers for early detection of pancreatic cancer has been a major topic. Very recently, exosomal microRNAs have become …

Semantically Meaningful Sentence Embeddings, Rojina Deuja

School of Computing: Dissertations, Theses, and Student Research

Text embedding is an approach used in Natural Language Processing (NLP) to represent words, phrases, sentences, and documents. It is the process of obtaining numeric representations of text to feed into machine learning models as vectors (arrays of numbers). One of the biggest challenges in text embedding is representing longer text segments like sentences. These representations should capture the meaning of the segment and the semantic relationship between its constituents. Such representations are known as semantically meaningful embeddings. In this thesis, we seek to improve upon the quality of sentence embeddings that capture semantic information.

The current state-of-the-art models are …

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.14 was released on 27 October, 2021. It is compatible with R 4.1.0 and consists of 2083 software packages, 408 experiment data packages, 904 up-to-date annotation packages, and 29 workflows.

Changes In R, Tomas Kalibera, Sebastian Meyer, Kurt Hornik, Gennadiy Starostin, Luke Tierney

The R Journal

We present important changes in the development version of R (referred to as R-devel, to become R 4.2) and give a summary of the new search engine interfaced by RSiteSearch(). Some statistics on bug tracking activities in 2021 are also provided.

Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin

The R Journal

The R package RPESE (Risk and Performance Estimators Standard Errors) implements a new method for computing accurate standard errors of risk and performance estimators when returns are serially dependent. The new method makes use of the representation of a risk or performance estimator as a summation of a time series of influence-function (IF) transformed returns, and computes estimator standard errors using a sophisticated method of estimating the spectral density at frequency zero of the time series of IF-transformed returns. Two additional packages used by RPESE are introduced, namely RPEIF which computes and provides graphical displays of the IF of risk …

The Vote Package: Single Transferable Vote And Other Electoral Systems In R, Adrian E. Raftery, Hana ŠevčÍková, Bernard W. Silverman

The R Journal

We describe the vote package in R, which implements the plurality (or first-past-the-post), two-round runoff, score, approval, and Single Transferable Vote (STV) electoral systems, as well as methods for selecting the Condorcet winner and loser. We emphasize the STV system, which we have found to work well in practice for multi-winner elections with small electorates, such as committee and council elections, and the selection of multiple job candidates. For single-winner elections, STV is also called Instant Runoff Voting (IRV), Ranked Choice Voting (RCV), or the alternative vote (AV) system. The package also implements the STV system with equal preferences, for …