Other Computer Sciences Commons^™

Coevolution, Dynamics And Allostery Conspire In Shaping Cooperative Binding And Signal Transmission Of The Sars-Cov-2 Spike Protein With Human Angiotensin-Converting Enzyme 2, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of …

Analyzing Yankees And Red Sox Sentiment Over The Course Of A Season, Connor Koch

Honors Projects in Data Science

This paper investigates data collected on twitter which references the Yankees or Red Sox during the 2020 Major League Baseball (MLB) season. The objective is to analyze the sentiment of tweets referencing the Yankees and Red Sox over the course of the season. In addition, an investigation of the networks within the data and the topics that were prevalent will be conducted. The 2020 MLB season was started late because of the COVID-19 pandemic and was a season like no other. The expectation of a dataset revolving around baseball is that the topics discussed would be about baseball. The findings …

Divide And Slide: Layer-Wise Refinement For Output Range Analysis Of Deep Neural Networks, Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, Qi Zhu

Computer Science Faculty Publications

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of …

Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari

Department of Computer Science Faculty Scholarship and Creative Works

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. …

Anatomy Of The Edelman: Measuring The World’S Best Analytics Projects, Michael F. Gorman, Lakshminarayana Nittala, Jeffrey M. Aldenb

MIS/OM/DS Faculty Publications

Each year, the INFORMS Edelman Award celebrates the best and most impactful implementations of operations research, management science, and analytics. As the Edelman Award approaches its 50-year mark, we provide a history and characterization of the award’s finalists and winners. We provide some basic descriptive analytics about the participating organizations and authors, the impact of their work, and the methods they employed. We also conduct predictive analytics on finalist submissions, gauging contributors to success in establishing winning entries. We find that predicting Edelman winners a priori is extremely difficult; however, given a set of finalists, predictive models based on monetary …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Teaching Computational Machine Learning (Without Statistics), Katherine M. Kinnaird

Statistical and Data Sciences: Faculty Publications

This paper presents an undergraduate machine learning course that emphasizes algorithmic understanding and programming skills while assuming no statistical training. Emphasizing the development of good habits of mind, this course trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts while adding increasing depth and nuance. Beginning with unsupervised learning, this course is sequenced as a series of machine learning ideas and concepts with specific algorithms acting as concrete examples. This paper also details course organization including evaluation practices and logistics.

Functional Morphology Of Gliding Flight Ii. Morphology Follows Predictions Of Gliding Performance, Jonathan Rader, Tyson L. Hedrick, Yanyan He, Lindsay D. Waldrop

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The evolution of wing morphology among birds, and its functional consequences, remains an open question, despite much attention. This is in part because the connection between form and function is difficult to test directly. To address this deficit, in prior work we used computational modeling and sensitivity analysis to interrogate the impact of altering wing aspect ratio, camber, and Reynolds number on aerodynamic performance, revealing the performance landscapes that avian evolution has explored. In the present work, we used a dataset of three-dimensionally scanned bird wings coupled with the performance landscapes to test two hypotheses regarding the evolutionary diversification of …

Development Of An Augmented Reality Based Facility Layout Planning And Optimization, Tan Chee Hau

Student Works (2020-2029)

This study presents an augmented reality (AR) based facility layout planning (FLP) system with an optimization algorithm to assist facility layout designers in decision making. AR is a technology that blends virtual data such as 3 Dimensional (3D) model into the actual world. FLP is about the allocation of facilities to achieve smooth, efficient and effective processes. FLP’s research can be divided into the procedural approach, algorithmic approach, Virtual Reality (VR) and AR-based approaches. AR-based FLP effectively addressed the traditional FLP issues such as facilitating the addition of a new machine in an existing production floor, rearrangement of machines’ sequence, …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …

Set Operators, Xiaojin Ye

Dissertations, Theses, and Capstone Projects

My research is centered on set operators. These are universally applicable regardless of the internal structure (numeric or non-numeric) of each individual observed datum. In our research, we have developed the theory of set operators to fill holes and gaps in observed data and eliminate paper shred garbage, thereby changing the observed symbolic data set into one whose pattern is closer to the pattern in the underlying population from which the observed data set was sampled with perturbations.

We describe different set operators including increasing operators, decreasing operators, ex- pansive operators, contractive operators, union preserving operators, intersection preserving op- erators, …

Role Of Influence In Complex Networks, Nur Dean

Dissertations, Theses, and Capstone Projects

Game theory is a wide ranging research area; that has attracted researchers from various fields. Scientists have been using game theory to understand the evolution of cooperation in complex networks. However, there is limited research that considers the structure and connectivity patterns in networks, which create heterogeneity among nodes. For example, due to the complex ways most networks are formed, it is common to have some highly “social” nodes, while others are highly isolated. This heterogeneity is measured through metrics referred to as “centrality” of nodes. Thus, the more “social” nodes tend to also have higher centrality.

In this thesis, …

Ranking Volatility In Building Energy Consumption Using Ensemble Learning And Information Entropy, Kunal Sharma, Jung-Ho Lewe

Georgia Journal of Science

Given the rise in building energy consumption and demand worldwide, energy inefficiency detection has become extremely important. A significant portion of the energy used in commercial buildings is wasted as a result of poor maintenance, degradation or improperly controlled equipment. Most facilities employ sensors to track energy consumption across multiple buildings. Smart fault detection and diagnostic systems use various anomaly detection techniques to discover point anomalies in consumption. While these systems work reasonably well in detecting equipment anomalies over short-term intervals, further exploration is needed in finding methods that consider long-term consumption to detect anomalous buildings. This paper presents a …

A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi

Engineering Faculty Articles and Research

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way …

A Multicenter Mixed-Effects Model For Inference And Prediction Of 72-H Return Visits To The Emergency Department For Adult Patients With Trauma-Related Diagnoses, Ehsan Yaghmaei, Louis Ehwerhemuepha, William Feaster, David Gibbs, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Objective

Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits.

Methods

We analyzed 3.2 million ED encounters with at least one diagnosis under “injury, poisoning and certain other consequences of external causes” and “external causes of morbidity.” These encounters included patients 18 years or older from across 128 emergency room facilities in the …

Functional Morphology Of Gliding Flight I. Modeling Reveals Distinct Performance Landscapes Based On Soaring Strategies, Lindsay D. Waldrop, Yanyan He, Tyson L. Hedrick, Jonathan Rader

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The physics of flight influences the morphology of bird wings through natural selection on flight performance. The connection between wing morphology and performance is unclear due to the complex relationships between various parameters of flight. In order to better understand this connection, we present a holistic analysis of gliding flight that preserves complex relationships between parameters. We use a computational model of gliding flight, along with analysis by uncertainty quantification, to 1) create performance landscapes of gliding based on output metrics (maximum lift-to-drag ratio, minimum gliding angle, minimum sinking speed, lift coefficient at minimum sinking speed); and 2) predict what …

Improving Spellchecking For Children: Correction And Design, Brody Downs

Boise State University Theses and Dissertations

Children commonly use software applications such as search engines and word processors in the classroom environment. However, a major barrier to using these programs successfully is the ability of children to type and spell effectively. While many programs make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users (i.e., adults) and have proven inadequate for children. The aims of this work is twofold: first, to address the types of spelling errors children make by researching, developing, and evaluating algorithms to generate and rank candidate spelling suggestions; and second, to evaluate the impact …

Children In 2077: Designing Children’S Technologies In The Age Of Transhumanism, Oğuz Oz Buruk, Oğuzhan Özcan, Gökçe Elif Baykal, Tilbe Göksun, Selçuk Acar, Güler Akduman, Mehmet Aydın Baytaş, Ceylan Beşevli, Joe Best, Aykut Coşkun, Hüseyin Uğur Genç, Baki Kocaballi, Samuli Laato, Cássia Mota, Konstantinos Papangelis, Marigo Raftopoulos, Richard Ramchurn, Juan Sádaba, Mattia Thibault, Annika Wolff, Mert Yıldız

Presentations and other scholarship

What for and how will we design children’s technologies in the transhumanism age, and what stance will we take as designers? This paper aims to answer this question with 13 fictional abstracts from sixteen authors of different countries, institutions and disciplines. Transhumanist thinking envisions enhancing human body and mind by blending human biology with technological augmentations. Fundamentally, it seeks to improve the human species, yet the impacts of such movement are unknown and the implications on children’s lives and technologies were not explored deeply. In an age, where technologies can clearly be defined as transhumanist, such as under-skin chips or …

Critical Media, Information, And Digital Literacy: Increasing Understanding Of Machine Learning Through An Interdisciplinary Undergraduate Course, Barbara R. Burke, Elena Machkasova

Irish Communication Review

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyze their outcomes and applicability.

Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …