Physical Sciences and Mathematics Commons^™

Evaluating Technology-Mediated Collaborative Workflows For Telehealth, Christopher Bondy Ph.D., Pengcheng Shi, Pamela Grover Md, Vicki Hanson, Linlin Chen, Rui Li

Articles

Goals: This paper discusses the need for a predictable method to evaluate gains and gaps of collaborative technology-mediated workflows and introduces an evaluation framework to address this need. Methods: The Collaborative Space Analysis Framework (CS-AF), introduced in this research, is a cross-disciplinary evaluation method designed to evaluate technology-mediated collaborative workflows. The 5-step CS-AF approach includes: (1) current-state workflow definition, (2) current-state (baseline) workflow assessment, (3) technology-mediated workflow development and deployment, (4) technology-mediated workflow assessment, (5) analysis, and conclusions. For this research, a comprehensive, empirical study of hypertension exam workflow for telehealth was conducted using the CS-AF approach. Results: The CS-AF …

A Framework Of Web-Based Dark Patterns That Can Be Detected Manually Or Automatically, Ioannis Stavrakakis, Andrea Curley, Dympna O'Sullivan, Damian Gordon, Brendan Tierney

Articles

This research explores the design and development of a framework for the detection of Dark Patterns, which are a series of user interface tricks that manipulate users into actions that they do not intend to do, for example, share more data than they want to, or spend more money than they plan to. The interface does this using either deception or other psychological nudges. User Interface experts have categorized a number of these tricks that are commonly used and have called them Dark Patterns. They are typically varied in their form and what they do, and the goal of this …

On The Documentation Of Refactoring Types, Eman Abdullah Alomar, Jiaqian Liu, Kenneth Addo, Mohamed Wiem Mkaouer, Christian D. Newman, Ali Ouni, Zhe Yu

Articles

Commit messages are the atomic level of software documentation. They provide a natural language description of the code change and its purpose. Messages are critical for software maintenance and program comprehension. Unlike documenting feature updates and bug fixes, little is known about how developers document their refactoring activities. Specifically, developers can perform multiple refactoring operations, including moving methods, extracting classes, renaming attributes, for various reasons, such as improving software quality, managing technical debt, and removing defects. Yet, there is no systematic study that analyzes the extent to which the documentation of refactoring accurately describes the refactoring operations performed at the …

Explaining Deep Learning Models For Tabular Data Using Layer-Wise Relevance Propagation, Ihsan Ullah, Andre Rios, Vaibhov Gala, Susan Mckeever

Articles

Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use …

Workflow Critical Path: A Data-Oriented Critical Path Metric For Holistic Hpc Workflows, Daniel D. Nguyen, Karen L. Karavanic

Computer Science Faculty Publications and Presentations

Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. …

A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista

All Works

Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature …

Teaching Computer Science Csc 222, Harrison Dekker

Library Impact Statements

No abstract provided.

Computational Thinking For Teachers, Susan Imberman

Open Educational Resources

This is a syllabus for a course in computational thinking. The course described introduces preservice and inservice teachers to the fundamental concepts of computer science, including web design, coding, ethics, computational thinking, course resources, etc.

Extending The Quality Of Secure Service Model To Multi-Hop Networks, Paul M. Simon, Scott R. Graham

Faculty Publications

Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels …

Comparison Of Major Cloud Providers, Justin Berman

Other Student Works

This paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing.

Tooth Position Determination By Automatic Cutting And Marking Of Dental Panoramic X-Ray Film In Medical Image Processing, Yen-Cheng Huang, Chiung-An Chen, Tsung-Yi Chen, He-Sheng Chou, Wei-Chi Lin, Tzu-Chien Li, Jia-Jun Yuan, Szu-Yin Lin, Chun-Wei Li, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo

Department of Information Systems & Computer Science Faculty Publications

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, …

Introduction To Using Python In The Digital Humanities, Elisabeth Shook

Library Faculty Publications and Presentations

The materials here are from the Python for Digital Humanities Workshop taught on December 13, 2021 for the Boise State University Digital Humanities Group. This 3-hour workshop was created to provide both a very brief introduction to the various capabilities of Python and a small lesson in using Python to pull meaningful insight out of text files.

Secure And Privacy-Preserving Crowdsensing Using Smart Contracts: Issues And Solutions, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

The advent of Blockchain and smart contracts is empowering many technologies and systems to automate commerce and facilitate the exchange, tracking and the provision of goods, data and services in a reliable and auditable way. Crowdsensing systems is one type of systems that have been receiving a lot of attention in the past few years. In crowdsensing systems consumer devices such as mobile phones and Internet of Things devices are used to deploy wide-scale sensor networks. We identify some of the major security and privacy issues associated with the development of crowdsensing systems based on smart contracts and Blockchain. We …

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz

Computer Science Technical Reports

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or …

Spatio-Temporal Relation Modeling For Few-Shot Action Recognition, Anirudh Thatipelli, Sanath Narayan, Salman Hameed Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Bernard Ghanem

Computer Vision Faculty Publications

We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global framelevel enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a …

Machine Learning And Radiomic Features To Predict Overall Survival Time For Glioblastoma Patients, Lina Chato, Shahram Latifi

Electrical & Computer Engineering Faculty Research

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A …

Proquest Tdm Studio: A Text And Data Mining Solution, Anamika Megwalu, Anne Marie Engelsen

Faculty Research, Scholarly, and Creative Activity

TDM Studio is an integrated platform offered by ProQuest for data and text mining. TDM stands for text and data mining. This cloud-based, all-in-one innovative product is designed to offer researchers a clean interface with rights-cleared content, Jupyter notebook, and data visualization tools. As a result, researchers can now search Pro-Quest databases, create large datasets, import data to Jupyter notebook for analysis, and download results within a day.

Computer Program Simulation Of A Quantum Turing Machine With Circuit Model, Shixin Wu

Mathematical Sciences Technical Reports (MSTR)

Molina and Watrous present a variation of the method to simulate a quantum Turing machine employed in Yao’s 1995 publication “Quantum Circuit Complexity”. We use a computer program to implement their method with linear algebra and an additional unitary operator defined to complete the details. Their method is verified to be correct on a quantum Turing machine.

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, …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Mechanics Of Ascension Through Genshin Impact, Megan Chao

ART 108: Introduction to Games Studies

Genshin Impact, or Genshin for short, is a free roleplaying, open-world, adventure video game released for Playstation 4, Microsoft Windows, iOS, and Android on September 28th, 2020. Since its initial release, the game has gained considerable recognition for its storytelling ability, in-depth lore, and amazing character and world-building design. They were awarded Apple’s “Game of the Year” in 2020 and have grossed over $100 million from in-app iOS purchases alone.

Difficulty In Video Games, Jason Bechdolt

ART 108: Introduction to Games Studies

Throughout time games have been utilized for a great variety of purposes: relaxing leisure, competitive activity, intellectual exercise, and many other sources of enjoyment. The element of difficulty has existed in various degrees throughout all of those game styles, usually found through the skill of an opponent, but lately many games have been designed to have a digital opponent to provide that difficulty. With access to more information than ever before, people have gotten the chance to witness lives and worlds that they can never physically enjoy, and this has led to a considerable market for games that can provide …

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

Department of Computer Science and Engineering: 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 …

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Ow-Detr: Open-World Detection Transformer, Akshita Gupta, Sanath Narayan, K.J. Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah

Computer Vision Faculty Publications

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification …

Semantically Meaningful Sentence Embeddings, Rojina Deuja

Department of Computer Science and Engineering: 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 …

Analyze And Examine Wildfire Events In California, Aleena Hoodith, Sakim Zaman, Safoan Hossain, Jiehao Huang

Publications and Research

•A wildfire is an unplanned, unwanted, uncontrolled fire in an area of combustible vegetation starting in rural areas and urban areas. •Recent studies have shown that the effect of anthropogenic climate change has fueled the wildfire events, leading to an increase in the annual burned areas and number of events. •California is one of the places having the most deadliest and destructive wildfire seasons. With the global warming effect of 1°C since 1850, the 20 largest wildfires events that have occurred in California, 8 of them were in 2017. (Center For Climate And Energy Solutions) •Climate change is primarily caused …