Computer Sciences Commons^™

Representative Functional Connectivity Learning For Multiple Clinical Groups In Alzheimer's Disease, Lu Zhang, Xiaowei Yu, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical …

The Power Of Supercomputing Applied To Fractal Image Generation, Charlin Me Duff

Mathematics Senior Capstones

This project looks into how parallelism benefits the runtime of generating large fractal images. First, it explains what the Julia and Mandelbrot Sets are, who discovered them, and how they are calculated. Following that is an introduction to supercomputers, parallel computing, and Cal Poly Humboldt’s very own supercomputer. Once groundwork is laid, I explain my process of adapting a fractal image generation program from serial computing to different levels of parallelism. After that, is an analysis of the effects of levels of parallelism on the runtime of large fractal image generation. This paper concludes with a reflection on the project …

Health Care Equity Through Intelligent Edge Computing And Augmented Reality/Virtual Reality: A Systematic Review, Vishal Lakshminarayanan, Aswathy Ravikumar, Harini Sriraman, Sujatha Alla, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this …

Embok 5.0 - Industry 4.0/5.0 Manifest And Latent Dimensions Mapping To The Asem Embok, T. Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

Industry 3.0 automation emerged replacing human labor with high volume processes and robotics. Industry 4.0, cyber-physical systems, and Industry 5.0, mass customization and cognitive systems, are in the early stages of emergence. Research into the impact of Industry 4.0 and 5.0 is focused at the strategic or organizational levels or on the technological challenges. Research into the impact of Industry 4.0 and 5.0 on engineering management has been limited to their impact on project management. This leaves open the question of the directions in which ASEM should evolve the Engineering Management Body of Knowledge (EMBOK) under the emergence of Industry …

Comprehensive Cough Data Analysis On Coda Tb, Jyoti Yadav, Aparna S. Varde, Lei Xie

School of Computing Faculty Scholarship and Creative Works

This work leverages CODA TB, a groundbreaking dataset for a novel comprehensive method of early TB detection from medical big data. Departing from the erstwhile, we find mere cough duration less effective in TB prediction. We discover key demographic and clinical factors (e.g. heart rate, presenting symptoms) to be crucial in distinguishing TB cases, motivating comprehensive cough data analysis with enhanced screening.

Applying Machine Learning To Categorize Distinct Categories Of Network Traffic, Isaac M. Dunham

Senior Honors Theses and Projects

The recent rapid growth of the field of data science has made available to all fields opportunities to leverage machine learning. Computer network traffic classification has traditionally been performed using static, pre-written rules that are easily made ineffective if changes, legitimate or not, are made to the applications or protocols underlying a particular category of network traffic. This paper explores the problem of network traffic classification and analyzes the viability of having the process performed using a multitude of classical machine learning techniques against significant statistical similarities between classes of network traffic as opposed to traditional static traffic identifiers.

To …

Towards Modeling Human Attention From Eye Movements For Neural Source Code Summarization, Aakash Bansal, Bonita Sharif, Collin Mcmillan

School of Computing: Faculty Publications

Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts …

Computer Vision In Adverse Conditions: Small Objects, Low-Resoltuion Images, And Edge Deployment, Raja Sunkara

Masters Theses

"Computer vision based on deep learning is an essential field that plays a significant role in object detection, image classification, semantic segmentation, instance segmentation, and other applications. However, these models face significant challenges in adverse conditions, such as small objects, low-resolution images, and edge deployment. These challenges limit the accuracy and efficiency of computer vision algorithms, making it difficult to obtain reliable results.

The primary objective of this thesis is to assess the performance of deep learning- based computer vision models in challenging conditions and provide viable solutions to overcome the obstacles. The study will specifically address three key challenges, …

Mat: Genetic Algorithms Based Multi-Objective Adversarial Attack On Multi-Task Deep Neural Networks, Nikola Andric

Masters Theses

"Vulnerability to adversarial attacks is a recognized deficiency of not only deep neural networks (DNNs) but also multi-task deep neural networks (MT-DNNs) that attracted much attention in the past few years. To the best of our knowledge, all multi-task deep neural network adversarial attacks currently present in the literature are non-targeted attacks that use gradient descent to optimize a single loss function generated by aggregating all loss functions into one. On the contrary, targeted attacks are sometimes preferred since they give more control over the attack. Hence, this paper proposes a novel targeted multi-objective adversarial ATtack (MAT) based on genetic …

Dynamic Discounted Satisficing Based Driver Decision Prediction In Sequential Taxi Requests, Sree Pooja Akula

Masters Theses

"Ridesharing platforms rely on connecting available taxi drivers to potential passengers to maximize their revenue. However, predicting the stopping decision made by every driver, i.e., the final task performed during a given day, is crucial to achieving this goal. Unfortunately, little research has been done on predicting drivers’ stopping decisions, especially when they deviate from expected utility maximization behavior. This research proposes a Dynamic Discounted Satisficing (DDS) heuristic to model and learn the task at which human agents will stop working for that day, assuming that the human agents are taking sequential decisions based on their preference order. We apply …

Coming Out While Going Fast: Queer Conviviality In Speedrunning Live Streams, Johanna Brewer

Computer Science: Faculty Publications

Drawing on ethnographic research of LGBTQIA+ live streaming speedrunners, this article demonstrates how by centering queer perspectives, we can catalyze meaningful social changes for all. Though for most people, beating the original Super Mario Bros. in under five minutes would seem unfathomably difficult, LGBTQIA+ speedrunning live streamers regularly accomplish this superhuman feat, while coming out to an audience of thousands at the same time. For queer and trans folks, broadcasting such a transgressive, transformational form of play is defiant demonstration of vulnerability; one that creates a comfortable space for a community to thrive, by cultivating a culture of queer conviviality. …

Image Schema Decompositions Of The Conceptual Dependency Ingest Primitive: A Study Of Paraphrases, Jamie C. Macbeth, Alexis Kilayko, Zoie Zhao, Sophie Song, Winniw X. Zheng

Computer Science: Faculty Publications

One of the hallmarks of the Schank-Minsky Conceptual Dependency Trans-Frames meaning representation system is that it attempts to express complex meanings by building large and complex conceptual structures using a relatively small number of primitives. Recently comparisons of image schemas with Conceptual Dependency primitives revealed ways of possibly reducing the number of primitives while maintaining the expressiveness of the set—an important research goal because it increases the flexibility and richness of the primitive-decomposed structures in a way that better approximates human cognition. Inspired by this prior work, we employ a paraphrase generation system to explore the replacement of the Conceptual …

Bringing Stakeholders Along For The Ride: Towards Supporting Intentional Decisions In Software Evolution: Supplemental Material, Alicia M. Grubb, Paola Spoletini

Data

Supplemental material for the research paper entitled, "Bringing Stakeholders Along for the Ride: Towards Supporting Intentional Decisions in Software Evolution". This paper presents an initial literature review to define intentionality, disambiguate it from its use in literature, and position it in relation to similar concepts. This supplement contains the literature review data file.

Multimodal Emotion Analysis With Focused Attention, Siddhi Kiran Bajracharya

Dissertations and Theses

Emotion analysis, a subset of sentiment analysis, involves the study of a wide array of emotional indicators. In contrast to sentiment analysis, which restricts its focus to positive and negative sentiments, emotion analysis extends beyond these limitations to a diverse spectrum of emotional cues. Contemporary trends in emotion analysis lean toward multimodal approaches that leverage audiovisual and text modalities. However, implementing multimodal strategies introduces its own set of challenges, marked by a rise in model complexity and an expansion of parameters, thereby creating a need for a larger volume of data. This thesis responds to this challenge by proposing a …

Exploring Spectral Bias In Time Series Long Sequence Forecasting, Kofi Nketia Ackaah-Gyasi, Sergio Valdez, Yifeng Gao, Li Zhang

Computer Science Faculty Publications

Transformers have achieved great success in the task of time series long sequence forecasting (TLSF) in recent years. However, existing research has pointed out that over-parameterized deep learning models are in favor of low frequency and could be difficult to capture high-frequency information for regression fitting task, named spectral bias. Yet the effect of such bias on TLSF problem, an auto-regressive problem with a long forecasting length, has not been explored. In this work, we take the first step to investigate the spectral bias issues in TLSF task for state-of-the-art models. Specifically, we carefully examine three different existing time series …

Adaptive Resolution Loss: An Efficient And Effective Loss For Time Series Self-Supervised Learning Framework, Kevin Garcia, Juan Manuel Perez, Yifeng Gao

Computer Science Faculty Publications

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, largescale time series data has become ubiquitous. One of the most prominent problems in time series data mining is representation learning. Recently, with the introduction of self-supervised learning frameworks (SSL), numerous amounts of research have focused on designing an effective SSL for time series data. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost …

Neuemot: Mitigating Neutral Label And Reclassifying False Neutrals In The 2022 Fifa World Cup Via Low-Level Emotion, Ademola Adesokan, Sanjay Madria

Computer Science Faculty Research & Creative Works

Sports have been extensively studied for their impact on people's emotional well-being, with research revealing that they have the ability to reduce anxiety and unhappiness while boosting positive emotions1. among all sports, soccer stands out as the most popular and controversial2, eliciting a wide range of emotional reactions from fans, players, officials, and spectators, particularly on social media. While sentiment classifications such as positive, negative, and neutral have been extensively studied, low-level emotions, which refer to more specific and granular emotional states beyond the three basic categories, have yet to be given much attention. This study scraped over 300,000 tweets …

Tweetace: A Fine-Grained Classification Of Disaster Tweets Using Transformer Model, Ademola Adesokan, Sanjay Madria, Long Nguyen

Computer Science Faculty Research & Creative Works

Disaster management teams play a crucial role in responding to catastrophic events with speed and efficiency. However, when faced with large data of disaster-related information, manual systems can struggle to classify the information accurately, especially when they are unavailable. This challenge highlights the need for integrating social media and implementing machine learning models to address the issue. However, the development of such models is dependent on the availability of adequately annotated data, which presents a significant obstacle in the field of crisis management. to address this challenge, our study focuses on the need for disaster event classification through social media. …

Supervised Deep Tree In Alzheimer's Disease, Xiaowei Yu, Lu Zhang, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

As a progressive neurodegenerative disorder, the pathological changes of Alzheimer's disease (AD) might begin as much as two decades before the manifestation of clinical symptoms. Since the nature of the irreversible pathology of AD, early diagnosis provides a more tractable way for disease intervention and treatment. Therefore, numerous approaches have been developed for early diagnostic purposes. Although several important biomarkers have been established, most of the existing methods show limitations in describing the continuum of AD progression. However, understanding this continuous development is essential to understand the intrinsic progression mechanism of AD. In this work, we proposed a supervised deep …

Urban Air Mobility: Vision, Challenges And Opportunities, Debjyoti Sengupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-Time autonomous …

Dispatching Point Selection For A Drone-Based Delivery System Operating In A Mixed Euclidean–Manhattan Grid, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Cristina M. Pinotti, Anil Shende

Computer Science Faculty Research & Creative Works

In this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal …

Reward Maximization For Disaster Zone Monitoring With Heterogeneous Uavs, Wenzheng Xu, Chengxi Wang, Hongbin Xie, Weifa Liang, Haipeng Dai, Zichuan Xu, Ziming Wang, Bing Guo, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study the deployment of $K$ heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of $K$ heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one …

Exploring Transformers As Compact, Data-Efficient Language Models, Clayton Fields, Casey Kennington

Computer Science Faculty Publications and Presentations

Large scale transformer models, trained with massive datasets have become the standard in natural language processing. The huge size of most transformers make research with these models impossible for those with limited computational resources. Additionally, the enormous pretraining data requirements of transformers exclude pretraining them with many smaller datasets that might provide enlightening results. In this study, we show that transformers can be significantly reduced in size, with as few as 5.7 million parameters, and still retain most of their downstream capability. Further we show that transformer models can retain comparable results when trained on human-scale datasets, as few as …

A Formal Framework For Disaster Risk Properties, Shirly Stephen, Mark Schildhauer, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Krzysztof Janowicz, Dean Rehberger

Computer Science and Engineering Faculty Publications

Disaster risk properties (or disaster variables) such as intensity, exposure, severity, vulnerability, resilience, and capacity are significant because they provide essential information for understanding and managing disaster risk and cascading effects. While there are an increasing number of datasets that record these properties based on different criteria, such as regional levels (e.g., community resilience at counties vs. census tracts), thematic levels (e.g., social vulnerability based on race vs. socioeconomic status), or even for different hazard types (e.g., disaster risk for earthquakes vs. hurricanes), we lack a formal model that captures the semantics of these properties, i.e., their interactions with one …

A Pattern For Modeling Computational Observations, Cogan Shimizu, Pascal Hitzler, Charles F. Vardeman

Computer Science and Engineering Faculty Publications

Knowledge graphs (KG) are an established method for heterogeneous data integration and have begun powering complex software agents. However, it is important to understand where the data in the knowledge graph originates, especially within the context of synthetic research agents and other trustworthy AI systems. In this paper, we propose an ontology design pattern for tracking the provenance and context of computational observations, as well as a proposing a supporting, simplified conceptual framework for modeling abstract and concrete versions of the same underlying notion.

Besoins Ontologiques Pour La Transformation Des Aliments, D. Dooley, M. Weber, L. Ibanescu, M. Lange, L. Chan, L. Soldatova, C. Yang, R. Warren, Cogan Shimizu, H. Mcginty, W. Hsiao

Computer Science and Engineering Faculty Publications

People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population. The complexity of such processes is evident in both every-day food preparation of recipes and in industrial food manufacturing, packaging and storage. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains. The challenge is to make this merger be, by analogy, …

The Knowwheregraph Ontology: A Showcase, Cogan Shimizu, Shirly Stephen, Rui Zhu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Pascal Hitzler, Krzysztof Janowicz, Colby K. Fisher, Mohammad Saeid Mahdavinejad, Antrea Christou, Adrita Barua, Abhilekha Dalal, Sanaz Saki Norouzi, Zilong Liu, Meilin Shi, Ling Cai, Gengchen Mai, Zhangyu Wang, Yuanyuan Tian

Computer Science and Engineering Faculty Publications

KnowWhereGraph is one of the largest fully publicly available spatially enabled knowledge graphs. It includes data on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. This paper showcases the KnowWhereGraph ontology, which acts as the schema for the KnowWhereGraph. We discuss how it enables …

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

Browse all Theses and Dissertations

This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra

Browse all Theses and Dissertations

The Internet of Things (IoT) infrastructure encompasses smart devices and real-time sensors connected through the Internet, facilitating the exchange of large datasets among these devices. This interconnected network of IoT sensors generates a significant volume of data for processing and analysis by embedded IoT Edge Computing systems. IoT Edge Computing systems enable efficient real-time analysis and data communications. Furthermore, IoT Edge Computing emerges to enhance the overall efficiency of IoT applications, making them adept at handling the dynamic demands of AI-based and large data-driven applications. The integration of IoT Edge Computing introduces several unique research challenges. Unfortunately, IoT Edge Computing …

Application Of Genomic Compression Techniques For Efficient Storage Of Captured Network Traffic Packets, James Alfred Loving

CCAC Theses and Dissertations

In cybersecurity, one of most important forensic tools are audit files; they contain a record of cyber events that occur on systems throughout the enterprise. Threats to an enterprise have become one of the top concerns of IT professionals world-wide. Although there are various approaches to detect anomalous insider behavior, these approaches are not always able to detect advanced persistent threats or even exfiltration of sensitive data by insiders. The issue is the volume of network data required to identify this anomalous activity. It has been estimated that an average corporate user creates a minimum of 1.5 MB audit data …