Data Science Commons^™

On Explainability Of Neural Networks, Cem Benar

Dissertations

It is widely reported that deep neural networks outperform most competitors for a range of applications. The state-of-the-art neural networks have built-in inductive bias of architectural choices, regularizations, optimizer types, and initialization methods. Using inductive bias is intuitive to enhance the model approximation. Deep neural networks are mostly dense and heavily overparameterized. They tend to be biased towards low-rank solutions to reduce complexity and improve generalization performance, known as implicit regularization. The implicit regularization as observed in specific architectures and various real-world data sets suggests to overparameterize neural networks judiciously and learn compressed representations (lower rank approximation) with improved performance. …

Crabr-Net: A Contextual Relational Attention-Based Recognition Network For Remote Sensing Scene Objective, Ningbo Guo, Mingyong Jiang, Lijing Gao, Yizhuo Tang, Jinwei Han, Xiangning Chen

Faculty, Staff and Student Publications

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the "summation" way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a …

Syllabus For Computational Physics (Phys 39907), Mark D. Shattuck

Open Educational Resources

Syllabus for City College of New York Computational Physics course.

On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study reviews the newest legislation and policies of Regional Comprehensive Economic Partnership (RCEP) participating countries on cross-border data flow, and then categorized them according to the ban on data transfer, local storage of data, permission-based regulation, and standards-based regulation. By referring to the indexes in the ASEAN Digital Integration Index, the subject and object factors of digital productivity in RCEP parities are sorted out, as well as the status quo of digital economy. Through the introduction of data value chain theory, the decisive impact of digital productivity factors on the policy formulation of cross-border data flow is expounded; by …

Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …

Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …

Verifying Empirical Predictive Modeling Of Societal Vulnerability To Hazardous Events: A Monte Carlo Experimental Approach, Yi Victor Wang, Seung Hee Kim, Menas C. Kafatos

Institute for ECHO Articles and Research

With the emergence of large amounts of historical records on adverse impacts of hazardous events, empirical predictive modeling has been revived as a foundational paradigm for quantifying disaster vulnerability of societal systems. This paradigm models societal vulnerability to hazardous events as a vulnerability curve indicating an expected loss rate of a societal system with respect to a possible spectrum of intensity measure (IM) of an event. Although the empirical predictive models (EPMs) of societal vulnerability are calibrated on historical data, they should not be experimentally tested with data derived from field experiments on any societal system. Alternatively, in this paper, …

Developing Electronic Clinical Quality Measures To Assess The Cancer Diagnostic Process, Daniel R Murphy, Andrew J Zimolzak, Divvy K Upadhyay, Li Wei, Preeti Jolly, Alexis Offner, Dean F Sittig, Saritha Korukonda, Riyaa Murugaesh Rekha, Hardeep Singh

Faculty, Staff and Student Publications

OBJECTIVE: Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance.

MATERIALS AND METHODS: We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of …

Systematic Design And Data-Driven Evaluation Of Social Determinants Of Health Ontology (Sdoho), Yifang Dang, Fang Li, Xinyue Hu, Vipina K Keloth, Meng Zhang, Sunyang Fu, Muhammad F Amith, J Wilfred Fan, Jingcheng Du, Evan Yu, Hongfang Liu, Xiaoqian Jiang, Hua Xu, Cui Tao

Faculty, Staff and Student Publications

Objective: Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way.

Material and methods: Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and …

Federated Generalized Linear Mixed Models For Collaborative Genome-Wide Association Studies, Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (

A Lifecycle Framework Illustrates Eight Stages Necessary For Realizing The Benefits Of Patient-Centered Clinical Decision Support, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Priyanka Desai, Rina Dhopeshwarkar, James Swiger, Edwin A Lomotan, Angela Dobes, Prashila Dullabh

Faculty, Staff and Student Publications

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following stages: Computable Clinical Knowledge, Patient-specific Inference, Information Delivery, Clinical Decision, Patient Behaviors, Health Outcomes, Aggregate Data, and patient-centered outcomes research (PCOR) Evidence. Using this idealized framework reminds key stakeholders that …

Gsprint23/Congressionaltwitternetwork: Data In Brief Article, Gina Sprint

Computer Science Faculty Scholarship

This repository stores the accompanying code and data for the weighted, bidirectional graph (henceforth referred to as a "Twitter Influence Network" graph) presented in the research papers 1. Fink et. al "A centrality measure for quantifying spread on weighted, directed networks" Physica A, 2023 (DOI link: https://doi.org/10.1016/j.physa.2023.129083) and 2. Fink et. al "A Congressional Twitter network dataset quantifying pairwise probability of influence" Data in Brief (https://doi.org/10.1016/j.dib.2023.109521 or https://repository.gonzaga.edu/physicsschol/2). This graph represents the how information flows in a network of US Congress members. Tweets from these members span the date range between February 9, 2022, and June 9, …

A Neural-Network-Based Landscape Search Engine: Lse Wisconsin, Matthew Haffner, Matthew Dewitte, Papia F. Rozario, Gustavo A. Ovando-Montejo

Environment and Society Faculty Publications

The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The …

Static Malware Family Clustering Via Structural And Functional Characteristics, David George, Andre Mauldin, Josh Mitchell, Sufiyan Mohammed, Robert Slater

SMU Data Science Review

Static and dynamic analyses are the two primary approaches to analyzing malicious applications. The primary distinction between the two is that the application is analyzed without execution in static analysis, whereas the dynamic approach executes the malware and records the behavior exhibited during execution. Although each approach has advantages and disadvantages, dynamic analysis has been more widely accepted and utilized by the research community whereas static analysis has not seen the same attention. This study aims to apply advancements in static analysis techniques to demonstrate the identification of fine-grained functionality, and show, through clustering, how malicious applications may be grouped …

Using Geographic Information To Explore Player-Specific Movement And Its Effects On Play Success In The Nfl, Hayley Horn, Eric Laigaie, Alexander Lopez, Shravan Reddy

SMU Data Science Review

American Football is a billion-dollar industry in the United States. The analytical aspect of the sport is an ever-growing domain, with open-source competitions like the NFL Big Data Bowl accelerating this growth. With the amount of player movement during each play, tracking data can prove valuable in many areas of football analytics. While concussion detection, catch recognition, and completion percentage prediction are all existing use cases for this data, player-specific movement attributes, such as speed and agility, may be helpful in predicting play success. This research calculates player-specific speed and agility attributes from tracking data and supplements them with descriptive …

A Hybrid Ensemble Of Learning Models, Bivin Sadler, Dhruba Dey, Duy Nguyen, Tavin Weeda

SMU Data Science Review

Statistical models in time series forecasting have long been challenged to be superseded by the advent of deep learning models. This research proposes a new hybrid ensemble of forecasting models that combines the strengths of several strong candidates from these two model types. The proposed ensemble aims to improve the accuracy of forecasts and reduce computational complexity by leveraging the strengths of each candidate model.

Crop Monitoring And Nutrient Prediction Using Satellite Imagery And Soil Data, Olatunde D. Akanbi, Brian Gonzalez Hernandez, Erika I. Barcelos, Arafath Nihar, Laura S. Bruckman, Yinghui Wu, Jeffrey Yarus, Roger H. French

Student Scholarship

No abstract provided.

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

An Immodest Proposal: Ai, Llms, And The Case For A Standalone Legal Research Requirement, Sarah C. Slinger, Nicholas Mignanelli, Jordan Jefferson, Susan Drisko Zago

Presentations and Speeches

The legal research course is over a century old. As a law school subject, it predates many doctrinal courses, as well as the advent of clinical legal education. It is several decades older than its sister subject, legal writing. In spite of its age and obvious importance, the place of the legal research course in the law school curriculum remains contested. While some law faculties recognize the value of legal research instruction and require a standalone legal research course in the first year, the vast majority combine it with legal writing (often over the objections of legal writing instructors and …

Math And Democracy, Kimberly A. Roth, Erika L. Ward

Journal of Humanistic Mathematics

Math and Democracy is a math class containing topics such as voting theory, weighted voting, apportionment, and gerrymandering. It was first designed by Erika Ward for math master’s students, mostly educators, but then adapted separately by both Erika Ward and Kim Roth for a general audience of undergraduates. The course contains materials that can be explored in mathematics classes from those for non-majors through graduate students. As such, it serves students from all majors and allows for discussion of fairness, racial justice, and politics while exploring mathematics that non-major students might not otherwise encounter. This article serves as a guide …

Responsible Data Science For Genocide Prevention, Victor Piercey

Journal of Humanistic Mathematics

The term "genocide" emerged out of an effort to describe mass atrocities committed in the first half of the 20th century. Despite a convention of the United Nations outlawing genocide as a matter of international law, the problem persists. Some organizations (including the United Nations) are developing indicator frameworks and “early-warning” systems that leverage data science to produce risk assessments of countries where conflict is present. These tools raise questions about responsible data use, specifically regarding the data sources and social biases built into algorithms through their training data. This essay seeks to engage mathematicians in discussing these concerns.

Stochastic Processes And Multi-Resolution Analysis: A Trigonometric Moment Problem Approach And An Analysis Of The Expenditure Trends For Diabetic Patients, Isaac Nwi-Mozu

Computational and Data Sciences (PhD) Dissertations

This dissertation is divided into two distinct parts. The main theme of the first part is to study stochastic processes (and related signal processing questions) using tools in wavelet analysis, functional analysis (we use in particular the trigonometric moment problem), the theory of realization of rational functions, and reproducing kernel Hilbert spaces. A novel form of multiresolution analysis is formulated in the discrete case that is used to study some stochastic processes. Using the trigonometric moment problem, we associate with a vector-valued wide-sense stationary process a multiresolution of a new kind. The notion of realization of rational functions was used …

A Method For Generating A Non-Manual Feature Model For Sign Language Processing, Robert G. Smith Dr, Markus Hofmann Dr

Articles

While recent approaches to sign language processing have shifted to the domain of Machine Learning (ML), the treatment of Non-Manual Features (NMFs) remains an open question. The principal challenge facing this method is the comparatively small sign language corpora available for training machine learning models. This study produces a statistical model which may be used in future ML, rules-based, and hybrid-learning approaches for sign language processing tasks. In doing so, this research explores the emerging patterns of non-manual articulation concerning grammatical classes in Irish Sign Language (ISL). The experimental method applied here is a novel implementation of an association rules …

Causal Inference And Machine Learning Methods In Parkinson's Disease Data Analysis, Albert Pierce

Computational and Data Sciences (PhD) Dissertations

This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.

For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical …

Application Of Machine Learning Algorithms For Elucidation Of Biological Networks From Time Series Gene Expression Data, Krupa Nagori

Computational and Data Sciences (PhD) Dissertations

This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).

Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab …

Understanding The Role Of Interactivity And Explanation In Adaptive Experiences, Lijie Guo

All Dissertations

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating …

Identifying Contributing Factors Associated With Dental Adverse Events Through A Pragmatic Electronic Health Record-Based Root Cause Analysis, Elsbeth Kalenderian, Suhasini Bangar, Alfa Yansane, Duong Tran, Emily Sedlock, Yan Xiao, Janelle Urata, Greg Olson, Amy Franklin, Krishna Kookal, Ana Ibarra-Noriega, Sayali Tungare, Oluwabunmi Tokede, Heiko Spallek, Joel M White, Muhammad F Walji

Faculty, Staff and Student Publications

OBJECTIVE: This study assessed contributing factors associated with dental adverse events (AEs).

METHODS: Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems. Based on information captured in the electronic health record, we analyzed harmful AEs to assess potential contributing factors; harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. We classified potential contributing factors according …

Knowledge Representation And Management 2022: Findings In Ontology Development And Applications, Jean Charlet, Licong Cui, Section Editors For The Imia Yearbook Section On Knowledge Representation And Management

Faculty, Staff and Student Publications

OBJECTIVES: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

METHODS: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.

RESULTS: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, …

A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman

Electronic Theses and Dissertations

This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …

Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner

Electronic Theses and Dissertations

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and …