Data Science Commons^™

Quantified Lives: Data, Bias, And The Cost Of Categorization, Colin F. Geraghty

Dissertations, Theses, and Capstone Projects

This project critically examines how statistical methods and data visualization have historically been used to categorize, rank, and control human populations. By exploring the works of key figures like Francis Galton, Karl Pearson, and R.A. Fisher, it traces the evolution of categorization frameworks from their eugenic roots to modern applications in artificial intelligence and global metrics like the World Happiness Report. Through interactive visualizations and historical analysis, the project invites users to question the legitimacy of these frameworks and reflect on their impact on society. At its core, this work emphasizes the need to move beyond rigid classification systems, challenging …

In Memoriam - Nora Sabelli: Master Orchestrator Of Grant Programs And Mentor For Advancing The Interdisciplinary Learning Sciences Field, Eric Hamilton, Jeremy Roschelle, Roy Pea, Barbara Means, Louis Gomez, Kim Gomez, Nancy Butler Songer

Education Division Scholarship

On Friday, September 6, 2024, the learning sciences field lost a giant in Dr. Nora Sabelli, 87 years old, a personal mentor to many researchers and an inspiration to so many learning scientists and STEM leaders. Nora’s first professional career was as a computational chemist, and later she became a passionate leader in research for improving STEM education. Nora’s time as a senior program officer at the National Science Foundation’s (NSF) Education and Human Resources (EHR) directorate was legendary; she was a force of nature who reshaped funding priorities for stronger science and a stronger connection of science to education …

Empirical Vulnerability Function Development Based On The Damage Caused By The 2014 Chiang Rai Earthquake, Thailand, Patcharavadee Hong, Masashi Matsuoka

Institute for Innovation & Entrepreneurship Publications

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand's building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving …

Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit, Solomon G. Dandekar

Computer Science and Engineering Theses - Archive

The increasing integration of technology into daily life has provided numerous benefits but also significant risks, particularly when exploited by malicious actors in cases of technology facilitated abuse (TFA). Per- petrators can misuse technology to monitor, control, and intimidate their partners, random strangers, etc. exacerbating cycles of abuse. From location tracking and cellphone surveillance to smart device manipula- tion, spyware, and doxing, digital tools have become powerful instruments for coercion and control. This research project investigates the role of technology in stalking and harassment by analyzing discussions on a relevant subreddit where victims share their experiences, strategies for coping, and …

Application Of Physics-Informed Neural Networks On Crop Yield Prediction At Multiple Scales, Aditya P. Prabhu, Pratishtha Poudel, James V. Krogmeier

Discovery Undergraduate Interdisciplinary Research Internship

Accurately predicting crop yields is a critical challenge in sustainable agriculture, food security, and farm management. Traditional process-based models rely on agronomic domain knowledge, crop physiology and statistical approaches, while purely data-driven approaches leverage machine learning or deep learning models using meteorological and spatial data. Unfortunately, these black-box models(Data-drive approaches) often lack interpretability and fail to incorporate well-established physical principles. This project explores a hybrid approach by implementing Physics Informed Neural Networks, mainly, physics-based recurrent neural networks (PI-RNNs) for time-series yield prediction. PINNs allow for the integration of scientific knowledge directly into the model by embedding physical laws as constraints …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

An Overview Of The Special Issue, Rachel Gibson, Trent Buskirk

Data Science Faculty Publications

[Introduction] Across the quantitative social sciences, researchers increasingly face significant challenges and opportunities prompted by the arrival of new sources of very rich, highly granular, and often unstructured digital data. While traditional methods such as surveys and content analysis tools remain indispensable for measuring individual attitudes, behaviors, demographic characteristics, and media messaging online, they often struggle to capture the complex multimodal information streams and metadata generated by social media platforms, mobile devices, sensors, and tracking applications. Collecting and analyzing these diverse new forms of content, dynamic moment-to-moment behaviors, and naturally occurring interactions has become a pressing and exciting research task-one …

Bibliography For Love Data Week 2025, Arianna Tillman, Isabella Piechota, Annikah Carpio

Library Displays and Bibliographies

A bibliography created to support a display about research data and Love Data Week during January/February 2025 at the Leatherby Libraries at Chapman University.

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Biotime 2.0: Expanding And Improving A Database Of Biodiversity Time Series, Maria Dornelas, Laura H. Antão, Amanda E. Bates, Viviana Brambilla, Jonathan M. Chase, Cher F. Y. Chow, Ada Fontrodona-Eslava, Anne E. Magurran, Inês S. Martins, Faye Moyes, Alban Sagouis, Samuel Adu-Acheampong, Daniel Acquah-Lamptey, Dušan Adam, Penelope A. Ajani, Aitor Albaina, Pablo Almaraz, Jeongseop An, Roger Sigismund Anderson, Madelaine Jean Robertson Anderson, Alexsander Z. Antunes, Ivan Arismendi, Linda Armbrecht, Pedro Aros-Mardones, Sreejith Kalpuzha Ashtamoorthy, Narayanan Ayyappan, Gal Badihi, Joseph J. Bailey, Andrew H. Baird, Mark Edward Baird, Sreekumar Vadakkethil Balakrishnan, José António L. Barão-Nóbrega, Adi Barash, Miguel Barbosa, Jos Barlow, Claus Bässler, Matthieu Beaumont, Natalie Beenaerts, Tiago Octavio Begot, Wallace Beiroz, Ricardo Beldade, David M. Bell, Alecia Bellgrove, Jonathan Belmaker, Lisandro Benedetti-Cecchi, Cassandra E. Benkwitt, Pamela Medina-Van Berkum, Brandon T. Bestelmeyer, Matthew C. Betts, Maxwell Kelvin Billah, Anne D. Bjorkman, Magdalena Błażewicz, Christopher P. Bloch, Shane A. Blowes, Antonio Bode, Juliano A. Bogoni, Thomas Bolger, Timothy C. Bonebrake, Erik Bonsdorff, Roberta Bottarin, Luke N. Brokensha, Rob W. Brooker, Andrew J. Brooks, Helge Bruelheide, Thiago Almeida Bueno, Claire Laguionie, Mariana Lopes Campagnoli, James Cant, Erica Pellegrini Caramaschi, Alexandre Caron, Tadhg Carroll, Tancredi Caruso, Juan Carvajal-Quintero, Giuseppe Castaldelli, Edward Castañeda-Moya, Pedro V. Castilho, Sonia Zanini Cechin, Shahar Chaikin, Uchangi Manjunatha Chandrashekara, Tory J. Chase, Chaolun Allen Chen, Jorge José Cherem, Sei-Woong Choi, Erica M. Christensen, Alexander V. Christianini, Jackson Wing Four Chu, Peter Coad, Carl Van Colen, Lise Comte, Elisabeth J. Cooper, J. Hans C. Cornelissen, Eddy Cosson, Unai Cotano, Luc Crevecoeur, Shannan Kyle Crow, Graeme S. Cumming, Vanessa S. Daga, Gabriella Damasceno, Gergana N. Daskalova, Claire H. Davies, Robert A. Davis, Frank P. Day, Sussy De-La-Zerda, Amy Elizabeth Deacon, Indradatta De Castro-Arrazola, Steven Degraer, Kharran Deonarinesingh, Juan C. Diaz-Ricaurte, Christopher R. Dickman, Tara Dirilgen, Ciaran John Dolan, J. Emmett Duffy, Timothy E. Dunn, Giselda Durigan, Ciara Dwyer, Steven Earl, Dor Edelist, Graham John Edgar, Sally Edmonson, Ashley K. Elgin, Kari Elsa Ellingsen, Sarah C. Elmendorf, Ruth S. Eriksen, S. K. Morgan Ernest, Ruben Escribano, Paula Cabral Eterovick, Brian S. Evans, Jason D. Everett, Vesela Evtimova, Dan A. Exton, Andrew J. Fairbairn, Filipe Moreli Fantacini, Fabiano Turini Farah, Fábio Zanella Farneda, Mario E. Favila, Philippe Fernandez-Fournier, Braulio Fernández-Zapata, Diogo F. Ferreira, Carola Ferronato, Christopher R. Du Feu, Alessandra Fidelis, David A. Fifield, Vilmar Picinatto Filho, Walter Mesquita Filho, Robert N. L. Fitt, Carlos A. H. Flechtmann, William R. Fraser, Donna L. Fraser, Lídia Freixas, John Fryxell, Garrett J. Fundakowski, Scott Stanley Gabara, Elise Gallois, Mariana Garcia Criado, Emili García-Berthou, Joaquim Garrabou, Andrew R. Gates, Roberto Cazzola Gatti, Anna Gavioli, Tal Gavriel, Benoit Gendreau-Berthiaume, Xingli Giam, Carina Gjerdrum, Michael Glemnitz, Jasmin Annica Godbold, Daniel Gómez-Gras, Rodrigo Barbosa Gonçalves, Andy Goold, Richard R. Gordon, Menachem Goren, Fernando Vilas Boas Goulart, William G. Gould, Meagan M. Graboski, Nicholas A. J. Graham, Maurício Eduardo Graipel, Laura J. Grange, Aaron C. Greenville, Gary D. Grossman, Valeria A. Guinder, Peter Haase, Gary N. Haskins, Kris Havstad, Luise Hermanutz, Michael Julian Hames Hickford, Pamela Hidalgo, Pedro Higuchi, Andrew S. Hoey, Gert Van Hoey, Annika Hofgaard, Kristen T. Holeck, Robert D. Hollister, Richard T. Holmes, Mia Odell Hoogenboom, Joaquín Hortal, Tammy Horton, Chih-Hao Hsieh, Christine L. Huffard, Ida-Maria Huikkonen, Allen H. Hurlbert, Julian Hynes, Pascal Irz, Natalia Macedo Ivanauskas, Akemi Iwayama, Darren K. James, Ute Jandt, Anna M. Jażdżewska, Merlijn Jocque, Sophie T. Johnston, Samuel E. I. Jones, Faith A. M. Jones, Julia A. Jones, Edite Jucevica, Ugis Kagainis, Maiko Kagami, Jungwon Kang, Xuejia Ke, Erin Colleen Keeley, Rebecca Kinnear, Kari Klanderud, Uwe Klinck, Roel Van Klink, Stefan Klotz, Carolien Kockaert, Halvor Knutsen, Matti Koivula, Alessandra Kortz, Peter Kriegel, Chao-Yang Kuo, David J. Kushner, Rosina Kyerematen, Raphaël Lagarde, Lesley T. Lancaster, Ori Frid Landau, Wouter Van Landuyt, Eric R. Larson, Mai Lazarus, Cheol Min Lee, Jonathan S. Lefcheck, Jonas J. Lembrechts, Renato A. Ferreira De Lima, Romullo Guimarães Lima, Nathália G. S. Lima, Cristina Linares, Sandra C. Lindstrom, Francisco Lloret, John David Lloyd, Cleonice Maria Cardoso Lobato, David M. Lodge, Peter Richard Long, Celeste López-Abbate, Adrià López-Baucells, Julio Louzada, Maite Louzao, Antonella Lugliè, Micheli Ribeiro Luiz, S. Ellen Macdonald, Joshua S. Madin, André Lincoln Barroso Magalhães, Rajindra Mahabir, David Maphisa, Thomas Edward Martin, Marcio Martins, Patrick T. Martone, Silvia Matesanz, Shin-Ichiro S. Matsuzaki, Thomas J. Matthews, Iain Mccombe Matthews, Connie J. Maxwell, Kent P. Mcfarland, Brian J. Mcgill, Diane Marie Mcknight, Michael J. Mcwilliam, Jason Meador, Henning Meesenburg, Kristin Meier, Viesturs Melecis, Peter L. Meserve, Christoph F. J. Meyer, Anders Michelsen, Natali Olivia Roman Miiller, Marco Milardi, Nataliya Milchakova, Robert J. Miller, Jonathan Millett, Tom Moens, Luciano F. A. Montag, Jon Moore, Jörg Müller, Akhil Murali, Shauna Ann Murray, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nakamura, Sasi Nayar, Francis Neat, James A. Nelson, Michael Paul Nelson, Boris P. Nikolov, Rym Nouioua, Collins Ayine Nsor, Michael O' Connor, Edward Adzesiwor Obodai, Amy Marie Offland, Romà Ogaya, Hisako Ogura, Thomas A. Okey, Julian D. Olden, Luiz Gustavo Rodrigues Oliveira-Santos, Jeffrey C. Oliver, Esben Moland Olsen, Vladimir G. Onipchenko, Daniel Oro, Dais Ozolins, Krzysztof Pabis, Bachisio Mario Padedda, Facundo X. Palacio, Alain Paquette, Sinta Trilestari Pardede, David M. Patersib, Sarah Pausina, Raphaël Pélissier, Steven C. Pennings, Josep Penuelas, Felipe Walter Pereira, Nivaldo Peroni, Sergio Picó, Francesca Pilotto, Hudson Tercio Pinheiro, Oscar Pizarro, Roberto Pizzolotto, Francesco Pomati, Paulo Santos Pompeu, Dominique Ponton, Eric Post, Nicolas Poulet, Juha Pöyry, Steven J. Presley, Herbert H. T. Prins, Pieter Provoost, Kathleen L. Prudic, Vignesh Punjayil, Petr Pyšek, Pascal Querner, Juan Pablo Quimbayo, Indar W. Ramnarine, Daniel C. Reed, Peter Bernard Reich, Suzanne M. Remillard, Cerren Richards, Anthony James Richardson, Itai Van Rijin, Victor H. Rivera-Monroy, Christian Rixen, Kevin Peter Robinson, Ricardo Rocha, Ricardo R. Rodrigues, Cassy Rodrigues, Bjørn De Roos, Denise De C. De Rossa-Feres, Loreta Rosselli, Peter Charles Rothlisberg, Ana Rubio, Lars G. Rudstam, Catalina S. Ruz, Nancy B. Rybicki, Gunther Van Ryckegem, Andrew L. Rypel, Jon P. Sadler, Victor Satoru Saito, Sofia Sal, Renato Portela Salomão, Nathan J. Sanders, Flavio A. M. Santos, Tiago Gomes Dos Santos, Swapan Kumar Sarker, Sara E. Scanga, Marcus Schaub, Jochen Schmidt, Inger Kappel Schmidt, Robert L. Schooley, Alfred Schultz, Alberto Scotti, Amanda Serpell-Stevens, Filipe C. Serrano, Elizabeth H. Shadwick, Matthew Shaft, Thomas W. Sherry, Erika Mayumi Shimabukuro, Jacek Siciński, Caya Sievers, Fernando Rodrigues Da Silva, Ana Carolina Da Silva, Juliana M. Silveira, Tadeu Siqueira, Arunkumar Kavidapadinjattathil Sivadasan, Prasad Theruvil Parambil Sivan, Agnija Skuja, Amalia L. Slaughter, Jasper A. Slingsby, Joseph R. Smith, Bruno Eleres Soares, Martin Solan, Flaviana Maluf Souza, Gabriel B. G. Souza, Joshua L. Sprague, Ulrich Stachow, J. John Stadt, Christopher D. Stallings, Radoslav Hristov Stanchev, Emily H. Stanley, Brian M. Starzomski, Jose Mauro Sterza, Maarten Stevens, F. Gary Stiles, Stefan Stoll, Rick D. Stuart-Smith, Yzel Rondon Súarez, Laura Super, Sarah R. Supp, Tapio Sutela, Iain M. Suthers, Anna Suuronen, Kerrie M. Swadling, Daniel K. Szydlowski, Hisatomo Taki, Sara Jeanne Snell Taylor, Pablo A. Tedesco, Nils Teichert, Akira Terui, Gary P. Thiede, Anne Thimonier, Oliver Thomas, Peter Allan Thompson, Simon Thorn, Jeremy S. Tiemann, Luís Felipe Toledo, Anne Tolvanen, Maria Teresa Zugliani Toniato, Ignasi Torre, Marcos Adriano Tortato, Kumiko Totsu, Andrew Trant, Robert R. Twilley, Hirokazu Urabe, Pierre Valade, Nelson Valdivia, Martha Isabel Vallejo, Thomas J. Valone, Jan Vanaverbeke, Tiago Silveira Vasconcelos, Teppo Vehanen, Fábio Venturoli, Hans M. Verheye, Hendrik Jannes Wietse Vermeulen, Arne Verstraeten, Marcelo Vianna, Rui Vieira, João Paulo Santos Vieira-Alencar, Marc Vilella, Jean Ricardo Simões Vitule, Lien Van Vu, Robert B. Waide, Paige S. Warren, Joseph Paul Wayman, Sara L. Webb, Benjamin Weigel, Ellen A. R. Welti, Fritha West, Fulgor Westermann, Matthew A. Whalen, Ethan P. White, Claire E. Widdicombe, Richard Williams, Mark Williamson, Michael R. Willig, Sonja Wipf, Eric J. Woehler, Alje Woldering, Kerry D. Woods, Wu-Bing Xu, Ruthy Yahel, Zeren Yang, Kyle J. A. Zawada, Camila Zornosa-Torres, Assaf Zvuloni

Biological Sciences Faculty Publications

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised …

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Neural Correlates Of Attentional Biases In Dietary Choice: Role Of Childhood Socioeconomic Status, Justine Jamie N. Gotico

CMC Senior Theses

Childhood poverty has been shown to increase adult risk for obesity above and beyond its direct effects on adult socioeconomic status (SES). One proposed mechanism of these effects is by shifting behavioral patterns of dietary consumption and choice, for example by increasing rapid attention to high-calorie unhealthy foods. Yet, whether such neural mechanisms can explain observed differences in dietary behavior based on childhood SES remains an open question. Here we used event-related potentials (ERPs) to examine early attentional correlates of low childhood SES during a dietary choice task, based on research suggesting that early attentional biases toward high-calorie foods emerge …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

How Should China Respond To “Pan-Data Sovereignty” Competition Among China, U.S., And Eu—An Analysis Based On The Digital Stack Model?, Yan Liu, Congjing Ran

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data sovereignty has become deeply intertwined with various economic and social development factors such as technology, trade, economy, culture, society, and politics, leading to a “Pan-Data Sovereignty” competition pattern in the digital space. Through the digital stack model, which examines digital technologies in a layered framework, we can more clearly assess the competitive capacities in“Pan-Data Sovereignty” of China, United States, and European Union. The analysis identifies a three-tiered global “Pan-Data Sovereignty” competition structure among China, U.S., and EU, with each entity holding distinct advantages across various layers of the digital stack. Intense future competition is anticipated in fields such as …

Unlocking The Power Of Data: Enhancing Public Policy Through Advanced Data Infrastructure And Language Model Analysis, Zahid Asghar

CBER Conference

Data is the fundamental building block for advancements in artificial intelligence (AI), general AI (GAI), machine learning (ML), and large language models (LLMs). This study emphasizes the critical need for robust data infrastructure, arguing that without it, countries cannot fully benefit from technological advancements in various economic sectors. Governments possess vast repositories of both structured and unstructured data across multiple domains such as the judiciary, parliaments, and civil bureaucracy. However, these potential goldmines remain untapped due to inadequate data management capabilities and a lack of appreciation for the necessity of high-quality data. The research identifies key issues in public data …

Statistical Analysis For Pre- And Post- Assessments Of Sdq And Idela Scores, Diego Murillo, Franceli L. Cibrian

Student Scholar Symposium Abstracts and Posters

This research aimed to assess the potential of Mazi Umntanakho ("Know Your Child") in tracking developmental milestones in young children. Mazi is a WhatsApp-based conversational agent that assists South African home visitors in evaluating and monitoring children's socio-emotional skills using the Strengths and Difficulties Questionnaire (SDQ) and the International Development and Early Learning Assessment (IDELA). A field study was conducted in low-income South African communities, where 95 home visitors assessed 1,208 children. This detailed analysis of the data was collected during that deployment, focusing on investigating whether assessment scores improved over time and whether the length of time between assessments …

The Impact Of Student Engagement Activities On Future Climate Change Adaptation: The Case Of Student Simulation Models, Bassel Mostafa Elkalaf

Future Journal of Social Science

This paper explores the critical role of student engagement in addressing the growing challenges of climate change, with a focus on the Model United Nations (MUN) as a case study. As climate-related security threats increase globally, educational platforms that prepare youth for effective leadership in climate politics are more essential than ever. MUN, a widely practiced student activity simulating global policy-making, provides a valuable opportunity for students to deepen their understanding of the interconnectedness between climate change, peace, and security. By participating in MUN simulations, students engage in debates, develop innovative solutions, and practice diplomatic skills, all while exploring the …

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Benefits And Challenges Of Constructing Low-Altitude Air Route Network Infrastructure For Developing Low-Altitude Economy, Xiaohan Liao, Chenchen Xu, Huping Ye

Bulletin of Chinese Academy of Sciences (Chinese Version)

Low-altitude airspace is a resource that needs to be fully explored, and the low-altitude economy is a new type of economic activity resulted from low-altitude airspace exploration and utilization. As the primary players of low-altitude flight activities, the commercialization and wide applications of unmanned aerial vehicles (UAVs) are promoting the prosperity of the low-altitude economy. A low-altitude air route network is an effective means to ensure the safe and efficient operations of a large number of UAVs, and it is also a new key infrastructure hosting low-altitude traffic. The history shows that the investment on public transport infrastructure as a …

Prospect And Problem Analysis Of Industry Data Application In Livestock And Poultry Breeding, Yiran Chen, Zhuqing Xiong, Jiaogen Zhou, Quan Wang, Jiancheng Shu, Yinfa Yan, Lanlin Yang, Zemeng Feng, Benhai Xiong, Yulong Yin

Bulletin of Chinese Academy of Sciences (Chinese Version)

Livestock and poultry breeding is a pillar industry in China. The massive data in livestock and poultry breeding is a valuable resource. The market-oriented utilization of livestock and poultry breeding data plays an important role in improving industry standards, increasing industry profits, and driving the development of the entire industry chain. Currently, based on the demand for marketization of livestock and poultry breeding data, the application of new generation information technologies such as artificial intelligence and the Internet of Things in the process of livestock and poultry breeding to collect breeding process data, after de-sensitization and de-classification, through cloud computing, …

Promote Deep Integration Of Real Economy And Digital Economy, Qinmin Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Deep integration of digital technology, industrial development, and data resources empowers development of the digital economy, constantly opens up new tracks, creates new momentum, and establishes new advantages, becoming a new powerful engine for global economic and social development. China attaches great importance to the high-quality development of the digital economy. This study discusses the practical experience of vigorously promoting digital industrialization and industrial digitization, promoting the deep integration of digital technology and the real economy, and accelerating the construction of a network power and digital China. It also proposes countermeasures and suggestions to further promote the efficient empowerment of …

Reflections On Strengthening Digital Public Goods Institutions To Promote Construction Of Open Source Innovation Ecosystem, Chao Zhang, Ze Feng, Kaihua Chen, Qigang Zhu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The digital public goods institutions are important foundations for promoting the development of an open source innovation ecosystem, which helps to break the key limitations faced by China’s open source innovation ecosystem, such as the lack of unity in technology routes and concentration of R&D power. Based on discussing the significance of improving digital public goods institutions, this study reviews the experience of major countries in the world in building digital public goods institutions and analyzes the prominent problems faced by the construction of China’s digital public goods institutions. This study points out that China’s open source innovation ecosystem faces …

Status Quo Of Large-Scale Models, Risks And Challenges, And Recommended Countermeasures, Le Cheng, Yang Xiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Large-scale models (large models) are not only central to technological innovation, but also deeply entwined with national security, economic transformation, and social governance. This study examines the status quo of large-model development, identifies the key risks and challenges, and proposes response strategies, aiming to provide theoretical and policy insights for China’s navigations in global artificial intelligence (AI) competition and advances technological innovation. The research indicates that competition in the large-model market is fierce, while the industry is gradually consolidating. Competition in large models between China and the United States has escalated into a form of geopolitical contest. From a technical …

Construction Of Data Factor Circulation Law System In The United States And Its Reflection For China, Zihan Lin, Youmei Ma, Feng Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pioneer country in the global digital economy, the United States has established a relatively complete system for the circulation of data elements. The focus is on government data openness, with the United States forming a legislative system for data openness at both federal and state levels to ensure data freedom and openness. In the field of personal data trading, the United States has established a relatively relaxed legal environment and data brokers to promote data circulation and trading. At present, China is actively promoting the market-oriented allocation reform of data elements, building a multi-level data element market, and …

Assessing Economic Losses With Covid-19 Integrated Models: A Retrospective Analysis, Timothy Robin Teng, Elvira De Lara-Tuprio, Joselito T. Sescon, Cymon Kayle Lubangco, Rolly Czar Joseph T. Castillo, Mark Anthony C. Tolentino, Maria Regina Justina E. Estuar, Lenard Paulo V. Tamayo, Christian E. Pulmano

Mathematics Faculty Publications

The COVID-19 pandemic led to a global crisis that forced governments to implement restrictive measures to control the spread of the disease. Although these restrictions, such as community quarantines, played a pivotal role in stabilizing healthcare systems, they also caused forced closures of various economic sectors that resulted in huge societal costs and severely impacted the marginalized. In order to understand and quantify the economic impact of the COVID-19 pandemic in the Philippines, an integrated modeling approach, which combined an epidemiological compartmental model and an economic model, was utilized. The evolving nature of COVID-19 required continuous updating of the integrated …

The Wallet And The Gut: Forecasting The 2024 Presidential Election With A State-By-State Adaptation Of The Time-For-Change Model, Simeon A. Betapudi, Hadassah Betapudi

Science University Research Symposium (SURS)

This study adapts Abramowitz's Time-for-Change model to a state-level framework to forecast the 2024 U.S. presidential election. The Time-for-Change model’s focus on the popular vote has become less relevant in recent years, given the growing divergence between popular vote outcomes and electoral college results. Our model addresses these issues by adapting the original Time-for-Change predictors (presidential approval rating, GDP, and time in office) to the state level. Using data from five election cycles (2004–2020), we employ an Ordinary Least Squares (OLS) regression to predict incumbent two-party vote share. Unlike the original model, state-level GDP and incumbency duration were found to …

Key Technology Selection And Countermeasures To Promote Full Lifecycle Data Governance, Yuyao Feng, Hongyun Zhang, Pengfei Wang, Jianping Li, Zongben Xu

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, the digital economy, driven by data as a critical element, has developed rapidly. Nevertheless, China’s progress in data factorization and valorization is still at a preliminary stage. The data governance system remains underdeveloped, with numerous challenges and technical issues arising in the full lifecycle governance of data, including supply, circulation, application, and security protection. Against this backdrop, this study analyzes the primary technical bottlenecks encountered during the modernization of China’s data governance framework. By employing bibliometric analysis, patent data analysis, Delphi surveys, and expert opinions, a critical technology list to support the modernization of data governance in …

Undergraduate Data Literacy In Engineering: A Collaborative Approach, Amber Gruszeczka, Nicole Galloway

Libraries Faculty & Staff Presentations

Data literacy is increasingly crucial to research across disciplines, and librarians are diversifying their skills to encourage students’ self-reliance when interacting with data. Two librarians discuss a collaboration across library departments to design instruction and materials to bring the fundamentals of data literacy to a freshman engineering course.

Tradeoffs Of Generalization, Kyra M. Abrams, Peter T. Darch

I-GUIDE Forum

Models used in geospatial data science are often built and optimized for a specific local context, such as a particular location at a point in time. However, upon publication, these models may be generalized beyond this context, reused in research simulating or predicting other times and places. Without sufficient information or documentation, bias embedded in these models can in turn result in bias in the reuser’s research outputs. Drawing on a long-term qualitative case study of aging dams researchers and developers of models used by these researchers, we find significant documentation gaps. We combine a literature-based genealogy with interviews with …