32584 research outputs found
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Kumaravadivelu, B.
Get PDFUniversity of Michigan-Ann Arbor, Applied Linguistics, 1986 Ph.D.
University of Lancaster, Linguistics for English Language Teaching. 1981 M.A.
University of Madras, India, English Language and Literature, 1972 M.A.
University of Madras, India, English Literature, 1970 B.A.https://scholarworks.sjsu.edu/erfa_bios/1340/thumbnail.jp
Whaley, George L.
Get PDFUniversity of Colorado, Boulder, Organizational Behavior, Finance, and Management Science, 1974 Ph.D.
University of Arizona, 1966 M.B.A.
University of Arizona, Engineering Mathematics, 1964 B.S.https://scholarworks.sjsu.edu/erfa_bios/1343/thumbnail.jp
Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics
Get PDFBackground: Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. Methodology: More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the-art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Results: Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools have the potential to improve identification performance relative to using the tools individually. Conclusion: We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content)
Horstmann, Cay
No full textUniversity of Michigan, Ann Arbor, Mathematics, Ph.D.1987 Christian Albrechts Universität Kiel, Germany, Mathematics and Computer Science, Diplom 1981 Syracuse University, Computer Science, M.S.1980https://scholarworks.sjsu.edu/erfa_bios/1354/thumbnail.jp
iSchool Student Research Journal, Vol.14, Iss.1
Get PDFVolume 14, Issue 1 of the School of Information Student Research Journal explores the multifaceted impacts of artificial intelligence (AI) on academia, particularly within library and information science (LIS) education and scholarly publishing. Highlighting the proactive measures taken by San Jose State University\u27s iSchool, this issue underscores the necessity of integrating AI competencies, such as data privacy and ethical AI use, into curricula. Through detailed case studies and policy reviews, the issue examines the ethical and societal implications of AI, including biases and inequalities, advocating for adaptive and responsible AI integration.
Odin Halvorson\u27s paper emphasizes the transformative potential of Large Language Models (LLMs) and how revised curriculum and updated policies can address AI\u27s impact, and ensure equitable access and ethical use of AI technologies.
Marc Hoffeditz\u27s editorial delves into the Student Research Journal\u27s development of its AI disclosure policy, offering a comprehensive literature review and practical guidelines for other scholarly journals to navigate the challenges posed by AI.
Souvick \u27Vic\u27 Ghosh and Denise McCoy\u27s article evaluates the evolution of SJSU iSchool\u27s Master of Library and Information Science (MLIS) program, proposing updates to core competencies to incorporate advancements in AI, ensuring graduates are prepared for an AI-integrated future.
Madelaine Russell\u27s book review of Christine Pawley\u27s Organizing Women: Home, Work, and the Institutional Infrastructure of Print in Twentieth-Century America provides a historical perspective on women\u27s roles in print culture, highlighting the intersections of gender, race, and information access.
This issue provides valuable insights into the ongoing integration of AI in LIS education and scholarly publishing, emphasizing the importance of ethical considerations, policy development, and curricular advancements to foster a future-ready academic landscape
Transit Workforce Development Challenges and Mitigation Practices
Get PDFThe objective of this research is to summarize previously conducted research related to workforce development challenges in the transit industry, detailing major findings and subsequent recommendations based on the annotated bibliography of the current atmosphere and most successful ways to mitigate those challenges to attract and retain talent in the transit industry. According to the U.S. Chamber of Commerce, the U.S. had over 10 million job openings and only 5.7 million unemployed workers in March 2023. In 2017, the Washington Post posited that there were not enough workers to fill the vacancies, as the U.S. Census Bureau announced a 17-year low in unemployment while the Bureau of Labor Statistics hit record streaks for numbers of job openings. The COVID-19 pandemic exacerbated the employment challenges that the U.S. already faced. To top it all, the pandemic also led to increased retirement rates among those close to retirement age. This provides transit industry executives and transportation professionals, with ideas of tools that are available to help attract more qualified candidates to the transit industry and ways to advance the development of the transit workforce. This research highlights some of the social media recruiting; online hiring platform improvements; and partnerships with career centers, universities, and recruitment centers to attract new employees to the transit workforce. In addition, increases in pay, benefits, bonuses, providing flexible schedule options and some other atypical ideas have successfully been used to retain workers in the transit workforce. This research focuses on ways in which all transit stakeholders can invest in all aspects of industry workforce development to ensure qualified employees choose the transit industry and that they are subsequently trained to be the most beneficial assets to the organization and remain there via effective retention strategies
Kim, Chungsoon C.
No full textSeoul National University, Home Economics, College of Education, B.S. 1960
University of Georgia, Child Development Department, M.S. 1961
Florida State University, Child Development Department, Ph. D, 1964https://scholarworks.sjsu.edu/erfa_bios/1389/thumbnail.jp
Regional Sea Level Rise Prediction in Monterey Bay with LSTMs and Vertical Land Motion
Get PDFEarth system data is vast in volume and variety, and is used to forecast weather,
hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of
clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of recent SLR research. Long Short-Term Memory (LSTM) historically performs well for SLR predictions but has underperformed when forecasting regional SLR using altimetry data such as Mean
Temperature Anomaly (MTA) and Oceanic Heat Content (OHC) time-scaled to quar- ters. This project proposes the inclusion of Vertical Land Motion (VLM) data, which
are often disregarded by existing literature due to the lack of cohesive datasets, in the dataset along with oceanic and atmospheric variables. Our experiments focusing on Monterey Bay, California demonstrates that VLM data can improve the performance of LSTMs for regional SLR prediction. We also identify key LSTM features by feature importance computation. Furthermore, we assess the viability of using VLM in the presence of missing data points and its effects on the prediction
ML-Based User Identification Through Mouse Dynamics
Get PDFUser authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from time-series data extracted from user sessions. These embeddings are then utilized for multiclass classification, treating user authentication as a multiclass problem rather than a binary classification task commonly seen in prior research.
The research employs the Balabit Mouse Dynamics Challenge Dataset to investi- gate the efficacy of different deep learning architectures in creating embeddings from temporal user session data and their subsequent performance in accurately classifying users based on mouse dynamics. The study highlights the enhancements made by the proposed framework by delivering a comparative analysis with the existing research
Examining the Historical Evolution and Contemporary Significance of Human Rights
Get PDFThis paper delves into the complex concept of human rights, examining its historical evolution and contemporary significance through the perspectives of Lynn Hunt\u27s Inventing Human Rights, Michelline R. Ishay\u27s The History of Human Rights, and Samuel Moyn\u27s The Last Utopia. Hunt\u27s work explores the 18th-century Enlightenment, highlighting the political foundations of natural, equal, and universal rights. Ishay provides a comprehensive account spanning ancient civilizations to modern globalization, emphasizing the dynamic nature of human rights struggles. Moyn challenges conventional views, asserting the mid-20th century emergence of contemporary human rights amidst the Cold War and failed utopian visions. While each historian offers unique insights, common themes emerge, including analyses of historical breakthroughs and pivotal social movements. The exploration encompasses key milestones, from the Enlightenment to the Universal Declaration of Human Rights, and contemporary challenges. Examining the relationship between human rights and political ideologies like nationalism, socialism/communism, and colonialism/anti-colonialism, this paper underscores the dynamic nature of human rights amid societal shifts. Additionally, it delves into ongoing affairs, exploring the present state of human rights activism intersecting with globalization. By synthesizing these perspectives, this paper aims to offer a comprehensive narrative on the intricate dimensions of human rights, contributing to a deeper understanding of its historical roots and contemporary relevance