Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (3)
- Artificial intelligence (2)
- Analysis (1)
- Autonomous aerial vehicles (1)
- Behavioral research (1)
-
- Blockchain (1)
- Blockchains (1)
- Citation contexts (1)
- Closed box (1)
- Computational linguistics (1)
- Computer science - artificial intelligence (1)
- Computer science - digital libraries (1)
- Computer science - learning (1)
- Cost effectiveness (1)
- Data mining (1)
- Discrete wavelet transform (1)
- Electroencephalography (1)
- Experimental (1)
- Feature extraction (1)
- Health aspects (1)
- Homeland security (1)
- Large language models (1)
- Learning systems (1)
- Location awareness (1)
- Machine learning technology (1)
- Medical research (1)
- Medicine (1)
- Mental illness (1)
- Missions (1)
- National security (1)
Articles 1 - 5 of 5
Full-Text Articles in Social and Behavioral Sciences
Short: Can Citations Tell Us About A Paper's Reproducibility? A Case Study Of Machine Learning Papers, Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu
Short: Can Citations Tell Us About A Paper's Reproducibility? A Case Study Of Machine Learning Papers, Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu
Computer Science Faculty Publications
The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from papers involved in Machine Learning Reproducibility Challenges in order to interpret the positive or negative outcomes of reproduction attempts. Our contributions include training classifiers for reproducibility-related contexts and sentiment analysis, and exploring correlations between …
Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong
Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong
Computer Science Faculty Publications
Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …
Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle
Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle
Computer Science Faculty Publications
The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …
Can Large Language Models Discern Evidence For Scientific Hypotheses? Case Studies In The Social Sciences, Sai Koneru, Jian Wu, Sarah Rajtmajer
Can Large Language Models Discern Evidence For Scientific Hypotheses? Case Studies In The Social Sciences, Sai Koneru, Jian Wu, Sarah Rajtmajer
Computer Science Faculty Publications
Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies …
Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu
Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu
Computer Science Faculty Publications
Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …