Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Medicine and Health Sciences (9078)
- Arts and Humanities (8677)
- Social and Behavioral Sciences (6960)
- Education (5223)
- Medical Specialties (4526)
-
- Medical Sciences (3827)
- Physical Sciences and Mathematics (3079)
- English Language and Literature (2582)
- Law (2379)
- Life Sciences (2245)
- Engineering (2159)
- Creative Writing (1776)
- Psychology (1671)
- Religion (1458)
- Public Health (1357)
- Computer Sciences (1347)
- Sociology (1332)
- Business (1267)
- Higher Education (1241)
- History (1239)
- Nursing (1013)
- Public Affairs, Public Policy and Public Administration (849)
- Biomedical Informatics (766)
- Communication (764)
- Oncology (687)
- Bioinformatics (664)
- History of Christianity (658)
- Curriculum and Instruction (645)
- Diseases (616)
- Feminist, Gender, and Sexuality Studies (615)
- Institution
-
- University of South Florida (34428)
- The Texas Medical Center Library (4387)
- Singapore Management University (1264)
- City University of New York (CUNY) (1172)
- University of Texas at Arlington (1153)
-
- University of Alabama at Birmingham (1125)
- Brigham Young University (1084)
- Missouri University of Science and Technology (942)
- Chulalongkorn University (938)
- Thomas Jefferson University (832)
- University of South Carolina (759)
- Liberty University (717)
- Ateneo de Manila University (710)
- University of Central Florida (681)
- University of Northern Colorado (635)
- Wofford College (628)
- Southwestern Oklahoma State University (525)
- Walden University (518)
- University of Montana (504)
- University of New Mexico (502)
- Washington University in St. Louis (500)
- Portland State University (458)
- University of Nevada, Las Vegas (451)
- University of Plymouth (357)
- University of Nebraska - Lincoln (347)
- Syracuse University (343)
- Sigma Theta Tau International Honor Society of Nursing (339)
- Kennesaw State University (313)
- University of Kentucky (301)
- Yale University (270)
- Keyword
-
- Humans (3120)
- Female (1569)
- Male (1417)
- English (1274)
- UAB (1082)
-
- Literary (971)
- Litmag (969)
- Adult (819)
- Middle Aged (777)
- Animals (746)
- Aged (635)
- Condolence notes (548)
- Mice (473)
- Child (469)
- Education (466)
- United States (436)
- COVID-19 (397)
- Adolescent (395)
- Retrospective Studies (282)
- Young Adult (275)
- Infant (245)
- Mental health (234)
- Preschool (221)
- Treatment Outcome (217)
- Risk Factors (214)
- Machine learning (211)
- Artificial intelligence (209)
- Child, Preschool (198)
- Gender (197)
- Higher education (192)
- Publication
-
- The Auk (22791)
- North American Bird Bander (2833)
- Eastern Bird Banding Association News (2766)
- Faculty, Staff and Students Publications (2032)
- Faculty, Staff and Student Publications (2009)
-
- Journal of Raptor Research (1843)
- Theses and Dissertations (1588)
- Marine Ornithology (1089)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (814)
- PoemMemoirStory (literary magazine) (694)
- Ontario Birds (673)
- Meacham-Carter Papers (616)
- Methodist Local Church Records (607)
- Research Collection School Of Computing and Information Systems (590)
- Mississippi Kite (579)
- Kritika Kultura (542)
- Doctoral Dissertations and Projects (494)
- Faculty Publications (491)
- Walden Dissertations and Doctoral Studies (485)
- Electronic Theses and Dissertations (472)
- Dissertations (425)
- Dissertations, Theses, and Capstone Projects (412)
- Cleveland Bird Calendar (382)
- The Migrant - Tennessee Ornithological Society (372)
- Mythprint (341)
- Maryland Birdlife (336)
- University of Montana Course Syllabi, 2021-2025 (322)
- Faculty Scholarship (286)
- Publications and Research (269)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (244)
- Publication Type
Articles 3301 - 3330 of 78063
Full-Text Articles in Entire DC Network
Navigating The Fine Line: The Role Of Literature As Reflection And Political Commentary, Paul Hong
Navigating The Fine Line: The Role Of Literature As Reflection And Political Commentary, Paul Hong
KAUPA Letters
This article examines the balance between literature and political commentary through the works of Han Kang, particularly The Vegetarian, Human Acts, and The White Book, which explore themes of trauma and societal constraints. It explores the rich tradition of Korean literature and the complex role of the U.S. in shaping Korea's modern history. Additionally, it draws comparisons with authors like Victor Hugo, Émile Zola, and Jean-Paul Sartre, discussing how literature reflects political realities while focusing on universal human struggles.
Application Of Prompt Engineering Technologies Toward Seamless Cybersecurity Breaches Response Process, Young B. Choi
Application Of Prompt Engineering Technologies Toward Seamless Cybersecurity Breaches Response Process, Young B. Choi
KAUPA Letters
In the rapidly evolving landscape of Cybersecurity, the detection and response to breaches is a critical challenge. Prompt engineering, a technique in natural language processing (NLP), has emerged as a transformative technology with the potential to enhance cybersecurity measures. This paper explores the application of prompt engineering in the context of cybersecurity, focusing on its role in the responding process to breaches using GPT-4o capabilities. We will examine how prompt engineering can improve threat intelligence, automate incident response, and augment human decision-making processes.
“A Frail Wish On Human Morality” Versus “A Life-And-Death Question For Humanity”, Yeomin Yoon
“A Frail Wish On Human Morality” Versus “A Life-And-Death Question For Humanity”, Yeomin Yoon
KAUPA Letters
No abstract provided.
Kaupa Letters: Volume 12, Issue 2, Korean American University Professors Association
Kaupa Letters: Volume 12, Issue 2, Korean American University Professors Association
KAUPA Letters
No abstract provided.
A Book Review: The Analects Of Confucius (論語), Young B. Choi
A Book Review: The Analects Of Confucius (論語), Young B. Choi
KAUPA Letters
No abstract provided.
An Unintentional Realist: Rural Poverty In Harold Bell Wright’S Ozarks Novels, John J. Han
An Unintentional Realist: Rural Poverty In Harold Bell Wright’S Ozarks Novels, John J. Han
KAUPA Letters
This paper explores the theme of rural poverty in Harold Bell Wright’s Ozarks novels, a topic often overlooked due to the romantic, Arcadian lens through which Wright portrays the region. Despite his idealized depictions of simple, spiritually fulfilling rural life, Wright’s personal experiences with poverty and his observations of Ozarks life offer valuable insights into the economic deprivation and isolation of the area. The analysis focuses on The Shepherd of the Hills, The Re-Creation of Brian Kent, and Ma Cinderella, examining how Wright portrays rural poverty, the self-sufficiency of Ozark natives, and the effects of poverty on their lives. While …
Reflections On Han Kang’S 2024 Nobel Prize In Literature, John J. Han
Reflections On Han Kang’S 2024 Nobel Prize In Literature, John J. Han
KAUPA Letters
No abstract provided.
Leveraging Ai Tools In University Writing Instruction: Enhancing Student Success While Upholding Academic Integrity, Daisuke Akiba, Rebecca Garte
Leveraging Ai Tools In University Writing Instruction: Enhancing Student Success While Upholding Academic Integrity, Daisuke Akiba, Rebecca Garte
Publications and Research
The emergence of AI-powered Large Language Models (LLMs), such as ChatGPT and Google Gemini, presents both opportunities and challenges for higher education, particularly regarding academic integrity in writing instruction. This exploratory study examines a novel pedagogical approach that integrates LLMs as required feedback tools in a university-level psychology writing assignment. The exclusive online approach emphasizes improvement through revision, requiring students to obtain AI-generated feedback on ungraded initial drafts based on an instructor-provided rubric, with final assessment focused on the quality of subsequent revisions. Analysis of survey data from 39 undergraduate students, incorporating both quantitative measures and qualitative responses, revealed several …
At Least, We Should Try, Semoon Chang
Strategizing Holidays: The Art Of Meaningful Togetherness, Paul Hong
Strategizing Holidays: The Art Of Meaningful Togetherness, Paul Hong
KAUPA Letters
No abstract provided.
Geochemical Evidence Of Drying During The 4.2 Ka Event In Sediment Cores From The Yucatán Peninsula, Mexico, Derek K. Gibson, Jonathan Obrist-Farner, Alex Correa-Metrio, Alejandra Rodriguez-Abaunza, Carlos Castañeda-Posadas
Geochemical Evidence Of Drying During The 4.2 Ka Event In Sediment Cores From The Yucatán Peninsula, Mexico, Derek K. Gibson, Jonathan Obrist-Farner, Alex Correa-Metrio, Alejandra Rodriguez-Abaunza, Carlos Castañeda-Posadas
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Tropical hydroclimate variability during the Middle and Late Holocene was investigated using geochemical indicators of local-scale precipitation and evaporation preserved in sediment cores from two sites in the Mexican Yucatán Peninsula. Scanning X-Ray fluorescence spectroscopy data show generally decreasing precipitation trends during the Early and Middle Holocene. During the transition between the Middle and Late Holocene, geochemical evidence of reduced watershed erosion and increased evaporation indicate that a centennial-scale drying event impacted the region between 4.3 and 4.0 ka (kilo-anum; thousand years before present). These findings suggest that the 4.2 ka drying event, which has been previously recorded in Europe, …
Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan
Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan
Computer Science Faculty Research & Creative Works
Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users’ emotional states. Traditional methods often rely on cameras or wearable sensors, which raise privacy concerns and add extra device burdens. In addition, existing acoustic-based methods struggle to maintain satisfactory performance when there is a distribution shift between the training dataset and the inference dataset. In this paper, we introduce FacER+, an active acoustic facial expression recognition system, which eliminates the requirement for external microphone arrays. FacER+ extracts facial expression features by analyzing the echoes of near-ultrasound signals emitted between the 3D facial …
From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang
From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang
Research Collection School Of Computing and Information Systems
This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …
Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan
Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan
Research Collection School Of Computing and Information Systems
In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …
Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen
Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen
Research Collection School Of Computing and Information Systems
With the popularity of on-demand ride services worldwide, ride-sourcing platforms must maintain an adequate fleet size and cope with growing travel demand. Recently, platforms have attempted to provide vehicle rental services to drivers who do not own cars, then recruited them to provide on demand ride services. This helps lower the entry barrier for drivers and offers another profitable business for platforms. From the government's perspective, however, it is challenging to coordinately regulate a ride-sourcing business and vehicle rental business. This paper proposes a bi-level optimization model to investigate how the government regulates the ride-sourcing market integrated with vehicle rental …
Towards Privacy-Aware Iot Communications: Delegable, Revocable, And Efficient, Pengfei Wu, Jianfei Sun, Guomin Yang, Robert H. Deng
Towards Privacy-Aware Iot Communications: Delegable, Revocable, And Efficient, Pengfei Wu, Jianfei Sun, Guomin Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
The Internet of Things (IoT) is widely recognized for its potential to enhance efficiency and productivity across various industries. However, its increasing prevalence has also made it a more attractive target for cybercriminals. While many advanced cryptographic solutions have been developed to secure IoT, some practical security and privacy issues such as self-sovereign delegation, flexible revocation, and lightweight access remain inadequately addressed in existing solutions. In this paper, we propose PLIC, a Privacy-aware Lightweight IoT Communication scheme, which not only enables any authorized user to flexibly delegate their lightweight access privileges to other delegatees, such that they can also access …
Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu
Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu
Research Collection School Of Computing and Information Systems
Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …
Reflections On Linear B, (Part 6): Possible Origin Of The Shape And Pronunciation /Ko/ Of Sign 70, Gerald Leonard Cohen
Reflections On Linear B, (Part 6): Possible Origin Of The Shape And Pronunciation /Ko/ Of Sign 70, Gerald Leonard Cohen
Arts, Languages and Philosophy Faculty Research & Creative Works
This article suggests that the shape of Linear B sign 70 may derive from the Egyptian hieroglyph uatch ‘young plant, green’ and that its pronunciation /ko/ is explainable by the Greek word for green (χλωρός).
Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O'Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner
Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O'Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner
Chemistry Faculty Research & Creative Works
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, …
Collaboration! Towards Robust Neural Methods For Routing Problems, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
Collaboration! Towards Robust Neural Methods For Routing Problems, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
Research Collection School Of Computing and Information Systems
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues – their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based Collaborative Neural Framework (CNF) w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing …
Flexfl: Heterogeneous Federated Learning Via Apoz-Guided Flexible Pruning In Uncertain Scenarios, Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen
Flexfl: Heterogeneous Federated Learning Via Apoz-Guided Flexible Pruning In Uncertain Scenarios, Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen
Research Collection School Of Computing and Information Systems
Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. …
Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen
Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen
Research Collection School Of Computing and Information Systems
This paper concerns imitation learning (IL) in cooperative multi-agent systems.The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning.In this work, we introduce a new multi-agent IL algorithm designed to address these challenges. Our approach enables thecentralized learning by leveraging …
A Community-Driven Framework To Evaluate Factors Influencing Nft Buyers, Yi Meng Lau, Ping Fan Ke
A Community-Driven Framework To Evaluate Factors Influencing Nft Buyers, Yi Meng Lau, Ping Fan Ke
Research Collection School Of Computing and Information Systems
This study examines the pivotal role of community-driven social dynamics in shaping non-fungible tokens (NFT) buyers’ purchasing intentions and the increasing social significance of digital assets. Building on prior research in online word-of-mouth, we propose a comprehensive framework that integrates technological, personal, social, economic, and political factors influencing NFT purchases. The framework emphasizes the importance of service providers, personalization, perceived value, and associated risks, while also providing insights into how online word-of-mouth and community dynamics impact these aspects. As a preliminary exploration, this research lays the foundation for future empirical studies to validate the framework and assess its applicability across …
Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun
Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun
Research Collection School Of Computing and Information Systems
Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …
Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua
Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …
Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu
Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu
Research Collection School Of Computing and Information Systems
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID …
Generative Artificial Intelligence In Business Higher Education: A Focus Group Study, Xuenan Huo, Keng Siau
Generative Artificial Intelligence In Business Higher Education: A Focus Group Study, Xuenan Huo, Keng Siau
Research Collection School Of Computing and Information Systems
This research investigates the opportunities and challenges of integrating generative artificial intelligence (GenAI) into business higher education, drawing insights from an asynchronous focus group research study with doctoral students who serve dual roles as both learners and educators. Key opportunities identified through thematic analysis include knowledge acquisition, intelligent co-ideation, supportive augmentation, and personalized learning. Challenges identified include AI trustworthiness, cognitive dependency, human value, policy and instruction, assessment integrity, and identity management. This study clarifies GenAI’s specific role in business education and provides practical insights for effectively integrating GenAI to enhance learning outcomes and address emerging challenges. An analysis theory on …
Safety Through Feedback In Constrained Rl, Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
Safety Through Feedback In Constrained Rl, Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
Research Collection School Of Computing and Information Systems
In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be prohibitively expensive. For instance, in the domain of self-driving, designing a cost function that encompasses all unsafe behaviours (e.g., aggressive lane changes, risky overtakes) is inherently complex, it must also consider all the actors present in the scene making it expensive to evaluate. In such scenarios, the cost function can be learned from feedback collected offline in between training rounds. This …
Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang
Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …