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Articles 2581 - 2610 of 11193
Full-Text Articles in Artificial Intelligence and Robotics
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Research Collection School Of Computing and Information Systems
Target-driven recommendation dialogues present unique challenges in dialogue management due to the necessity of anticipating user interactions for successful conversations. Current methods face significant limitations: (I) inadequate capabilities for conversation anticipation, (II) computational inefficiencies due to costly simulations, and (III) neglect of valuable past dialogue experiences. To address these limitations, we propose a new framework, Experiential Policy Learning (EPL), for enhancing such dialogues. EPL embodies the principle of Learning From Experience, facilitating anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. To demonstrate its flexibility, we introduce Tree-structured EPL (T-EPL) …
A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao
A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao
Research Collection School Of Computing and Information Systems
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …
Consecutive Batch Model Editing With Hook Layers, Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam
Consecutive Batch Model Editing With Hook Layers, Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam
Research Collection School Of Computing and Information Systems
As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive and batch scenarios to edit the model behavior directly. Despite all these practical expectations, existing model editing methods fail to realize all of them. Furthermore, the memory demands for such sequential model editing approaches tend to be prohibitive, frequently necessitating an external memory that grows incrementally over time. To cope with these challenges, we propose CoachHooK, a model editing method that simultaneously supports sequential and batch editing. CoachHooK is memory-friendly as it only needs a …
Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang
Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang
Research Collection School Of Computing and Information Systems
Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates …
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Research Collection School Of Computing and Information Systems
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …
Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng
Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng
Research Collection School Of Computing and Information Systems
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …
Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang
Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang
Research Collection School Of Computing and Information Systems
In the realm of dialogue-to-image retrieval, the primary challenge is to fetch images from a pre-compiled database that accurately reflect the intent embedded within the dialogue history. Existing methods often overemphasize inter-modal alignment, neglecting the nuanced nature of conversational context. Dialogue histories are frequently cluttered with redundant information and often lack direct image descriptions, leading to a substantial disconnect between conversational content and visual representation. This study introduces VCU, a novel framework designed to enhance the comprehension of dialogue history and improve cross-modal matching for image retrieval. VCU leverages large language models (LLMs) to perform a two-step extraction process. It …
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …
Ai And Data Science For Public Policy, Kenneth Benoit
Ai And Data Science For Public Policy, Kenneth Benoit
Research Collection School of Social Sciences
Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need …
The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster
The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster
Faculty and Staff Publications & Presentations
Dr. Lesh's second presentation, "The Digital Renaissance in Education: Adapting Generative AI in Pre-Service Teacher and Provider Strategies," offered insights into the transformative role of generative AI in teacher education. Collaborating with Dr. JeVaughn Lancaster virtually, Lesh and Lancaster shared data from a recent study examining teachers' perceptions of AI in academic research. Findings underscored the potential for AI to enhance educational efficiency while also identifying ethical considerations that must be addressed. Lesh and Lancaster advocated for responsible AI training, stressing that generative AI should augment, not replace, educators' expertise and critical thinking.
Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai
Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai
Research Collection Lee Kong Chian School Of Business
As of June 2024, the U.S. Food and Drug Administration (FDA) has approved 950 medical artificial intelligence (AI) devices. The current regulatory framework freezes AI algorithms after approval, requiring new submissions for updates to ensure compliance with Good Machine Learning Practices (GMLP). This approach imposes a significant administrative burden, while hindering the ability of AI algorithms to learn from new data. To address these challenges, the FDA has explored a novel pathway known as Predetermined Change Control Plans (PCCP), allowing developers to outline future changes during initial submissions and exempting approved changes from regulatory review. Yet, the impact of this …
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
USF Tampa Graduate Theses and Dissertations
Suicide prevention through early detection using social media data has been widely studied. However, the critical role of peer support interactions among individuals with similar mental disorders has not been deeply investigated or explored. In this study, we explore peer interactions in online communities for individuals with bipolar disorder and leverage this information to predict suicide risk levels. We propose a model that uses contextualized posts and comments along with their sentiment features. By embedding these features into a peer support network, our model captures peer interactions and predicts suicide risk levels using the bidirectional LSTM Graph Neural Networks (Bi-LSTM …
Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill
Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill
University of Cincinnati Law Review
Federal agencies have caught the artificial intelligence (AI) bug. A December 2023 report by the Government Accountability Office found that twenty of twenty-three federal agencies surveyed reported using some form of AI, with about two hundred current use cases for algorithms and about one thousand more in the planning phase. These agencies are using algorithms in all aspects of administration, including rulemaking, adjudication, and enforcement. The risks of AI are well-documented. Previous work has shown that algorithms can be, among other things, biased and prone to error. However, perhaps no problem poses a more serious threat to the use of …
What Do We Know About Hugging Face? A Systematic Literature Review And Quantitative Validation Of Qualitative Claims, Jason Jones, Wenxin Jiang, Nicholas Synovic, George K. Thiruvathukal, James C. Davis
What Do We Know About Hugging Face? A Systematic Literature Review And Quantitative Validation Of Qualitative Claims, Jason Jones, Wenxin Jiang, Nicholas Synovic, George K. Thiruvathukal, James C. Davis
Computer Science: Faculty Publications and Other Works
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Prior research has examined SPRs for traditional software, such as NPM (JavaScript) and PyPI (Python). Pre-Trained Model (PTM) Registries are an emerging class of SPR of increasing importance, because they support the deep learning supply chain.
Aims: Recent empirical research has examined PTM registries in ways such as vulnerabilities, reuse processes, and evolution. However, no existing research synthesizes them to provide a systematic understanding of the current knowledge. Some of the existing research includes qualitative …
Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan
Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan
Faculty, Staff and Student Publications
The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …
Key Technology Foresight And Policy Recommendation For Information Technology Enabling Smart Justice, Yirong Wu, Shuo Liu
Key Technology Foresight And Policy Recommendation For Information Technology Enabling Smart Justice, Yirong Wu, Shuo Liu
Bulletin of Chinese Academy of Sciences (Chinese Version)
Fair justice is the last line of defense for maintaining social fairness and justice, and promoting the construction of smart justice is an important means to improve the efficiency and fairness of judicial work. Currently, China’s smart judiciary is in a critical stage of upgrading its intelligence in areas such as investigation, trial, supervision, and judicial execution, empowered by information technology. The application of information technology in national governance faces order dilemmas and security risks, with the establishment of corresponding laws and regulations demonstrating noticeable lag. Designating to meet the demand of information technology supporting the construction of smart justice …
Enhancing Scenario-Oriented Technology Foresight On Information Technology For Public Governance, Kaihua Chen, Shuo Wang, Chao Zhang, Jie Yang, Xiaoguang Yang, Lei Guo, Yue Hao
Enhancing Scenario-Oriented Technology Foresight On Information Technology For Public Governance, Kaihua Chen, Shuo Wang, Chao Zhang, Jie Yang, Xiaoguang Yang, Lei Guo, Yue Hao
Bulletin of Chinese Academy of Sciences (Chinese Version)
To effectively adapt to the reshaping of public governance concepts, models, and tools driven by the new generation of information technologies represented by artificial intelligence, it is essential to take practical application scenarios as the entry point. Implementing periodic technology foresight on public governance information technologies can promote the integrated development of governance and digital technologies. This is an urgent task for both the public governance and information technology domains. Foresight for information technologies supporting public governance must be oriented toward the demands of complex data-driven and intelligent governance scenarios. It should focus on guiding technological directions with social value, …
Challenges, Countermeasures, And Forward-Looking Technologies For Cyber Society Governance From System Security Perspective, Hongbin Pei, Jingxin Hai, Pinghui Wang, Xiaohong Guan
Challenges, Countermeasures, And Forward-Looking Technologies For Cyber Society Governance From System Security Perspective, Hongbin Pei, Jingxin Hai, Pinghui Wang, Xiaohong Guan
Bulletin of Chinese Academy of Sciences (Chinese Version)
With the rapid development and widespread application of artificial intelligence and information technologies, the cyber society has evolved into a new form featuring human-machine integration, mass interaction, and intelligent interconnection. While transforming human life and production, it has also brought security risks such as cyberattacks, privacy breaches, online rumors, and information cocoons, posing serious challenges to social order. Cyber society governance has thus become a key component of national governance modernization. This study proposes the STC governance paradigm from a system security perspective, covering three key dimensions: security (cyber infrastructure security), trustworthiness (credibility of cyber entity identities), and controllability (content …
Empowering Modernization Of National Emergency Management With New Generation Of Information Technology: Technology Foresight And Policy Recommendations Based On Typical Scenarios, Haibo Zhang, Xinyu Dai, Yi Peng, Yi Liu, Xue Lin, Yongjian Zhu, Wu Chen, Zhen Wu, Xinyue Qin, Depei Qian, Jian Lv
Empowering Modernization Of National Emergency Management With New Generation Of Information Technology: Technology Foresight And Policy Recommendations Based On Typical Scenarios, Haibo Zhang, Xinyu Dai, Yi Peng, Yi Liu, Xue Lin, Yongjian Zhu, Wu Chen, Zhen Wu, Xinyue Qin, Depei Qian, Jian Lv
Bulletin of Chinese Academy of Sciences (Chinese Version)
In the context of the new round of scientific and technological revolution, how to grasp the historical opportunity of supporting the development of the “overall safety and emergency response framework” with the NGIT, and promoting the transformation of the public safety governance to emphasis on prevention, is a pressing issue to be studied. This study focuses on five typical scenarios in emergency management, drawing on multiple rounds of expert interviews and questionnaire surveys to identify a list of critical technologies, analyze future development trends and constraints, and provide references for advancing relevant technological research and development. The study further emphasizes …
Key Technology Selection And Countermeasures To Promote Full Lifecycle Data Governance, Yuyao Feng, Hongyun Zhang, Pengfei Wang, Jianping Li, Zongben Xu
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 …
Accelerating The Development Of Ai-Driven Knowledge Innovation System, Innovation Paradigm Research Group, Chinese Academy Of Sciences
Accelerating The Development Of Ai-Driven Knowledge Innovation System, Innovation Paradigm Research Group, Chinese Academy Of Sciences
Bulletin of Chinese Academy of Sciences (Chinese Version)
The world is undergoing a paradigm shift in full-chain innovation driven by AI-powered scientific research, which will give rise to a completely new knowledge innovation system. This profound and comprehensive transformation presents a critical opportunity for China to build an independent knowledge innovation system and a major historical chance to accelerate its development into a science and technology powerhouse. The deep impact of this change stems from AI’s ability to reshape the epistemology and methodology of knowledge innovation, accelerating the iteration of scientific theories and the deep restructuring of knowledge systems. This shift is making the structure of knowledge more …
Review Of Current Trends In Information Technology Concerning Phonetic Similarity”, Zaid Rajih Mohammed, Ahmed H. Aliwy
Review Of Current Trends In Information Technology Concerning Phonetic Similarity”, Zaid Rajih Mohammed, Ahmed H. Aliwy
Al-Bahir
With the increasing availability of textual information in various languages via the Internet in homes and companies through Internet and intranet services, there is an urgent need for the technologies and tools necessary to process this information, phonetic representation, and voice interaction. For example voice to voice machine translation need to phonetic mapping and similarity among the languages especially for names and foreign words. This one example of the importance of phonetic mapping and similarity. This article aims to describe, in detail, the recent surge in interest and advancements in phonetic similarity (PS), phonetic representation, and phonetic mapping researches. PS …
The Evolution Of Community Engagement Strategies In The Context Of Advancements In Artificial Intelligence Technologies, Erika Grodzki, Stefanie Powers, Gary Carlin, Hung Chum Kao
The Evolution Of Community Engagement Strategies In The Context Of Advancements In Artificial Intelligence Technologies, Erika Grodzki, Stefanie Powers, Gary Carlin, Hung Chum Kao
Faculty and Staff Publications & Presentations
As the AI landscape continues to evolve, the need for communities to nurture, inform, and challenge these technologies becomes paramount. This panel addresses the importance of traditional and AI driven strategies in terms of community engagement. Four case studies are reviewed to show how AI technologies are being implemented in communities. While AI offers numerous benefits, it is important not to overlook the value of human interaction in building and maintaining thriving community engagement. AI cannot replicate emotional intelligence and authentic relationship building - critical components of community engagement.
Personalized Learning Path Problem Variations: Computational Complexity And Ai Approaches, Sean A. Mochocki, Mark Reith, Brett J. Borghetti, Gilbert L. Peterson, John Jasper, Laurence D. Merkle
Personalized Learning Path Problem Variations: Computational Complexity And Ai Approaches, Sean A. Mochocki, Mark Reith, Brett J. Borghetti, Gilbert L. Peterson, John Jasper, Laurence D. Merkle
Faculty Publications
E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of …
Artificial Intelligence And Machine Learning In Ocular Oncology, Retinoblastoma (Armor): Experience With A Multiracial Cohort, Vijitha S. Vempuluru, Rajiv Viriyala, Virinchi Ayyagari, Komal Bakal, Patanjali Bhamidipati, Krishna Krishore Dhara, Sandor R. Ferenczy, Carol L. Shields, Swathi Kaliki
Artificial Intelligence And Machine Learning In Ocular Oncology, Retinoblastoma (Armor): Experience With A Multiracial Cohort, Vijitha S. Vempuluru, Rajiv Viriyala, Virinchi Ayyagari, Komal Bakal, Patanjali Bhamidipati, Krishna Krishore Dhara, Sandor R. Ferenczy, Carol L. Shields, Swathi Kaliki
Wills Eye Hospital Papers
Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. …
Enhanced Lithological Mapping In Arid Crystalline Regions Using Explainable Ai And Multi-Spectral Remote Sensing Data, Hesham Morgan, Ali Elgendy, Amir Said, Mostafa Hashem, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Enhanced Lithological Mapping In Arid Crystalline Regions Using Explainable Ai And Multi-Spectral Remote Sensing Data, Hesham Morgan, Ali Elgendy, Amir Said, Mostafa Hashem, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Lithological classification is essential for understanding the spatial distribution of rocks, especially in arid crystalline areas. Artificial intelligence (AI) recent advancements with multi-spectral satellite imagery have been utilized to enhance lithological mapping in these areas. Here we employed different AI models namely, Support Vector Machine (SVM), Random Forest Classification (RFC), Logistic Regression, XGBoost, and K-nearest neighbors (KNN) for lithological mapping. This was followed by the application of explainable AI (XAI) for lithological discrimination (LD) which is still not widely explored. Based on the highest accuracy and F1 score of the previously mentioned models, RFC model outperformed all of them, and …
Tradeoffs Of Generalization, Kyra M. Abrams, Peter T. Darch
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 …
An Introduction To The Time-Independent Schrödinger Equation And Methods To Solve It, Vu Giang, Alex Gnech
An Introduction To The Time-Independent Schrödinger Equation And Methods To Solve It, Vu Giang, Alex Gnech
OUR Journal: ODU Undergraduate Research Journal
The Time-Independent Schrödinger Equation is a linear elliptic PDE that describes quantum-mechanical systems. Its significance in the science of submicroscopic phenomena, particularly quantum mechanics, is as central as Newton’s laws of motion are to classical mechanics. This study uses various methods, including novel neural networks and finite difference schemes, to solve the one-dimensional two-body equation.
A Plugin-Based Unreal Engine Adapter For Hla-Based Distributed Simulation, Mei Yang, Peng Wang
A Plugin-Based Unreal Engine Adapter For Hla-Based Distributed Simulation, Mei Yang, Peng Wang
Journal of System Simulation
Abstract: With the wide application of game engine-based simulation in transportation, military and other fields, the demand for interoperability between game engine and traditional simulations is becoming increasingly strong. For the HLA-based integration of Unreal Engine and the traditional simulations, a plugin-based Unreal Engine adapter for distributed simulation is designed, which enables the rapid development of Unreal Engine federate and the efficient integration. The simulation shows the feasibility of the plugin-based Unreal Engine adapter.
Research On Sequential Design Methods For Satellite Combat Simulation Tests, Yanlin Wang, Zhijun Cheng, Zichen Wang, Jian Zhong
Research On Sequential Design Methods For Satellite Combat Simulation Tests, Yanlin Wang, Zhijun Cheng, Zichen Wang, Jian Zhong
Journal of System Simulation
Abstract: Aiming at the problem that satellite monitoring mission simulation tests cannot take into account the number of sample points and model accuracy in the complex test space, a hybrid sequential test design method for satellite simulation tests based on sample density and local nonlinearity is proposed. Voronoi division is used to describe the density of discrete point distribution, and the nonlinearity is measured with the help of Taylor expansion and sample point neighborhood gradient information. The two are combined to calculate the hybrid metrics, and the sample points are ranked and new ones are added until the stopping criterion …