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Articles 93571 - 93600 of 2913339
Full-Text Articles in Entire DC Network
Sustainable Practices In Highway Construction: A Green Evaluation Approach With Hypersoft Set, Xiaodong Zhang
Sustainable Practices In Highway Construction: A Green Evaluation Approach With Hypersoft Set, Xiaodong Zhang
Neutrosophic Sets and Systems
No abstract provided.
An Empirical Study On The Quality Of Industry-Linked Education In Vocational Colleges: Double-Framed Treesoft Set Framework, Yanbin Liu, Peina Liang, Jingjie Ma
An Empirical Study On The Quality Of Industry-Linked Education In Vocational Colleges: Double-Framed Treesoft Set Framework, Yanbin Liu, Peina Liang, Jingjie Ma
Neutrosophic Sets and Systems
No abstract provided.
Indetermsoft Set For Artificial Intelligence Solutions For Teaching Competition Plans Assessment In Colleges, Xun Lin, Xiaoli Feng
Indetermsoft Set For Artificial Intelligence Solutions For Teaching Competition Plans Assessment In Colleges, Xun Lin, Xiaoli Feng
Neutrosophic Sets and Systems
No abstract provided.
Windows Malware Detection Under The Machine Learning Models And Neutrosophic Numbers, Alber S. Aziz, Mohamed Eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ashraf. M. Hussein, Nariman A. Khalil
Windows Malware Detection Under The Machine Learning Models And Neutrosophic Numbers, Alber S. Aziz, Mohamed Eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ashraf. M. Hussein, Nariman A. Khalil
Neutrosophic Sets and Systems
No abstract provided.
Green Transformation Evaluation Of Industrial Economies In Resource-Based Regions Under The Superhypersoft Set Model, Zixi Qian
Neutrosophic Sets and Systems
No abstract provided.
Assessment Of Artificial Intelligence-Driven Fitness And Health Management Programs For Adolescents Using The Superhypersoft Set Framework, Di Wu, Ali Khatibi, Jacquline Tham
Assessment Of Artificial Intelligence-Driven Fitness And Health Management Programs For Adolescents Using The Superhypersoft Set Framework, Di Wu, Ali Khatibi, Jacquline Tham
Neutrosophic Sets and Systems
No abstract provided.
An Application Of Site Selection For Solid Waste Management System Using Neutrosophic Set, A. Savitha Mary, D. Sarukasan, C. Kayelvizhi, L. Jethruth Emelda Mary, F. Josephine Daisy, K. Pitchaimani
An Application Of Site Selection For Solid Waste Management System Using Neutrosophic Set, A. Savitha Mary, D. Sarukasan, C. Kayelvizhi, L. Jethruth Emelda Mary, F. Josephine Daisy, K. Pitchaimani
Neutrosophic Sets and Systems
No abstract provided.
Analyzing The Performance Of Ideological And Political Education In Universities: A Neutrosophic Approach To Handling Truth, Indeterminacy, And Falsehood In Students' Responses, Di Wangm
Neutrosophic Sets and Systems
No abstract provided.
Beyond Binary Judgments: A Neutrosophic Framework For Evaluating News Writing Quality Through Common And Uncommon Meaning, Xiaochun Yuan
Beyond Binary Judgments: A Neutrosophic Framework For Evaluating News Writing Quality Through Common And Uncommon Meaning, Xiaochun Yuan
Neutrosophic Sets and Systems
No abstract provided.
Integrating Treesoft And Hypersoft Paradigms Into Urban Elderly Care Evaluation: A Comprehensive N-Superhypergraph Approach, Yan Cao
Neutrosophic Sets and Systems
No abstract provided.
Topsis Method-Based Decision-Making Model For Bipolar Quadripartitioned Neutrosophic Environment, G. Muhiuddin, Mohamed E. Elnair, Satham Hussain S, Durga Nagarajan
Topsis Method-Based Decision-Making Model For Bipolar Quadripartitioned Neutrosophic Environment, G. Muhiuddin, Mohamed E. Elnair, Satham Hussain S, Durga Nagarajan
Neutrosophic Sets and Systems
No abstract provided.
A Neutrostructural Methodology For Empowering New-Quality Productivity Toward High-Quality Development In The Smart Elderly Care Industry, Xiaoqin Yang
Neutrosophic Sets and Systems
No abstract provided.
Kindergarten Teachers’ Classroom Management Abilities: A Neutrosophic Perspective On Partial Locality, Indeterminacy, And Non-Local Influences, Yanzi Zhang
Neutrosophic Sets and Systems
No abstract provided.
Neutrosophic Sets And Systems, Vol. 85,2025, Florentin Smarandache, Mohamed Abdel-Basset, Maikel. Leyva Vazquez
Neutrosophic Sets And Systems, Vol. 85,2025, Florentin Smarandache, Mohamed Abdel-Basset, Maikel. Leyva Vazquez
Neutrosophic Sets and Systems
No abstract provided.
A Brief Guide To Statistical Analysis Of Grouped Data In Preclinical Research, Colby J Vorland, Lilian Golzarri-Arroyo, David B Allison
A Brief Guide To Statistical Analysis Of Grouped Data In Preclinical Research, Colby J Vorland, Lilian Golzarri-Arroyo, David B Allison
Children’s Nutrition Research Center Staff Publications
Clustering and nesting (C&N) arise in many preclinical studies such as when animals are group-housed, share litters, or in cell culture. Ignoring C&N undermines the validity of analyses. We explain how C&N arise and valid designs and analyses.
The Diabetes Prevention Program And Its Outcomes Study: Niddk's Journey Into The Prevention Of Type 2 Diabetes And Its Public Health Impact, Jill P Crandall, Dana Dabelea, William C Knowler, David M Nathan, Marinella Temprosa, Dpp Research Group
The Diabetes Prevention Program And Its Outcomes Study: Niddk's Journey Into The Prevention Of Type 2 Diabetes And Its Public Health Impact, Jill P Crandall, Dana Dabelea, William C Knowler, David M Nathan, Marinella Temprosa, Dpp Research Group
Children’s Nutrition Research Center Staff Publications
The current-day epidemic of type 2 diabetes, largely driven by increased adiposity and reduced physical activity in the setting of genetic susceptibility, is a major public health challenge. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) presciently proposed the Diabetes Prevention Program (DPP), a multicenter randomized clinical trial, designed by investigators in conjunction with NIDDK staff and initiated in 1996. The primary goal of DPP was to determine whether an intensive lifestyle intervention (ILS) or metformin in comparison with placebo would reduce the development of diabetes in a high-risk population with prediabetes. After mean 2.8 years, ILS …
To Pause With A Cliffhanger Or A Temporary Closure? The Differential Impact Of Serial Versus Episodic Narratives On Children's Physical Activity Behaviors, Amy Shirong Lu, Melanie C Green, Caio Victor Sousa, Jungyun Hwang, I-Min Lee, Debbe Thompson, Tom Baranowski
To Pause With A Cliffhanger Or A Temporary Closure? The Differential Impact Of Serial Versus Episodic Narratives On Children's Physical Activity Behaviors, Amy Shirong Lu, Melanie C Green, Caio Victor Sousa, Jungyun Hwang, I-Min Lee, Debbe Thompson, Tom Baranowski
Children’s Nutrition Research Center Staff Publications
Research has supported the effectiveness of narratives for promoting health behavior, but different narrative presentation formats (serial vs. episodic) have seldom been compared. Suspense theories suggest that serial narratives, which do not provide a full resolution at the end of an episode, may create higher motivation for continued engagement with a story. Forty-four 8 to 12-year-old children were randomly assigned to watch an animation series designed for an existing active video game in which the plot was delivered either continuously across multiple episodes (serial) or in multiple yet relatively independent self-contained episodes (episodic). Controlling for social desirability, children who watched …
Assessment Of Patient Knowledge And Awareness Of Prosthodontics For Student And Patient Education And Communication, Mijin Choi D.D.S., M.S., M.B.A., Facp, Tejal Gohil B.D.S., Cristina Osorio D.D.S., Hannah Jeong, Thomas S. Giugliano D.D.S.
Assessment Of Patient Knowledge And Awareness Of Prosthodontics For Student And Patient Education And Communication, Mijin Choi D.D.S., M.S., M.B.A., Facp, Tejal Gohil B.D.S., Cristina Osorio D.D.S., Hannah Jeong, Thomas S. Giugliano D.D.S.
The New York State Dental Journal
This study evaluates patient understanding of the scope of prosthodontic treatments. Utilizing an 11-question survey, 149 patients at a postgraduate prosthodontic clinic were queried. Data analysis included frequency summaries, cross-tabulations and Pearson Chi-square tests. Results showed 68.4% of respondents knew about the role of prosthodontists. Their main concerns were improving chewing (43.9%) and smile (42.6%), varying by age. While many recognized prosthodontists’ scope, cosmetic and complex dental treatments were less well known as prosthodontic specialties. This highlights a gap in patient awareness regarding the full range of prosthodontic services.
Language Brokering Conditions The Indirect Association Between Mexican‐Origin Adolescents' Academic Discrimination And Educational Expectations, Su Yeong Kim, Yayu Du, Chantal Alvarado, Wei Xiang Sim, Wen Wen, Tianlu Zhang, Jingyi Shen
Language Brokering Conditions The Indirect Association Between Mexican‐Origin Adolescents' Academic Discrimination And Educational Expectations, Su Yeong Kim, Yayu Du, Chantal Alvarado, Wei Xiang Sim, Wen Wen, Tianlu Zhang, Jingyi Shen
Research Collection School of Social Sciences
Mexican-origin adolescents, a significant portion of the US Latino population, often experience a decline in educational expectations from early to late adolescence. Contextual factors such as academic discrimination and language brokering for parents may contribute to this decline. This study investigates the indirect effect of academic discrimination experienced in middle school on educational expectations in young adulthood through high school grades and engagement, and the moderating role of language brokering experiences in these relations. Data were collected from 604 Mexican-origin adolescents across four waves from 2012 to 2023. Academic discrimination experiences in middle school were negatively associated with school grades …
Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong
Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong
Research Collection School Of Computing and Information Systems
Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …
Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang
Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang
Research Collection School Of Computing and Information Systems
Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMSCAN, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMSCAN systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s ‘brain’ behaves differently when generating harmful or untruthful responses. By analyzing …
An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi
An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi
Research Collection School Of Computing and Information Systems
We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to formula size in the worst case. In this case, the synthesis cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components …
Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo
Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …
Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam
Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries—the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of …
Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong
Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong
Research Collection School Of Computing and Information Systems
It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and …
Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng
Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write …
Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng
Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Android system provides application developers with the ability to define custom permissions, which serve to moderate the sharing of resources and interactions with other applications. However, poor development practices of developers can render the permission mechanism ineffective, weakening the system protection. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, or even component hijacking. To automatically …
Diversity Optimization For Travelling Salesman Problem Via Deep Reinforcement Learning, Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong
Diversity Optimization For Travelling Salesman Problem Via Deep Reinforcement Learning, Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong
Research Collection School Of Computing and Information Systems
Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a …
An Efficient Diffusion-Based Non-Autoregressive Solver For Traveling Salesman Problem, Mingzhao Wang, You Zhou, Zhiguang Cao, Yubin Xiao, Xuan Wu, Wei Pang, Yuan Jiang, Hui Yang, Peng Zhao, Yuanshu Li
An Efficient Diffusion-Based Non-Autoregressive Solver For Traveling Salesman Problem, Mingzhao Wang, You Zhou, Zhiguang Cao, Yubin Xiao, Xuan Wu, Wei Pang, Yuan Jiang, Hui Yang, Peng Zhao, Yuanshu Li
Research Collection School Of Computing and Information Systems
Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-autoregressive (NAR) approaches benefit from faster inference through parallelism, they typically deliver solutions of inferior quality compared to autoregressive ones. To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner. Firstly, we introduce a one-step diffusion model that integrates the controlled discrete noise addition process with self-consistency enhancement, enabling optimal solution prediction through simultaneous denoising of multiple solutions. Secondly, we …
Retrieval Augmented Generation For Dynamic Graph Modeling, Yuxia Wu, Lizi Liao, Yuan Fang
Retrieval Augmented Generation For Dynamic Graph Modeling, Yuxia Wu, Lizi Liao, Yuan Fang
Research Collection School Of Computing and Information Systems
Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering relevant insights over time. Traditional approaches primarily rely on graph neural networks with temporal components or sequence generation models, which often focus narrowly on the historical context of target nodes. This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader …