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arXiv 2025年

作者： Rameshwar, V. Arvind Tandon, Anshoo India Urban Data Exchange Program Unit Indian Institute of Science Bengaluru India

In this article, we revisit the well-studied problem of mean estimation under user-level Ε-differential privacy (DP). While user-level Ε-DP mechanisms for mean estimation, which typically bound (or clip) user contributions to reduce sensitivity, are well-known, an analysis of their estimation errors usually assumes that the data samples are independent and identically distributed (i.i.d.), and sometimes also that all participating users contribute the same number of samples (data homogeneity). Our main result is a precise characterization of the worst-case estimation error under general clipping strategies, for heterogeneous data, and as a by-product, the clipping strategy that gives rise to the smallest worst-case error. Interestingly, we show via experimental studies that even for i.i.d. samples, our clipping strategy performs uniformly better that the well-known clipping strategy of Amin et al. (2019), which involves additional, private parameter estimation. © 2025, CC BY.

关键词： Differential privacy

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Some granular structures for weighted digraphs

Some granular structures for weighted digraphs

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Tse-Yu Lin Data Science Degree Program National Taiwan University

Graphs are non-Euclidean data structures used to model interaction relationships between abstract objects. When additional structures, such as graph orientation, weighted vertices, or weight functions on edges, are imposed on the graph, additional graph properties can be applied to discover hidden patterns within the structure of the graph. In this paper, we discuss possible granular structures for graphs, with a focus on granular structure for edge-weighted digraphs derived from pairwise comparison problems. These structures have been applied in various fields such as recommender systems, graph databases, and social network analysis.

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ERFSL: An Efficient Reward Function Searcher via Large Language Models for Custom-EnvironmentMulti-ObjectiveReinforcementLearning 39

ERFSL: An Efficient Reward Function Searcher via Large Langu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xie, Guanwen Xu, Jingzehua Yang, Yiyuan Ding, Yimian Zhang, Shuai MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Computer Science University of Oxford United Kingdom Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)157735897X

We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective reinforcement learning (RL). ERFSL generates reward components based on explicit user requirements and rectifies them, and iteratively optimizes the weights of these components based on textual context. Applied to an underwater data collection RL task, ERFSL corrects reward codes with only one feedback iteration per requirement, and acquires diverse reward functions within the Pareto set. ERFSL also presents robust capability for deviated weights and small-size LLMs such as GPT-4o mini. The full-text prompts, examples of LLM-generated answers, and source code are available at https://***/LLMRsearcher/. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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AoI-MDP: An AoI Optimized Markov Decision Process Dedicated in The Underwater Task 39

AoI-MDP: An AoI Optimized Markov Decision Process Dedicated ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ding, Yimian Xu, Jingzehua Yang, Yiyuan Xie, Guanwen Wang, Xinqi Zhang, Shuai MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Computer Science University of Oxford United Kingdom Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)157735897X

Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI-MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI-MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision-making using reinforcement learning. Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open-source at https://***/Xiboxtg/AoI-MDP. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Markov processes

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： Computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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Adoption without Transformation: AI and Digital Transformation in China and Taiwan 24

Adoption without Transformation: AI and Digital Transformati...

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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Wong, Wilson Wong, Natalie Wai-Man Hinnant, Charles C. Data Science and Policy Studies Program Chinese University of Hong Kong Hong Kong Department of Public Administration National Chengchi University Taiwan School of Information Florida State University United States

ISBN: (纸本)9798400709883

Although there exists a growing body of literature exploring AI and its applications in the public sector, few explanatory theories have been developed. This paper aims to address this gap by examining the AI strategies of China and Taiwan through an analysis of their respective strategic documents. In this study, we find China has adopted a government-driven approach and Taiwan has pursued a public-private partnership approach, showcasing their own distinct characteristics and values. In contrast to predictions made in the existing literature on digital transformation the introduction of AI-driven changes has not led to a fundamental shift or breakthrough in the trajectory of the two public administration systems. Rather than leveraging AI for transformative purposes, governments primarily employ it to reinforce their current trajectory. This study highlights a theory-practice gap in the AI digital transformation of the public sector, emphasizing the need for theory-building rooted in the core theories and public values of public administration. © 2024 Copyright held by the owner/author(s).

关键词： Public administration

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Novel NBA Fantasy League driven by Engineered Team Chemistry...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Arkanath, Ganesh Gupta, Nishad Kurban, Hasan Sharma, Parichit Madhavan, K.R. Buxton, Elham Khorasani Dalkilic, Mehmet M. Texas A&m University at Qatar Electrical and Computer Engineering Doha Qatar Indiana University Computer Science Department BloomingtonIN United States Indiana University Data Science Program BloomingtonIN United States University of Illinois Computer Science Department SpringfieldIL United States

ISBN: (纸本)9798350324457

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position;(2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan. © 2023 IEEE.

关键词： big data data-mining feature-engineering nba-fantasy-league python-flask

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Performance prediction in major league baseball by long short-term memory networks

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International Journal of data science and Analytics 2023年第1期15卷 93-104页

作者： Sun, Hsuan-Cheng Lin, Tse-Yu Tsai, Yen-Lung Center for Information Systems and Technology Claremont Graduate University Claremont United States Data Science Degree Program National Taiwan University Taipei Taiwan Department of Mathematical Sciences National Chengchi University Taipei Taiwan

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models’ ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users’ needs. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Baseball

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