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Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2025年第1期31卷 754-764页

作者： Koonchanok, Ratanond Papka, Michael E. Reda, Khairi Indiana Univ Indianapolis Indianapolis IN 46202 USA Argonne Natl Lab Lemont IL USA Univ Illinois Chicago IL USA

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://***/qmfv6

关键词： Bayes methods Data visualization Uncertainty Correlation Accuracy Decision making biological system modeling Visual inference statistical rationality human-machine collaboration

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The Role of Generative Artificial Intelligence in Internet of Electric Vehicles

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 6208-6232页

作者： Zhang, Hanwen Niyato, Dusit Zhang, Wei Zhao, Changyuan Du, Hongyang Jamalipour, Abbas Sun, Sumei Pei, Yiyang Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Singapore Inst Technol Informat & Commun Technol Cluster Singapore 138683 Singapore Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Sydney Sch Elect & Comp Engn Sydney NSW 2006 Australia

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this article, we specifically consider Internet of Electric Vehicles (IoEV) and we categorize GenAI for IoEV into four different layers, namely, EV's battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

关键词： Electric vehicle charging Batteries Electricity Schedules Routing biological system modeling Security Long short term memory Load modeling Job shop scheduling Forecasting generative artificial intelligence (GenAI) Internet of electric vehicles (IoEV) scenarios generation scheduling

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A Topological-Indicators-Based k-Means Clustering Algorithm and Its Application in Time Series Data: A Case Study on Sea Level Variability in Peninsular Malaysia

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IEEE ACCESS 2025年 13卷 46514-46533页

作者： Lin, Zixin Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Bin Mohd Gobithaasan, Rudrusamyr Univ Sains Malaysia USM Sch Math Sci George Town 11800 Penang Malaysia

Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, particularly when applied to datasets with mixed groups or significant noise. In this study, we analyzed monthly sea level data derived from daily time series at 14 tide gauge stations along the coastline of Peninsular Malaysia. To enhance traditional k-means clustering, we propose a hybrid approach that combines clustering techniques with topological data analysis (TDA). Specifically, we integrate k-means and its variant, k-means++, with persistent homology, the primary tool in TDA, to capture topological insights from the datasets. The proposed approach clusters the 14 tide gauge stations based on predefined topological features, and the probability of data points from each station belonging to specific clusters is computed. The results demonstrate that our approach significantly improves the performance of traditional k-means clustering by incorporating topological information, compared to using clustering without such insights.

关键词： Sea level Sea measurements Point cloud compression Time series analysis Vectors Noise Clustering algorithms biological system modeling Analytical models Tides Machine learning computational topology environmental sustainability sea level variability

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Link Prediction in Complex Hyper-Networks Leveraging HyperCentrality

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IEEE ACCESS 2025年 13卷 12239-12254页

作者： Nandini, Y. V. Jaya Lakshmi, T. Krishna Enduri, Murali Zairul Mazwan Jilani, Mohd SRM Univ AP Dept Comp Sci & Engn Amaravati 522502 Andhra Pradesh India Sheffield Hallam Univ Dept Comp Sheffield S1 1WB England

In complex networks, predicting the formation of new connections, or links, within complex networks has been a central challenge, traditionally addressed using graph-based models. These models, however, are limited in their ability to capture higher-order interactions that exist in many real-world networks, such as social, biological, and technological systems. To account for these multi-node interactions, hyper-networks have emerged as a more flexible framework, where hyperedges can connect multiple nodes simultaneously. Traditional link prediction methods often treat all common neighbors equally, overlooking the fact that not all nodes contribute uniformly to the formation of future links. Each node within a network holds a distinct level of importance, which can influence the likelihood of link formation among its neighbors. To address this, we introduce a link prediction approach leveraging hypercentrality measures adapted from traditional centrality metrics such as degree, clustering coefficient, betweenness, and closeness to capture node significance and improve link prediction in hyper-networks. We propose the Link Prediction Based on HyperCentrality in hyper-networks (LPHC) model, which enhances traditional common neighbor and jaccard coefficient of hyper-network frameworks by incorporating centrality scores to account for node importance. Our approach is evaluated across multiple real-world hyper-networks datasets, demonstrating its superiority over traditional link prediction methods. The results show that link prediction in hypercentrality-based models, particularly those utilizing hyperdegree and hyperclustering coefficients for common neighbor and jaccard coefficent approaches in hyper-networks, consistently outperform existing methods in terms of both F1-score and Area Under the Precision-Recall Curve (AUPR), offering a more precise understanding of potential link formations in hyper-networks. The proposed LPHC model consistently outperforms the existi

关键词： Predictive models Collaboration Complex networks Proteins Complexity theory biological system modeling Reviews Multiplexing Hypertext systems Computational modeling Hyper-networks link prediction centrality measures

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YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 385-397页

作者： Yuan, Junchao Wang, Lina Wang, Tingting Bashir, Ali Kashif Al Dabel, Maryam M. Wang, Jiaxing Feng, Hailin Fang, Kai Wang, Wei Zhejiang A&F Univ Coll Math & Comp Sci Hangzhou 311300 Peoples R China Macau Univ Sci & Technol Coll Comp Sci & Engn Fac Innovat Engn Macau 999078 Peoples R China Manchester Metropolitan Univ Dept Comp & Math Manchester England Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Univ Hafr Albatin Dept Comp Sci & Engn Hafar Al Batin Saudi Arabia Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310014 Peoples R China Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Guangdong Hong Kong Macao Joint Lab Emot Intellige Shenzhen 518172 Peoples R China

Pine wilt disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors, such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this article presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and fuzzy deep neural networks to develop a residual fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a detail processing module into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the dynamic upsampling operator, our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well suited for PWD detection in interfering environments.

关键词： Feature extraction Diseases biological system modeling Accuracy Object detection Forestry Noise Remote sensing Interference Adaptation models Fuzzy deep neural networks (FDNNs) interference environments remote sensing residual learning upsampling process

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Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 801-815页

作者： Wei, Shengxun Gao, Zan Ma, Chunjie Zhao, Yibo Guan, Weili Chen, Shengyong Tianjin Univ Technol Key Lab Comp Vis & Syst Minist Educ Tianjin 300384 Peoples R China Qilu Univ Technol Shandong Artificial Intelligence Inst Shandong Acad Sci Jinan 250014 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 150001 Peoples R China

Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay between intra-class and inter-class variations and to identify features that remain unaffected by changes in appearance. Sufficient data collection for model training would significantly aid in addressing this problem. However, it is challenging to gather diverse datasets in practice. Current methods focus on implicitly learning identity information from the original image or introducing additional auxiliary models, which are largely limited by the quality of the image and the performance of the additional model. To address these issues, inspired by prompt learning, we propose a novel multiple information prompt learning (MIPL) scheme for cloth-changing person ReID, which learns identity robust features through the common prompt guidance of multiple messages. Specifically, the clothing information stripping (CIS) module is designed to decouple the clothing information from the original RGB image features to counteract the influence of clothing appearance. The bio-guided attention (BGA) module is proposed to increase the learning intensity of the model for key information. A dual-length hybrid patch (DHP) module is employed to make the features have diverse coverage to minimize the impact of feature bias. Extensive experiments demonstrate that the proposed method outperforms all state-of-the-art methods on the LTCC, Celeb-reID, Celeb-reID-light, and CSCC datasets, achieving rank-1 scores of 74.8%, 73.3%, 66.0%, and 88.1%, respectively. When compared to AIM (CVPR23), ACID (TIP23), and SCNet (MM23), MIPL achieves rank-1 improvements of 11.3%, 13.8%, and 7.9%, respectively, on the PRCC dataset.

关键词： Clothing Pedestrians Visualization Training Feature extraction Computer vision Cameras biological system modeling Transformers Text recognition Cloth-changing person ReID prompt learning knowledge representation information retrieval vision-language learning

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DeepReg: A Trustworthy and Privacy-Friendly Ownership Regulatory Framework for Deep Learning Models

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 854-870页

作者： Zhuang, Xirong Zhang, Lan Tang, Chen Li, Yaliang Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230052 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230031 Peoples R China Alibaba Grp Bellevue WA 98004 USA

Well-trained deep learning (DL) models are widely recognized as valuable intellectual property (IP) and have been extensively adopted. However, concerns regarding IP infringement emerge when these models are either privately sold to end-users or publicly released online. Unauthorized activities, such as redistributing privately purchased models or exploiting restricted open-source models for commercial gain, pose a significant threat to the interests of model owners. In this paper, we introduce DeepReg, a trustworthy and privacy-friendly regulatory framework designed to address IP infringement within the realm of DL models, thereby nurturing a healthier development ecosystem. DeepReg enables a designated third-party regulator to extract the fingerprint of the original model within a Trusted Execution Environment, as well as to verify suspect models utilizing solely the predicted label without probability. Specifically, we leverage the uniqueness of feature extractors in DL models to craft multiple synthetic inputs for a selected real input. The real input, along with its synthetic inputs, establishes a one-to-many relationship, thereby creating a unique fingerprint for the original model. Furthermore, we propose two distinct methods for suspect detection and piracy judgment. These methods analyze the responses from the model API upon feeding the fingerprint, ensuring a high level of confidence while preventing malicious accusations. Experimental results demonstrate that DeepReg achieves 100% detection accuracy for pirated models, with zero false positives for irrelevant models.

关键词： biological system modeling Fingerprint recognition Computational modeling Regulators Feature extraction Data models Watermarking Codes Predictive models Regulation Model regulation model fingerprint piracy detection deep learning

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Ride-Sharing Determinants: Spatial and Spatio-Temporal Bayesian Analysis for Chicago Service in 2022

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IEEE ACCESS 2025年 13卷 61440-61456页

作者： Elkhouly, Mohamed Alhadidi, Taqwa Cleveland State Univ Dept Math & Stat Cleveland OH 44115 USA Al Ahliyya Amman Univ Civil Engn Dept Amman 19111 Jordan

Ridesharing services have revolutionized transportation, transforming the manner in which individuals navigate urban environments. The estimation of ride-sharing demand is crucial for enhancing service utilization, reliability, and mitigating traffic congestion. In this research, we employ spatial and spatiotemporal Bayesian models to estimate ride-sharing demand in the 77 Chicago community areas. We investigate various explanatory variables, including demographic, socio-economic, transportation, and land-use features. The Bayesian models adjust for random and structured errors to account for spatial correlations. Additionally, we utilize spatially correlated priors for explanatory variables to improve model precision. Model diagnostics demonstrate the absence of residual spatial structure in the spatial regression, confirming effective management of spatial correlation. The spatiotemporal model yields a squared correlation of 0.95 between observed ride-shares and fitted values, indicating robust predictive power. Demographic factors, including population size and crime rate, emerge as the primary determinants positively influencing ride-sharing demand. Higher income levels, increased numbers of economically active citizens, and higher proportions of car-free households are associated with increased demand. Public transit availability and walkability also play significant roles in ride-sharing patterns in Chicago. Our study demonstrates that ride-sharing demand recovered in 2022 following COVID-19-related closures, particularly during weekends. This research provides planners and policymakers with insights to enhance first and last mile services, identify underserved areas, and enable ride-sharing to address public transportation gaps. Such knowledge has the potential to enhance mobility for individuals with disabilities and those residing in rural areas. This study, utilizing Bayesian models, offers deeper insights into ride-sharing demand, thereby contributing to

关键词： biological system modeling Bayes methods Spatiotemporal phenomena Urban areas Data models Vehicle dynamics Shared transport Public transportation Predictive models Automobiles Bayesian analysis Chicago ridesharing spatial spatio-temporal

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Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection

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IEEE ACCESS 2025年 13卷 8691-8708页

作者： Sriram, Suthir Aburvan, P. Kaarthic, T. P. Arun Vijayaraj, Nivethitha Murugan, Thangavel Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai 601103 India United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates

Recently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid networks that also deliver anchor-free detection. In delivering YOLOv10, we enhance it with dual partial attention mechanism (DPAM) and dual adaptive label assignment with sun glint removal module (DALSM) along with marine fusion loss (MFL). With DPAM, the latest refinement processes for conservation focus on feature extraction to account for key highlights and in the scene, include temporal context, both critical for interpretation of the dynamic realm below the ocean. The prefix with DALSM involves adaptive dual label assignment and techniques for sun glint removal. The marine fusion loss (MFL) provides an object detection prediction that combines both binary cross-entropy loss and complete intersection over union (CIoU) loss to enhance bounding box localization while also including spatial context to incorporate important underwater features. With the experiments, we attenuate enhancements with device gradient clipping, model checkpointing, and advanced augmentation processes to gain 3.04% improvement in mean Average Precision (mAP). These enhancements mitigate perceived challenges of underwater detection while enhancing knowledge of understanding marine life.

关键词： Object detection Accuracy Feature extraction Fish Real-time systems biological system modeling Sun Attention mechanisms Visualization Lighting Dual partial attention mechanism (DPAM) dual adaptive label assignment with sun glint removal module (DALSM) marine-fusion loss (MFL) YOLOv10

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Image-and-Label Conditioning Latent Diffusion Model: Synthesizing Aβ-PET From MRI for Detecting Amyloid Status

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第2期29卷 1221-1231页

作者： Ou, Zaixin Pan, Yongsheng Xie, Fang Guo, Qihao Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China ShanghaiTech Univ State Key Lab Adv Med Mat & Devices Shanghai 201210 Peoples R China Fudan Univ Huashan Hosp Dept Nucl Med Shanghai 200040 Peoples R China Fudan Univ Huashan Hosp PET Ctr Shanghai 200040 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Gerontol Shanghai 200025 Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai 200030 Peoples R China Shanghai Clin Res & Trial Ctr Shanghai 201210 Peoples R China

Deposition of beta-amyloid (A beta), which is gen-erally observed by A beta-PET, is an important biomarker toevaluate subjects with early-onset dementia. However, ac-quisition of A beta-PET usually suffers from high expense andradiation hazards, making A beta-PET not commonly used asMRI. As A beta-PET scans are only used to determine whetherA beta deposition is positive or not, it is highly valuable tocapture the underlying relationship between A beta deposi-tion and other neuroimages (i.e., MRI) and detect amyloidstatus based on other neuroimages to reduce necessityof acquiring A beta-PET. To this end, we propose an image-and-label conditioning latent diffusion model (IL-CLDM) tosynthesize A beta-PET scans from MRI scans by enhancingcritical shared information to finally achieve MRI-based A beta classification. Specifically, two conditioning modules areintroduced to enable IL-CLDM to implicitly learn joint imagesynthesis and diagnosis: 1) an image conditioning module,to extract meaningful features from source MRI scans toprovide structural information, and 2) a label conditioningmodule, to guide the alignment of generated scans to thediagnosed label. Experiments on a clinical dataset of 510subjects demonstrate that our proposed IL-CLDM achievesimage quality superior to five widely used models, and oursynthesized A beta-PET scans (by IL-CLDM) can significantlyhelp classification of A beta as positive or negative.

关键词： Image quality Image synthesis Magnetic resonance imaging biological system modeling Diffusion models Feature extraction Hazards Medical diagnosis Diseases Dementia A beta-PET imputation Alzheimer's disease Diffusion model

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