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arXiv 2025年

作者： Li, Yiming Yan, Kaiying Shao, Shuo Zhai, Tongqing Xia, Shu-Tao Qin, Zhan Tao, Dacheng College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Mathematics Sun Yat-sen University Guangdong Guangzhou510275 China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou310007 China Institute of Blockchain and Data Security Hangzhou310051 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Research Center of Artificial Intelligence Peng Cheng Laboratory Shenzhen518000 China

With the increasing adoption of deep learning in speaker verification, large-scale speech datasets have become valuable intellectual property. To audit and prevent the unauthorized usage of these valuable released datasets, especially in commercial or open-source scenarios, we propose a novel dataset ownership verification method. Our approach introduces a clustering-based backdoor watermark (CBW), enabling dataset owners to determine whether a suspicious third-party model has been trained on a protected dataset under a black-box setting. The CBW method consists of two key stages: dataset watermarking and ownership verification. During watermarking, we implant multiple trigger patterns in the dataset to make similar samples (measured by their feature similarities) close to the same trigger while dissimilar samples are near different triggers. This ensures that any model trained on the watermarked dataset exhibits specific misclassification behaviors when exposed to trigger-embedded inputs. To verify dataset ownership, we design a hypothesis-test-based framework that statistically evaluates whether a suspicious model exhibits the expected backdoor behavior. We conduct extensive experiments on benchmark datasets, verifying the effectiveness and robustness of our method against potential adaptive attacks. The code for reproducing main experiments is available at GitHub. © 2025, CC BY.

关键词： Deep learning

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SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection

SIDVis: Designing Visual Interactive System for Analyzing Su...

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International Conference on Information Visualisation (IV)

作者： Md Rafiqul Islam Md. Kowsar Hossain Sakib Anwaar Ulhaq Shanjita Akter Jianlong Zhou David Asirvathamt Business Information Systems Australian Institute of Higher Education Sydney Australia School of Computing Mathematics and Engineering Charles Sturt University Australia Manna Institute NSW Australia School of Computer Science Taylors University Malaysia Data Science Institute (DSI) University of Technology Sydney NSW Australia

Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID. This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualization system that improves performance and interpretability at the same time. The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.

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Sketch: A Toolkit for Streamlining LLM Operations

arXiv

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arXiv 2024年

作者： Jiang, Xin Li, Xiang Ma, Wenjia Fang, Xuezhi Yao, Yiqun Yu, Naitong Meng, Xuying Han, Peng Li, Jing Sun, Aixin Wang, Yequan Beijing Academy of Artificial Intelligence Beijing China AstralForge AI Lab Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Electronic Science and Technology of China Chengdu China Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore

Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks through a generative approach. However, the flexibility of their output format poses challenges in controlling and harnessing the model's outputs, thereby constraining the application of LLMs in various domains. In this work, we present Sketch, an innovative toolkit designed to streamline LLM operations across diverse fields. Sketch comprises the following components: (1) a suite of task description schemas and prompt templates encompassing various NLP tasks;(2) a user-friendly, interactive process for building structured output LLM services tailored to various NLP tasks;(3) an open-source dataset for output format control, along with tools for dataset construction;and (4) an open-source model based on LLaMA3-8B-Instruct that adeptly comprehends and adheres to output formatting instructions. We anticipate this initiative to bring considerable convenience to LLM users, achieving the goal of "plug-and-play" for various applications. The components of Sketch will be progressively open-sourced at https://***/cofe-ai/Sketch. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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A Survey on Fairness-aware Recommender Systems

arXiv

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arXiv 2023年

作者： Jin, Di Wang, Luzhi Zhang, He Zheng, Yizhen Ding, Weiping Xia, Feng Pan, Shirui College of Intelligence and Computing Tianjin University China Department of Data Science and AI Faculty of IT Monash University Australia School of Information Science and Technology Nantong University China School of Computing Technologies RMIT University Australia School of Information and Communication Technology Griffith University Australia

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems. © 2023, CC BY.

关键词： Recommender systems

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Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions

arXiv

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arXiv 2025年

作者： Xie, Guanwen Xu, Jingzehua Ding, Yimian Zhang, Zhi Zhang, Shuai Li, Yi Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Data Science New Jersey Institute of Technology NJ07102 United States

The adaptivity and maneuvering capabilities of Autonomous Underwater Vehicles (AUVs) have drawn significant attention in oceanic research, due to the unpredictable disturbances and strong coupling among the AUV’s degrees of freedom. In this paper, we developed large language model (LLM)-enhanced reinforcement learning (RL)-based adaptive S-surface controller for AUVs. Specifically, LLMs are introduced for the joint optimization of controller parameters and reward functions in RL training. Using multi-modal and structured explicit task feedback, LLMs enable joint adjustments, balance multiple objectives, and enhance task-oriented performance and adaptability. In the proposed controller, the RL policy focuses on upper-level tasks, outputting task-oriented high-level commands that the S-surface controller then converts into control signals, ensuring cancellation of nonlinear effects and unpredictable external disturbances in extreme sea conditions. Under extreme sea conditions involving complex terrain, waves, and currents, the proposed controller demonstrates superior performance and adaptability in high-level tasks such as underwater target tracking and data collection, outperforming traditional PID and SMC controllers. 3 Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

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Research on the Distribution Characteristics of Standardized Big data Resources Based on data Visualization - - Taking Standardization Practice in Shandong Province as an Example 9

Research on the Distribution Characteristics of Standardized...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Sun, Jinyang Peng, Dongdong Lu, Zhe Zhai, Mengran Qi, Ning Qi, Yuanhua SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics;(2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution";(3) the distribution of think-tank experts presents obvious "agglomeration effect". © 2024 IEEE.

关键词： data consistency

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Development of machine learning-based models to predict 10-year risk of cardiovascular disease:a prospective cohort study

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Stroke & Vascular Neurology 2023年第6期8卷 475-485页

作者： Jia You Yu Guo Ju-Jiao Kang Hui-Fu Wang Ming Yang Jian-Feng Feng Jin-Tai Yu Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence Department of NeurologyHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghaiChina Zhangjiang Fudan International Innovation Center Fudan UniversityShanghaiChina School of Data Science Fudan UniversityShanghaiChina Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal UniversityZhejiangChina Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center Fudan UniversityShanghaiChina

Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical *** study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction *** From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years *** implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident *** model was validated through a leave-one center-out *** During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of *** model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±***,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

关键词： cardiovascular prediction learning

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TO–YOLOX: a pure CNN tiny object detection model for remotesensing images

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International Journal of Digital Earth 2023年第1期16卷 3882-3904页

作者： Zhe Chen Yuan Liang Zhengbo Yu Ke Xu Qingyun Ji Xueqi Zhang Quanping Zhang Zijia Cui Ziqiong He Ruichun Chang Zhongchang Sun Keyan Xiao Huadong Guo School of Mathematics and Physics Chengdu University of TechnologyChengduPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Centre of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China Geomathematics Key Laboratory of Sichuan Province Chengdu University of TechnologyChengduPeople’s Republic of China Digital Hu Line Research Institute Chengdu University of TechnologyChengduPeople’s Republic of China School of Management Science Chengdu University of TechnologyChengduPeople’s Republic of China School of Earth Science and Resources China University of Geosciences(Beijing)BeijingPeople’s Republic of China MNR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijingPeople’s Republic of China

Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster ***,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult ***,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)***–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing ***–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing *** verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation.

关键词： Tiny object detection TO-YOLOX remote sensing image deep learning attentionmechanism

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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

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China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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RSVP for VPSA: A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis

arXiv

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arXiv 2024年

作者： Klaffenboeck, Manfred Gleicher, Michael Sorger, Johannes Wimmer, Michael Möller, Torsten The Institute of Visual Computing and Human Centered Technology TU Wien Austria The Complexity Science Hub Vienna Austria The Department of Computer Sciences The University of Wisconsin - Madison United States The Faculty of Computer Science The University of Vienna The Research Network Data Science Uni Wien Austria

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights. © 2024, CC BY.

关键词： Visualization

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