IEEE Transactions on Big Data最新文献_第3页

NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models NetPrompt：神经网络提示增强大型语言模型中的事件提取

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-03-17 DOI: 10.1109/TBDATA.2025.3552333

Lin Mu;Yide Cheng;Jun Shen;Yiwen Zhang;Hong Zhong

{"title":"NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models","authors":"Lin Mu;Yide Cheng;Jun Shen;Yiwen Zhang;Hong Zhong","doi":"10.1109/TBDATA.2025.3552333","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3552333","url":null,"abstract":"Event Extraction involves extracting event-related information such as event types and event arguments from context, which has long been tackled through well-designed neural networks or fine-tuned pre-trained language models. These approaches require substantial annotated data for tuning parameters and are resource-intensive. Recently, Prompting strategies with frozen parameters, such as Chain-of-Thought and Self-Consistency, have delivered success in NLP using LLMs by generating intermediate thought steps. However, they suffer from the challenge of error propagation and lack of interaction between different thoughts. In this paper, we propose <italic>Neural Network-based Prompting (NetPrompt), a novel network-structured prompting strategy for event extraction. The core idea behind NetPrompt is to imitate the excellent information integration capabilities of neural network structures. Specifically, we first decompose the event extraction problem into diverse intermediate subtasks, and each subtask is represented as a node in different layers of the network, the output of the nodes in the preceding layer is fed into the subsequent layer. Secondly, we propose pruning strategies to adapt the reasoning overhead to different problems. Finally, we have conducted extensive experiments on two widely used event extraction benchmarks to evaluate NetPrompt. The results demonstrated that NetPrompt significantly improved the event extraction performance compared to previous methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 5","pages":"2628-2642"},"PeriodicalIF":5.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward High-Quality Spatiotemporal Recommendation: Trajectory Recovery Based on Spatial and Temporal Dependencies 迈向高质量时空推荐：基于时空依赖的轨迹恢复

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-03-14 DOI: 10.1109/TBDATA.2025.3570071

Yihao Zhao;Chenhao Wang;Hongyu Wang;Shunzhi Zhu;Lisi Chen

{"title":"Toward High-Quality Spatiotemporal Recommendation: Trajectory Recovery Based on Spatial and Temporal Dependencies","authors":"Yihao Zhao;Chenhao Wang;Hongyu Wang;Shunzhi Zhu;Lisi Chen","doi":"10.1109/TBDATA.2025.3570071","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3570071","url":null,"abstract":"The rapid advancement of location and information technologies has generated a significant volume of human mobility data, which has been extensively utilized in spatiotemporal recommendation systems, including personalized point-of-interest recommendation, route recommendation, and location-aware event recommendation. Achieving high-quality recommendation results necessitates excellent quality of input trajectory data. However, trajectories obtained from GPS-enabled devices often contain missing and erroneous data that is unevenly distributed over time and highly sparse, which significantly hampers the effectiveness spatiotemporal data analytics. Therefore, trajectory recovery plays an important role in spatiotemporal recommendation systems. The objective of trajectory recovery is to utilize historical trajectories to restore missing locations, providing high-quality data for spatiotemporal recommendation systems. The development of an effective trajectory recovery mechanism faces three major challenges: 1) Complex and multi-granularity transition patterns among different locations; 2) Difficulty in discovering spatio-temporal dependencies; and 3) Data sparsity and noise. To address these challenges, we propose an attentional model with spatio-temporal recurrent neural networks, ARMove, to recover human mobility from long and sparse trajectories. In ARMove, we first design a spatio-temporal weighted recurrent neural network to capture users’ long-term preferences. Next, we introduce a multi-granularity trajectory encoder to model complex transition patterns and multi-level periodicity of human mobility. An attention-based history aggregation module is proposed to leverage historical mobility information. Extensive evaluation results reveal that our model outperforms the state-of-the-art models, demonstrating its ability to reconstruct high-quality and fine-grained human mobility trajectories.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1628-1639"},"PeriodicalIF":7.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial Emerging Horizons: The Rise of Large Language Models and Cross-Modal Generative AI 新兴的视野：大语言模型和跨模态生成人工智能的兴起

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-03-14 DOI: 10.1109/TBDATA.2025.3537217

Guang Yang;Jing Zhang;Giorgos Papanastasiou;Ge Wang;Dacheng Tao

引用次数: 0

How to Decide Like Human? A Commonsense-Aware Hierarchical Framework for Knowledge Graph Reasoning 如何像人一样做决定？知识图推理的常识感知层次框架

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-20 DOI: 10.1109/TBDATA.2025.3544126

Yi Xia;Gang Zhou;Junyong Luo;Mingjing Lan;Ningbo Huang

{"title":"How to Decide Like Human? A Commonsense-Aware Hierarchical Framework for Knowledge Graph Reasoning","authors":"Yi Xia;Gang Zhou;Junyong Luo;Mingjing Lan;Ningbo Huang","doi":"10.1109/TBDATA.2025.3544126","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3544126","url":null,"abstract":"Reasoning over knowledge graphs has attracted considerable attention from researchers and is being widely applied to contribute question answering systems, recommender systems, and other information retrieval systems. However, existing reasoning methods tend to suffer from poor interpretability which is not consistent with human commonsense. The trustworthiness and reliability of the knowledge discover outcomes thus decreased as a result. Inspired by the process of human decision-making, we propose a commonsense-aware hierarchical framework called <italic>HDLH, which incorporates commonsense knowledge into hierarchical knowledge graph reasoning process with deep reinforcement learning. <italic>HDLH implements hierarchical reasoning process through exploration and exploitation sequentially by applying multi-agent reinforcement learning. Multiple agents in <italic>HDLH simulate the multi-level decision-making ability of humans, and reason hierarchically and reasonably to maintain its efficiency and interpretability. Moreover, commonsense knowledge is incorporated by means of the reward-shaping function, ultimately guiding the agent to reason more consistently with human perceptions and reduce the huge search space. We evaluated <italic>HDLH with various tasks on five real-world datasets. The experimental results reveal that <italic>HDLH achieves better performance compared with state-of-the-art baseline models.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 5","pages":"2545-2556"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LightST: A Simplifying Spatio-Temporal Graph Neural Network for Traffic Flow Forecasting LightST：一种用于交通流量预测的简化时空图神经网络

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-20 DOI: 10.1109/TBDATA.2025.3544131

Jie Hu;Taichuan Zheng;Lilan Peng;Fei Teng;Shengdong Du;Tianrui Li

{"title":"LightST: A Simplifying Spatio-Temporal Graph Neural Network for Traffic Flow Forecasting","authors":"Jie Hu;Taichuan Zheng;Lilan Peng;Fei Teng;Shengdong Du;Tianrui Li","doi":"10.1109/TBDATA.2025.3544131","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3544131","url":null,"abstract":"Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction. Recently, a paradigm that models spatio-temporal dependencies through graph neural networks and time series models has become one of the most promising methods to solve this problem. However, existing methods still have limitations due to ineffectively modeling dynamic spatial dependencies and high time and space complexity. To address these issues, we propose a simplifying and powerful general spatio-temporal traffic flow forecasting model called LightST. Specifically, LightST first embeds temporal covariates and spatial position information to enhance the spatio-temporal modeling capabilities. Then, stacked temporal linear layers are introduced to capture temporal dependencies efficiently. Finally,we propose a concise adaptive spatio-temporal embedding graph convolution method to extract implicit spatial dependencies over time via dynamic graph convolution with adaptive spatio-temporal embedding graph generation. Extensive experiment results on four public traffic flow datasets demonstrate the superiority of our LightST concerning computational efficiency and prediction performance.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 5","pages":"2517-2528"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Higher-Order Community Detection by Motif-Based Modularity Optimization 基于模序模块化优化的高阶社团检测

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-20 DOI: 10.1109/TBDATA.2025.3544129

Jing Xiao;Yu-Cheng Zou;Xiao-Ke Xu

引用次数: 0

Blockchain-Empowered Federated Learning: Benefits, Challenges, and Solutions 区块链授权的联邦学习：好处、挑战和解决方案

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-13 DOI: 10.1109/TBDATA.2025.3541560

Zeju Cai;Jianguo Chen;Yuting Fan;Zibin Zheng;Keqin Li

引用次数: 0

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task 探索权衡：统一的大型语言模型与高度特异性放射学NLI任务的局部微调模型

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-13 DOI: 10.1109/TBDATA.2025.3536928

Zihao Wu;Lu Zhang;Chao Cao;Xiaowei Yu;Zhengliang Liu;Lin Zhao;Yiwei Li;Haixing Dai;Chong Ma;Gang Li;Wei Liu;Quanzheng Li;Dinggang Shen;Xiang Li;Dajiang Zhu;Tianming Liu

{"title":"Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task","authors":"Zihao Wu;Lu Zhang;Chao Cao;Xiaowei Yu;Zhengliang Liu;Lin Zhao;Yiwei Li;Haixing Dai;Chong Ma;Gang Li;Wei Liu;Quanzheng Li;Dinggang Shen;Xiang Li;Dajiang Zhu;Tianming Liu","doi":"10.1109/TBDATA.2025.3536928","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536928","url":null,"abstract":"Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology natural language inference (NLI) task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4’s reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) ChatGPT and GPT-4 outperform other LLMs in the radiology NLI task and 2) other specifically fine-tuned Bert-based models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings not only demonstrate the feasibility and promise of constructing a generic model capable of addressing various tasks across different domains, but also highlight several key factors crucial for developing a unified model, particularly in a medical context, paving the way for future artificial general intelligence (AGI) systems. We release our code and data to the research community.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1027-1041"},"PeriodicalIF":7.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning 联邦学习中效率约束效用-隐私双目标优化的理论分析

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-13 DOI: 10.1109/TBDATA.2025.3534622

Hanlin Gu;Xinyuan Zhao;Gongxi Zhu;Yuxing Han;Yan Kang;Lixin Fan;Qiang Yang

{"title":"A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning","authors":"Hanlin Gu;Xinyuan Zhao;Gongxi Zhu;Yuxing Han;Yan Kang;Lixin Fan;Qiang Yang","doi":"10.1109/TBDATA.2025.3534622","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3534622","url":null,"abstract":"Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention. Differential privacy has emerged as a prevalent technique in FL, safeguarding the privacy of individual user data while impacting utility and training efficiency. Within Differential Privacy Federated Learning (DPFL), previous studies have primarily focused on the utility-privacy trade-off, neglecting training efficiency, which is crucial for timely completion. Moreover, differential privacy achieves privacy by introducing controlled randomness (noise) on selected clients in each communication round. Previous work has mainly examined the impact of noise level (<inline-formula><tex-math>$sigma$</tex-math></inline-formula>) and communication rounds (<inline-formula><tex-math>$T$</tex-math></inline-formula>) on the privacy-utility dynamic, overlooking other influential factors like the sample ratio (<inline-formula><tex-math>$q$</tex-math></inline-formula>, the proportion of selected clients). This paper systematically formulates an efficiency-constrained utility-privacy bi-objective optimization problem in DPFL, focusing on <inline-formula><tex-math>$sigma$</tex-math></inline-formula>, <inline-formula><tex-math>$T$</tex-math></inline-formula>, and <inline-formula><tex-math>$q$</tex-math></inline-formula>. We provide a comprehensive theoretical analysis, yielding analytical solutions for the Pareto front. Extensive empirical experiments verify the validity and efficacy of our analysis, offering valuable guidance for low-cost parameter design in DPFL.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 5","pages":"2503-2516"},"PeriodicalIF":5.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk-Constrained Reinforcement Learning With Augmented Lagrangian Multiplier for Portfolio Optimization 基于增广拉格朗日乘子的风险约束强化学习投资组合优化

IF 5.7 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-31 DOI: 10.1109/TBDATA.2025.3533905

Bayaraa Enkhsaikhan;Ohyun Jo

引用次数: 0