IEEE Transactions on Big Data最新文献_第2页

CHEAT: A Large-Scale Dataset for Detecting CHatGPT-writtEn AbsTracts CHEAT：用于检测CHatGPT-writtEn摘要的大规模数据集

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536929

Peipeng Yu;Jiahan Chen;Xuan Feng;Zhihua Xia

引用次数: 0

To Write or Not to Write as a Machine? That’s the Question 像机器一样写作还是不写作？这就是问题所在

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536938

Robiert Sepúlveda-Torres;Iván Martínez-Murillo;Estela Saquete;Elena Lloret;Manuel Palomar

{"title":"To Write or Not to Write as a Machine? That’s the Question","authors":"Robiert Sepúlveda-Torres;Iván Martínez-Murillo;Estela Saquete;Elena Lloret;Manuel Palomar","doi":"10.1109/TBDATA.2025.3536938","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536938","url":null,"abstract":"Considering the potential of tools such as ChatGPT or Gemini to generate texts in a similar way to a human would do, having reliable detectors of AI –AI-generated content (AIGC)– is vital to combat the misuse and the surrounding negative consequences of those tools. Most research on AIGC detection has focused on the English language, often overlooking other languages that also have tools capable of generating human-like texts, such is the case of the Spanish language. This paper proposes a novel multilingual and multi-task approach for detecting machine versus human-generated text. The first task classifies whether a text is written by a machine or by a human, which is the research objective of this paper. The second task consists in detect the language of the text. To evaluate the results of our approach, this study has framed the scope of the AuTexTification shared task and also we have collected a different dataset in Spanish. The experiments carried out in Spanish and English show that our approach is very competitive concerning the state of the art, as well as it can generalize better, thus being able to detect an AI-generated text in multiple domains.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1042-1053"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Terrain Scene Generation Using a Lightweight Vector Quantized Generative Adversarial Network 基于轻量级矢量量化生成对抗网络的地形场景生成

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536926

Yan Wang;Huiyu Zhou;Xinghui Dong

{"title":"Terrain Scene Generation Using a Lightweight Vector Quantized Generative Adversarial Network","authors":"Yan Wang;Huiyu Zhou;Xinghui Dong","doi":"10.1109/TBDATA.2025.3536926","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536926","url":null,"abstract":"Natural terrain scene images play important roles in the geographical research and application. However, it is challenging to collect a large set of terrain scene images. Recently, great progress has been made in image generation. Although impressive results can be achieved, the efficiency of the state-of-the-art methods, e.g., the Vector Quantized Generative Adversarial Network (VQGAN), is still dissatisfying. The VQGAN confronts two issues, i.e., high space complexity and heavy computational demand. To efficiently fulfill the terrain scene generation task, we first collect a Natural Terrain Scene Data Set (NTSD), which contains 36,672 images divided into 38 classes. Then we propose a Lightweight VQGAN (Lit-VQGAN), which uses the fewer parameters and has the lower computational complexity, compared with the VQGAN. A lightweight super-resolution network is further adopted, to speedily derive a high-resolution image from the image that the Lit-VQGAN generates. The Lit-VQGAN can be trained and tested on the NTSD. To our knowledge, either the NTSD or the Lit-VQGAN has not been exploited before.<sup>1</sup> Experimental results show that the Lit-VQGAN is more efficient and effective than the VQGAN for the image generation task. These promising results should be due to the lightweight yet effective networks that we design.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"988-1000"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949212","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

Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models 适应任何：使用文本到图像扩散模型跨域和类别定制任何图像分类器

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536933

Weijie Chen;Haoyu Wang;Shicai Yang;Lei Zhang;Wei Wei;Yanning Zhang;Luojun Lin;Di Xie;Yueting Zhuang

{"title":"Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models","authors":"Weijie Chen;Haoyu Wang;Shicai Yang;Lei Zhang;Wei Wei;Yanning Zhang;Luojun Lin;Di Xie;Yueting Zhuang","doi":"10.1109/TBDATA.2025.3536933","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536933","url":null,"abstract":"We study a novel problem in this paper, that is, if a modern text-to-image diffusion model can tailor any image classifier across domains and categories. Existing domain adaption works exploit both source and target data for domain alignment so as to transfer the knowledge from the labeled source data to the unlabeled target data. However, as the development of text-to-image diffusion models, we wonder if the high-fidelity synthetic data can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each image classification task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with labels derived from text prompts, and then leverage them as a bridge to dig out the knowledge from the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as any unlabeled target data. Extensive experiments validate the feasibility of this idea, which even surprisingly surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1013-1026"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949114","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

AugGPT: Leveraging ChatGPT for Text Data Augmentation AugGPT：利用ChatGPT进行文本数据增强

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536934

Haixing Dai;Zhengliang Liu;Wenxiong Liao;Xiaoke Huang;Yihan Cao;Zihao Wu;Lin Zhao;Shaochen Xu;Fang Zeng;Wei Liu;Ninghao Liu;Sheng Li;Dajiang Zhu;Hongmin Cai;Lichao Sun;Quanzheng Li;Dinggang Shen;Tianming Liu;Xiang Li

{"title":"AugGPT: Leveraging ChatGPT for Text Data Augmentation","authors":"Haixing Dai;Zhengliang Liu;Wenxiong Liao;Xiaoke Huang;Yihan Cao;Zihao Wu;Lin Zhao;Shaochen Xu;Fang Zeng;Wei Liu;Ninghao Liu;Sheng Li;Dajiang Zhu;Hongmin Cai;Lichao Sun;Quanzheng Li;Dinggang Shen;Tianming Liu;Xiang Li","doi":"10.1109/TBDATA.2025.3536934","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536934","url":null,"abstract":"Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning (FSL) scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely used strategy to mitigate such challenges is to perform data augmentation to better capture data invariance and increase the sample size. However, current text data augmentation methods either can’t ensure the correct labeling of the generated data (lacking faithfulness), or can’t ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models (LLM), especially the development of ChatGPT, we propose a text data augmentation approach based on ChatGPT (named ”AugGPT”). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on multiple few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"907-918"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949265","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

Expertise or Hallucination? A Comprehensive Evaluation of ChatGPT's Aptitude in Clinical Genetics 专业还是幻觉？ChatGPT在临床遗传学上的综合评价

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536939

Yingbo Zhang;Shumin Ren;Jiao Wang;Chaoying Zhan;Mengqiao He;Xingyun Liu;Rongrong Wu;Jing Zhao;Cong Wu;Chuanzhu Fan;Bairong Shen

{"title":"Expertise or Hallucination? A Comprehensive Evaluation of ChatGPT's Aptitude in Clinical Genetics","authors":"Yingbo Zhang;Shumin Ren;Jiao Wang;Chaoying Zhan;Mengqiao He;Xingyun Liu;Rongrong Wu;Jing Zhao;Cong Wu;Chuanzhu Fan;Bairong Shen","doi":"10.1109/TBDATA.2025.3536939","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536939","url":null,"abstract":"Whether viewed as an expert or as a source of ‘knowledge hallucination’, the use of ChatGPT in medical practice has stirred ongoing debate. This study sought to evaluate ChatGPT's capabilities in the field of clinical genetics, focusing on tasks such as ‘Clinical genetics exams’, ‘Associations between genetic diseases and pathogenic genes’, and ‘Limitations and trends in clinical genetics’. Results indicated that ChatGPT performed exceptionally well in question-answering tasks, particularly in clinical genetics exams and diagnosing single-gene diseases. It also effectively outlined the current limitations and prospective trends in clinical genetics. However, ChatGPT struggled to provide comprehensive answers regarding multi-gene or epigenetic diseases, particularly with respect to genetic variations or chromosomal abnormalities. In terms of systematic summarization and inference, some randomness was evident in ChatGPT's responses. In summary, while ChatGPT possesses a foundational understanding of general knowledge in clinical genetics due to hyperparameter learning, it encounters significant challenges when delving into specialized knowledge and navigating the complexities of clinical genetics, particularly in mitigating ‘Knowledge Hallucination’. To optimize its performance and depth of expertise in clinical genetics, integration with specialized knowledge databases and knowledge graphs is imperative.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"919-932"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949097","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 Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models 用于比较专业深度学习和通用大型语言模型的多模态评估框架

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536937

Mohammad Nadeem;Shahab Saquib Sohail;Dag Øivind Madsen;Ahmed Ibrahim Alzahrani;Javier Del Ser;Khan Muhammad

{"title":"A Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models","authors":"Mohammad Nadeem;Shahab Saquib Sohail;Dag Øivind Madsen;Ahmed Ibrahim Alzahrani;Javier Del Ser;Khan Muhammad","doi":"10.1109/TBDATA.2025.3536937","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536937","url":null,"abstract":"Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT-4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and fake news detection often require finely tuned models, posing a comparison challenge with specialized Deep Learning (DL). We propose an assessment framework to compare task-specific intelligence with general-purpose LLMs on suicide and depression tendency identification. For this purpose, we trained two DL models on a suicide and depression detection dataset, followed by testing their performance on a test set. Afterward, the same test dataset is used to evaluate the performance of four LLMs (GPT-3.5, GPT-4, Google Bard, and MS Bing) using four classification metrics. The BERT-based DL model performed the best among all, with a testing accuracy of 94.61%, while GPT-4 was the runner-up with accuracy 92.5%. Results demonstrate that LLMs do not outperform the specialized DL models but are able to achieve comparable performance, making them a decent option for downstream tasks without specialized training. However, LLMs outperformed specialized models on the reduced dataset.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1001-1012"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949116","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

2024 Reviewers List* 2024审稿人名单*

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-15 DOI: 10.1109/TBDATA.2025.3526356

引用次数: 0

Advances in Robust Federated Learning: A Survey With Heterogeneity Considerations 鲁棒联邦学习的研究进展：考虑异质性的综述

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-08 DOI: 10.1109/TBDATA.2025.3527202

Chuan Chen;Tianchi Liao;Xiaojun Deng;Zihou Wu;Sheng Huang;Zibin Zheng

引用次数: 0

Big Data-Driven Advancements and Future Directions in Vehicle Perception Technologies: From Autonomous Driving to Modular Buses 车辆感知技术的大数据驱动进展与未来方向：从自动驾驶到模块化公交车

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-08 DOI: 10.1109/TBDATA.2025.3527208

Hongyi Lin;Yang Liu;Liang Wang;Xiaobo Qu

{"title":"Big Data-Driven Advancements and Future Directions in Vehicle Perception Technologies: From Autonomous Driving to Modular Buses","authors":"Hongyi Lin;Yang Liu;Liang Wang;Xiaobo Qu","doi":"10.1109/TBDATA.2025.3527208","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3527208","url":null,"abstract":"The rapid development of Big Data and artificial intelligence (AI) is revolutionizing the automotive and transportation industries, leading to the creation of the Autonomous Modular Bus (AMB). Designed to address the key challenges of modern public transportation systems, the AMB adopts a modular dynamic assembly approach. However, existing research on the AMB predominantly focuses on operational aspects, whereas in-transit docking remains the primary obstacle to its commercial deployment. This challenge stems from the fact that current perception accuracy in autonomous vehicles is limited to the decimeter level, with insufficient capability to manage adverse weather and complex traffic conditions. To enable AMBs to achieve full-scenario autonomous driving capabilities, this paper reviews current perception technologies from three perspectives: single-vehicle single-sensor perception, multi-sensor fusion perception, and cooperative perception. It examines the characteristics of existing perception solutions and evaluates their applicability to AMB-specific requirements. Furthermore, considering the unique challenges of in-transit docking, this paper identifies and proposes four future research directions for advancing AMB perception systems as well as general autonomous driving technologies.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1568-1587"},"PeriodicalIF":7.5,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949176","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