检索结果-内蒙古大学图书馆

A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

computers, Materials & Continua 2024年第11期81卷 3351-3369页

作者： Qi Wang Chenxin Li Chichen Lin Weijian Fan Shuang Feng Yuanzhong Wang School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China School of Data Science and Intelligent Media Communication University of ChinaBeijing100024China Beijing 797 Audio Co. Ltd.Beijing100016China

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news *** previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different *** paper proposes a novel news media bias and factuality profiling framework assisted by correlated *** framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling *** approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and *** method improves the effectiveness of news media *** results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other *** paper further analyzes and discusses based on the experimental results.

关键词： News media profiling factuality bias correlated features

来源：评论

学校读者我要写书评

暂无评论

Legal Judgement Prediction via Contrastive Learning Based-Retrieval and Semantic Embedding

Legal Judgement Prediction via Contrastive Learning Based-Re...

引用

2024 IEEE International Symposium on Product Compliance Engineering-Asia, ISPCE-AS 2024

作者： Wei, Chaoyu Zhang, Qixuan Xu, Juan Wang, Yanling Huang, Zhenhua Hao, Tianyong School of Computer Science South China Normal University GuangZhou China Big Data Rule of Law Research Institute Nanjing University of Information Science & Technology Nanjing China School of Law South China Normal University GuangZhou China

ISBN: (纸本)9798331523008

Legal judgment prediction is a significant task in Legal Artificial Intelligence, aiming to predict judgment results based on fact descriptions automatically. The judgment output may include law articles, charges, prison terms, or combinations. Relevant legal cases with similar backgrounds or events should likewise be considered essential references for judgment, as they play an indispensable role in evaluating the prediction task. However, when applied to Chinese legal data, current methodologies often neglect the specialized preprocessing required for unique semantic attributes of Chinese, which affects their prediction performance. This paper proposes a novel contrastive learning- based model for legal judgment prediction named CLR-LJP to improve prediction performance. According to the legal judgment process in real life, law article prediction is converted as a multi- label classification task that includes both relative law cases and law articles. The CLR-LJP firstly converts fact descriptions, law articles, and charges into vectors by embedding Chinese semantics. Then, contrastive learning is introduced in retrieval training to distinguish the similar content of different label definitions. Finally, the role embedding method is applied to learn the interactions among multiple law articles to predict prison terms. Experiment results indicate that our CLR-LJP model achieves the best performance compared to the state-of-the-art models, especially in the task of prison term prediction. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Method for Object Extraction from Crop Image Based on Visual Saliency 2

A Method for Object Extraction from Crop Image Based on Visu...

引用

2nd International Conference on computer, Big data and Artificial Intelligence, ICCBDAI 2021

作者： Wang, Yuting Liao, Zhouyu School of Big Data and Computer Science Hechi University Yizhou Hechi Guangxi China

In order to solve the problem of accurate extraction of crop targets in natural background, an image target extraction method based on visual saliency is proposed. Firstly, an image is divided into multiple super pixels. Secondly, the significance detection method of ‘background first’ is used to calculate the significance of the whole image. Thirdly, the optimal data source is generated according to the characteristics of the high saliency region. Finally, the optimal data source is divided into target region and background region, and the crop target is extracted from the original image. Experimental results on a large number of crop images show that this method can extract targets accurately and completely. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

引用

Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

来源：评论

学校读者我要写书评

暂无评论

CREAM: Named Entity Recognition with Concise query and REgion-Aware Minimization 24th

CREAM: Named Entity Recognition with Concise query and REg...

引用

24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Yao, Xun Yang, Qihang Hu, Xinrong Yang, Jie Guo, Yi School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

ISBN: (纸本)9789819972531

Recent advancements in Machine Reading Comprehension (MRC) models have sparked interest in the field of Named Entity Recognition (NER), where entities are extracted as answers of given queries. Yet, existing MRC-based models face several challenges, including high computational costs, limited consideration of entity content information, and the tendency to generate sharp boundaries, that hinder their generalizability. To alleviate these issues, this paper introduces CREAM, an enhanced model leveraging Concise query and REgion-Aware Minimization. First, we propose a simple yet effective strategy of generating concise queries based primarily on entity categories. Second, we propose to go beyond existing methods by identifying entire entities, instead of just their boundaries (start and end positions), with an efficient continuous cross-entropy loss. An in-depth analysis is further provided to reveal their benefit. The proposed method is evaluated on six well-known NER benchmarks. Experimental results demonstrate its remarkable effectiveness by surpassing the current state-of-the-art models, with the substantial averaged improvement of 2.74, 1.12, and 2.38 absolute percentage points in Precision, Recall, and F1 metrics, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine Reading Comprehension Named Entity Recognition Query Optimization Region-Aware Loss

来源：评论

学校读者我要写书评

暂无评论

Instance by instance: An iterative framework for multi-instance 3d registration

引用

IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1117-1128页

作者： Yang, Jiaqi Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Quan, Siwen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Shenzhen China

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. The source codes are availableonline at https://***/caoxy01/IBI. © 2014 Chinese Association of Automation.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Acyclic coloring of two operations of graphs 8

Acyclic coloring of two operations of graphs

引用

2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Jin, Xinze Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The coloring theory of graphs is a very important direction in graph theory. The graph coloring problem has a strong application background. Many practical problems such as timetabling problems, frequency allocation problems, traffic arrangements, circuit design, and storage problems can be transformed into graph coloring problems. A substantial amount of scholarly work has been dedicated to the exploration of acyclic coloring, resulting in a plethora of significant findings that significantly contribute to the theoretical framework of vertex coloring in graphs. An acyclic coloring of a graph G refers to a proper vertex coloring where the subgraph resulting from any pair of color classes does not encompass any cycles. We analyze acyclic coloring in splitting graphs and Cartesian products of paths and cycles by using the method of structural coloring, reductive proof, and mathematical induction. Moreover, we present the precise acyclic chromatic numbers for splitting graphs of cycles and Cartesian products of paths and cycles. © Published under licence by IOP Publishing Ltd.

关键词： Integrated circuit design

来源：评论

学校读者我要写书评

暂无评论

Automated Adversarial-Attack Removal with SafetyNet Using ADGIT

Automated Adversarial-Attack Removal with SafetyNet Using AD...

引用

International Conference on Smart Computing & Communications (ICSCC)

作者： Steven Halim Arvio Anandi Vyos Devin Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step.

关键词： Accuracy Perturbation methods Detectors computer architecture Robustness data models Security Artificial intelligence Image reconstruction Image classification

来源：评论

学校读者我要写书评

暂无评论

Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models

arXiv

引用

arXiv 2025年

作者： Ren, Qingyu Zeng, Jie He, Qianyu Liang, Jiaqing Xiao, Yanghua Zhou, Weikang Sun, Zeye Yu, Fei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China Ant Group China

It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints. However, it is an unexplored area to enhance LLMs’ ability to follow soft constraints. To bridge the gap, we initially design a pipeline to construct datasets with high-quality outputs automatically. Additionally, to fully utilize the positive and negative samples generated during the data construction process, we choose Direct Preference Optimization (DPO) as the training method. Furthermore, taking into account the difficulty of soft constraints indicated by the number of constraints, we design a curriculum learning training paradigm based on the constraint quantity. We experimentally evaluate the effectiveness of our methods in improving LLMs’ soft constraint following ability and analyze the factors driving the improvements. The datasets and code are publicly available at https://***/Rainierrq/FollowSoftConstraint. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：