Information Fusion最新文献_第10页

Natural language processing in finance: A survey 金融领域的自然语言处理：调查

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102755

Kelvin Du , Yazhi Zhao , Rui Mao , Frank Xing , Erik Cambria

{"title":"Natural language processing in finance: A survey","authors":"Kelvin Du , Yazhi Zhao , Rui Mao , Frank Xing , Erik Cambria","doi":"10.1016/j.inffus.2024.102755","DOIUrl":"10.1016/j.inffus.2024.102755","url":null,"abstract":"<div><div>This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) Environmental, Social, Governance (ESG) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102755"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Mid-Net: Rethinking efficient network architectures for small-sample vascular segmentation 中网：重新思考用于小样本血管分割的高效网络架构

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102777

Dongxin Zhao , Jianhua Liu , Peng Geng , Jiaxin Yang , Ziqian Zhang , Yin Zhang

{"title":"Mid-Net: Rethinking efficient network architectures for small-sample vascular segmentation","authors":"Dongxin Zhao , Jianhua Liu , Peng Geng , Jiaxin Yang , Ziqian Zhang , Yin Zhang","doi":"10.1016/j.inffus.2024.102777","DOIUrl":"10.1016/j.inffus.2024.102777","url":null,"abstract":"<div><div>Deep learning-based medical image segmentation methods have demonstrated significant clinical applications. However, training these methods on small-sample vascular datasets remains challenging due to the scarcity of labeled data and severe category imbalance. To address this, this paper proposes Mid-Net, which fully exploits the often-overlooked feature representation potential of the middle-layer network through cross-layer guidance to improve model learning efficiency in data-constrained environments. Mid-Net consists of three core components: the encoding path, the guidance path, and the calibration path. In the encoding path, a feature pyramid structure with large kernel convolutions is used to extract semantic information at different scales. The guidance path combines the sensitivity of the shallow-layer network to spatial details with the global perceptual abilities of the deep-layer network to provide more discriminative guidance to the middle-layer network in a feature-decoupled manner. The calibration path further calibrates the spatial location information of the middle-layer network through end-to-end supervised learning. Experiments conducted on the publicly available retinal vascular datasets DRIVE, STARE, and CHASE_DB1, as well as coronary angiography datasets DCA1 and CHUAC, demonstrate that Mid-Net achieves superior segmentation results with lower computational resource requirements compared to state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102777"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Alignable kernel network 可对齐的内核网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102758

Ying Xie , Jixiang Wang , Zhiqiang Xu , Junnan Shen , Lijie Wen , Rongbin Xu , Hang Xu , Yun Yang

{"title":"Alignable kernel network","authors":"Ying Xie , Jixiang Wang , Zhiqiang Xu , Junnan Shen , Lijie Wen , Rongbin Xu , Hang Xu , Yun Yang","doi":"10.1016/j.inffus.2024.102758","DOIUrl":"10.1016/j.inffus.2024.102758","url":null,"abstract":"<div><div>To enhance the adaptability and performance of Convolutional Neural Networks (CNN), we present an adaptable mechanism called Alignable Kernel (AliK) unit, which dynamically adjusts the receptive field (RF) dimensions of a model in response to varying stimuli. The branches of AliK unit are integrated through a novel align transformation softmax attention, incorporating prior knowledge through rank ordering constraints. The attention weightings across the branches establish the effective RF scales, leveraged by neurons in the fusion layer. This mechanism is inspired by neuroscientific observations indicating that the RF dimensions of neurons in the visual cortex vary with the stimulus, a feature often overlooked in CNN architectures. By aggregating successive AliK ensembles, we develop a deep network architecture named the Alignable Kernel Network (AliKNet). AliKNet with interdisciplinary design improves the network’s performance and interpretability by taking direct inspiration from the structure and function of human neural systems, especially the visual cortex. Empirical evaluations in the domains of image classification and semantic segmentation have demonstrated that AliKNet excels over numerous state-of-the-art architectures, achieving this without increasing model complexity. Furthermore, we demonstrate that AliKNet can identify target objects across various scales, confirming their ability to dynamically adapt their RF sizes in response to the input data.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102758"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Polyp-Mamba: A Hybrid Multi-Frequency Perception Gated Selection Network for polyp segmentation 息肉-曼巴：用于息肉分割的混合多频感知门控选择网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102759

Xingguo Zhu , Wei Wang , Chen Zhang , Haifeng Wang

{"title":"Polyp-Mamba: A Hybrid Multi-Frequency Perception Gated Selection Network for polyp segmentation","authors":"Xingguo Zhu , Wei Wang , Chen Zhang , Haifeng Wang","doi":"10.1016/j.inffus.2024.102759","DOIUrl":"10.1016/j.inffus.2024.102759","url":null,"abstract":"<div><div>Accurate segmentation of polyps in the colorectal region is crucial for medical diagnosis and the localization of polyp areas. However, challenges arise from blurred boundaries due to the similarity between polyp edges and surrounding tissues, variable polyp morphology, and speckle noise. To address these challenges, we propose a Hybrid Multi-Frequency Perception Gated Selection Network (Polyp-Mamba) for precise polyp segmentation. First, we design a dual multi-frequency fusion encoder that employs Mamba and ResNet to quickly and effectively learn global and local features in polyp images. Specifically, we incorporate a novel Hybrid Multi-Frequency Fusion Module (HMFM) within the encoder, using discrete cosine transform to analyze features from multiple spectral perspectives. This approach mitigates the issue of blurred polyp boundaries caused by their similarity to surrounding tissues, effectively integrating local and global features. Additionally, we construct a Gated Selection Decoder to suppress irrelevant feature regions in the encoder and introduce deep supervision to guide decoder features to align closely with the labels. We conduct extensive experiments using five commonly used polyp test datasets. Comparisons with 14 state-of-the-art segmentation methods demonstrate that our approach surpasses traditional methods in sensitivity to different polyp images, robustness to variations in polyp size and shape, speckle noise, and distribution similarity between surrounding tissues and polyps. Overall, our method achieves superior mDice scores on five polyp test datasets compared to state-of-the-art methods, indicating better performance in polyp segmentation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102759"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Divide and augment: Supervised domain adaptation via sample-wise feature fusion 分而治之：通过样本特征融合实现有监督的领域适应

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102757

Zhuangzhuang Chen , Bin Pu , Lei Zhao , Jie He , Pengchen Liang

{"title":"Divide and augment: Supervised domain adaptation via sample-wise feature fusion","authors":"Zhuangzhuang Chen , Bin Pu , Lei Zhao , Jie He , Pengchen Liang","doi":"10.1016/j.inffus.2024.102757","DOIUrl":"10.1016/j.inffus.2024.102757","url":null,"abstract":"<div><div>The training of deep models relies on appropriate regularization from a copious amount of labeled data. And yet, obtaining a large and well-annotated dataset is costly. Thus, supervised domain adaptation (SDA) becomes attractive, especially when it aims to regularize these networks for a data-scarce target domain by exploiting an available data-rich source domain. Different from previous methods focusing on an cumbersome adversarial learning manner, we assume that a source or target sample in the feature space can be regarded as a combination of (1) domain-oriented features (i.e., those reflecting the difference among domains) and (2) class-specific features (i.e., those inherently defining a specific class). By exploiting this, we present Divide and Augment (DivAug), a feature fusion-based data augmentation framework that performs target domain augmentation by transforming source samples into the target domain in an energy-efficient manner. Specifically, with a novel <em>semantic inconsistency loss</em> based on a multi-task ensemble learning scheme, DivAug enforces two encoders to learn the decomposed domain-oriented and class-specific features, respectively. Furthermore, we propose a simple sample-wise feature fusion rule that transforms source samples into target domain by combining class-specific features from a source sample and domain-oriented features from a target sample. Extensive experiments demonstrate that our method outperforms the current state-of-the-art methods across various datasets in SDA settings.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102757"},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises IFNet：数据驱动的多传感器估计融合与传感器测量噪声中的未知相关性

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-24 DOI: 10.1016/j.inffus.2024.102750

Ming Wang, Haiqi Liu, Hanning Tang, Mei Zhang, Xiaojing Shen

{"title":"IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises","authors":"Ming Wang, Haiqi Liu, Hanning Tang, Mei Zhang, Xiaojing Shen","doi":"10.1016/j.inffus.2024.102750","DOIUrl":"10.1016/j.inffus.2024.102750","url":null,"abstract":"<div><div>In recent years, multisensor fusion for state estimation has gained considerable attention. The effectiveness of the optimal fusion estimation method heavily relies on the correlation among sensor measurement noises. To enhance estimate fusion performance by mining unknown correlation in the data, this paper introduces a novel multisensor fusion approach using an information filtering neural network (IFNet) for discrete-time nonlinear state space models with cross-correlated measurement noises. The method presents three notable advantages: First, it offers a data-driven perspective to tackle uncertain correlation in multisensor estimate fusion while preserving the interpretability of the information filtering. Second, by harnessing the RNN’s capability to manage data streams, it can dynamically update the fusion weights between sensors to improve fusion accuracy. Third, this method has a lower complexity than the state-of-the-art KalmanNet measurement fusion method when dealing with the fusion problem involving a large number of sensors. Numerical simulations demonstrate that IFNet exhibits better fusion accuracy than traditional filtering methods and KalmanNet fusion filtering when correlation among measurement noises is unknown.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102750"},"PeriodicalIF":14.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Efficient audio–visual information fusion using encoding pace synchronization for Audio–Visual Speech Separation 利用编码同步实现高效视听信息融合，实现视听语音分离

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102749

Xinmeng Xu , Weiping Tu , Yuhong Yang

{"title":"Efficient audio–visual information fusion using encoding pace synchronization for Audio–Visual Speech Separation","authors":"Xinmeng Xu , Weiping Tu , Yuhong Yang","doi":"10.1016/j.inffus.2024.102749","DOIUrl":"10.1016/j.inffus.2024.102749","url":null,"abstract":"<div><div>Contemporary audio–visual speech separation (AVSS) models typically use encoders that merge audio and visual representations by concatenating them at a specific layer. This approach assumes that both modalities progress at the same pace and that information is adequately encoded at the chosen fusion layer. However, this assumption is often flawed due to inherent differences between the audio and visual modalities. In particular, the audio modality, being more directly tied to the final output (i.e., denoised speech), tends to converge faster than the visual modality. This discrepancy creates a persistent challenge in selecting the appropriate layer for fusion. To address this, we propose the Encoding Pace Synchronization Network (EPS-Net) for AVSS. EPS-Net allows for the independent encoding of the two modalities, enabling each to be processed at its own pace. At the same time, it establishes communication between the audio and visual modalities at corresponding encoding layers, progressively synchronizing their encoding speeds. This approach facilitates the gradual fusion of information while preserving the unique characteristics of each modality. The effectiveness of the proposed method has been validated through extensive experiments on the LRS2, LRS3, and VoxCeleb2 datasets, demonstrating superior performance over state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102749"},"PeriodicalIF":14.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

DSEM-NeRF: Multimodal feature fusion and global–local attention for enhanced 3D scene reconstruction DSEM-NeRF：多模态特征融合和全局-局部注意力用于增强型三维场景重建

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102752

Dong Liu , Zhiyong Wang , Peiyuan Chen

引用次数: 0

Euclidean and Poincaré space ensemble Xgboost 欧几里得和庞加莱空间集合 Xgboost

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102746

Ponnuthurai Nagaratnam Suganthan , Lingping Kong , Václav Snášel , Varun Ojha , Hussein Ahmed Hussein Zaky Aly

{"title":"Euclidean and Poincaré space ensemble Xgboost","authors":"Ponnuthurai Nagaratnam Suganthan , Lingping Kong , Václav Snášel , Varun Ojha , Hussein Ahmed Hussein Zaky Aly","doi":"10.1016/j.inffus.2024.102746","DOIUrl":"10.1016/j.inffus.2024.102746","url":null,"abstract":"<div><div>The Hyperbolic space has garnered attention for its unique properties and efficient representation of hierarchical structures. Recent studies have explored hyperbolic alternatives to hyperplane-based classifiers, such as logistic regression and support vector machines. Hyperbolic methods have even been fused into random forests by constructing data splits with horosphere, which proved effective for hyperbolic datasets. However, the existing incorporation of the horosphere leads to substantial computation time, diverting attention from its application on most datasets. Against this backdrop, we introduce an extension of Xgboost, a renowned machine learning (ML) algorithm to hyperbolic space, denoted as PXgboost. This extension involves a redefinition of the node split concept using the Riemannian gradient and Riemannian Hessian. Our findings unveil the promising performance of PXgboost compared to the algorithms in the literature through comprehensive experiments conducted on 64 datasets from the UCI ML repository and 8 datasets from WordNet by fusing both their Euclidean and hyperbolic-transformed (hyperbolic UCI) representations. Furthermore, our findings suggest that the Euclidean metric-based classifier performs well even on hyperbolic data. Building upon the above finding, we propose a space fusion classifier called, EPboost. It harmonizes data processing across various spaces and integrates probability outcomes for predictive analysis. In our comparative analysis involving 19 algorithms on the UCI dataset, our EPboost outperforms others in most cases, underscoring its efficacy and potential significance in diverse ML applications. This research marks a step forward in harnessing hyperbolic geometry for ML tasks and showcases its potential to enhance algorithmic efficacy.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102746"},"PeriodicalIF":14.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution 具有多排序关系的超图卷积网络，用于跨文档事件核心参照解析

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2024-10-22 DOI: 10.1016/j.inffus.2024.102769

Wenbin Zhao , Yuhang Zhang , Di Wu , Feng Wu , Neha Jain

{"title":"Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution","authors":"Wenbin Zhao , Yuhang Zhang , Di Wu , Feng Wu , Neha Jain","doi":"10.1016/j.inffus.2024.102769","DOIUrl":"10.1016/j.inffus.2024.102769","url":null,"abstract":"<div><div>Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of documents. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102769"},"PeriodicalIF":14.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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