检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Yang, Guang Cao, Juan Sheng, Qiang Qi, Peng Li, Xirong Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors;2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://***/guang-yanng/DRAG. © 2022, CC BY.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

arXiv

引用

arXiv 2021年

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021, CC BY-SA.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

引用

arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

来源：评论

学校读者我要写书评

暂无评论

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

引用

arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Evolutionary dimensionality reduction for crack localization in ship structures using a hybrid computational intelligent approach

Evolutionary dimensionality reduction for crack localization...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Vassilios A. Kappatos George Georgoulas Chrysostomos D. Stylios Evangelos S. Dermatas Electrical Engineering Department University of Patras Rio-Patras Greece Department of Computer Applications in Finance and Management TEI of Ionian Islands Lefkas Greece Laboratory of Knowledge and Intelligent Computing Department of Informatics and Communications Technology TEI Epirus Artas Greece

Acoustic emission (AE) is one of the most important non-destructive testing (NDT) methods for materials and constructions. Using AE testing, the location of a single event (crack) can be classified efficiently into three typical areas in a ship hull. The problem is a typical classification problem based on the use of features extracted from piezo-sensors' signal. As in most classification problems, the extraction and selection of the most appropriate set of features plays a major role in the overall performance of the system. In this research work we investigate the use of an evolutionary algorithm to extract new features from a set of primitive features in a lower dimensional space through a linear transformation. These features are subsequently fed into a probabilistic neural network (PNN) that performs the classification. In simulation experiments, where a stiffened plate model (SPM) is partially sank into water, the localization rate in noisy environments outperforms a work, where a feature selection phase alone was used before the classification phase. The proposed hybrid computational intelligent approach shows the potential merit of using it in real life situations where the signal is distorted by noise.

关键词： Marine vehicles Computational intelligence intelligent structures Competitive intelligence Acoustic testing Feature extraction Working environment noise Acoustic emission Materials testing Nondestructive testing

来源：评论

学校读者我要写书评

暂无评论

Detection of articulation disorders using Empirical Mode Decomposition and neural networks

Detection of articulation disorders using Empirical Mode Dec...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： George Georgoulas Voula C. Georgopoulos George D. Stylios Chrysostomos D. Stylios Department of Computer Applications in Finance and Management TEI of Ionian Islands Lefkas Greece Department of Speech and Language Therapy TEI of Patras Patras Greece The Laboratory of Knowledge and Intelligent Computing Department of Informatics and Communications Technology TEI Epirus Kostakioi Artas Greece

This paper introduces a novel approach based on signal processing methods to extract features from speech signals and based on them to detect a specific type of articulation disorders. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. Empirical Mode Decomposition and the Hilbert Huang transform are applied to the speech signal in order to calculate the marginal spectrum of the signal. The marginal spectrum is subsequently subject to a mel-cepstrum like processing to extract features which are fed to a neural network classifier responsible for the identification of the articulation disorder. Our preliminary results suggest that this approach is very promising for the detection of the disorder under study.

关键词： Neural networks Signal processing Speech processing Pediatrics Computer errors Feature extraction Computer applications Finance Financial management Speech analysis

来源：评论

学校读者我要写书评

暂无评论

knowledge Energy in knowledge Flow Networks

Knowledge Energy in Knowledge Flow Networks

引用

International Conference on Semantics, knowledge and Grid (SKG)

作者： Hai Zhuge Weiyu Guo Xiang Li Lianhong Ding China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate School of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving cause of forming an autonomous knowledge flow network, and explores the behind principles. Knowing these principles can help team managers and the support systems improve cooperation by monitoring the knowledge energy of nodes, and by evaluating and adjusting knowledge flows. A knowledge flow network management system can be the high layer of the knowledge grid to help improve the efficiency of distributed knowledge-intensive teamwork.

关键词： knowledge management Teamwork intelligent networks Monitoring Energy management Information processing Computers IP networks Prototypes Routing

来源：评论

学校读者我要写书评

暂无评论

Aggregate experts knowledge in Fuzzy Cognitive Maps

Aggregate experts knowledge in Fuzzy Cognitive Maps

引用

IEEE International Conference on Fuzzy Systems

作者： Giovanni Mazzuto Maurizio Bevilacqua Chrysostomos Stylios Voula C. Georgopoulos Dipartimento Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche Ancona Italy Laboratory of Knowledge and Intelligent Computing (KIC) Educational Institute of Epirus Arta Greece School Health and Welfare Professions TEI of Western Greece Patras Greece

In the last years, increased attention has been paid to the use of experts' opinions as concerned with the decisionmaking process and problem solving. Several approaches for aggregating them have been analysed but the literature review has highlighted that the experts' credibility in many cases is neglected. Thus, the present study aims at developing a novel approach for Fuzzy Cognitive Maps modelling, described in the literature as the unique methodology able to aggregate individuals knowledge and beliefs, combining a group decisionmaking technique, such as the Aggregation of Individual Evaluations one, and a structured credibility evaluation method according to the expertise areas of each expert. In particular, using a numerical example, the comparison between the traditional outcomes and those of the proposed method, highlights how the second one allows to obtain results more concordant with the single experts judgements than the first one, criticized of smoothing the experts opinion excessively.

关键词： Mathematical model Decision making Fuzzy cognitive maps Analytical models Aggregates Linguistics Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Linear and non-linear features for intrapartum cardiotocography evaluation

Linear and non-linear features for intrapartum cardiotocogra...

引用

Computers in Cardiology (CinC)

作者： Václav Chudáček Jiří Spilka Michal Huptych George Georgoulas Lenka Lhotská Chrysostomos Stylios Michal Koucký Petr Janků Department of Cybernetics Czech Technical University Prague Czech Republic Laboratory of Knowledge and Intelligent Computing TEI Epirus Artas Greece Obstetrics and Gynaecology clinic Charles University Prague Czech Republic Obstetrics and Gynaecology clinic University Hospital Brno Czech Republic

Since 1960's obstetricians have been using cardiotocography (CTG) to detect possible ongoing hypoxia of the fetus. CTG consists of fetal heart rate (fHR) and uterine contraction (TOCO) monitoring. The evaluation of the fHR in clinical settings is ruled by FIGO guidelines, which are based on evaluation of macroscopic morphological features derived from the fHR, such as baseline variability. Although upgrades were proposed to the guidelines - none of them is taking into account results achieved by the adult heart rate variability research. In this work, almost complete set of features previously used for fHR description is investigated and the features are assessed based on their statistical significance in the task of distinguishing the records into three FIGO classes. Inter-correlation of the features is also discussed. We assess the features on a large data set and use expert signal evaluation instead of pH values with the aim to give an overall view of the potential usefulness of the features in the clinical settings. We conclude the paper by presenting the best uncorrected feature subset according to the meta-analysis of three different ranking methods.

关键词： Fetal heart rate Heart rate variability Artificial neural networks Pediatrics Monitoring Correlation Gynecology

来源：评论

学校读者我要写书评

暂无评论

Towards Holistic Analysis of Wireless Capsule Endoscopic Videos: A Taxonomy-Driven Machine Learning Framework for Clinically Comprehensive WCE Frame Level Analysis

Towards Holistic Analysis of Wireless Capsule Endoscopic Vid...

引用

Electrics and Computer (INTCEC), Interdisciplinary Conference on

作者： Haroon Wahab Rita Goel Maida Alamgir Irfan Mehmood Khan Muhammad Hassan Ugail Centre for Visual Computing & Intelligent Systems School of Computer Science AI and Electronics University of Bradford Bradford England UK Department of Applied Artificial Intelligence Visual Analytics Knowledge Laboratory (VIS2KNOW) Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Manual perusal of capsule endoscopic videos is laborious due to their length, redundancy, and poor quality frames, risking misdetection. Limited representation of diverse anomalies and subtle lesions led to less generalized approaches targeting only individual or few anomalous findings. To deploy automated analysis in clinical settings effectively, a holistic, end-to-end approach is crucial, distinguishing between frame-level analysis and disease-level diagnosis. This study proposes a holistic framework for frame-level analysis based on a taxonomy of findings, leveraging clinical consensus. Clinical knowledge implicit in the taxonomy can enhance Computer Aided Diagnostics (CAD) systems. Convolutional Neural Networks (CNNs) trained at appropriate granularity levels in the taxonomy show improved performance metrics. Validation on the Kvasir-capsule dataset demonstrates a 2-4% performance boost. Taxonomy-based inference scores effectively resolve mimicked anomalies without additional learning.

关键词： Training Wireless communication Measurement Taxonomy Redundancy Merging Manuals

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：