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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zhongqiang Lu, Dongli Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798350368741

Fake news often spreads rapidly on social media in various forms, exposing users to a large amount of misinformation and disinformation. Recently, promising results have been achieved in fake news detection. However, most of the approaches only consider one-hop neighborhood information in cross-modal mapping relationships or focus mainly on pairwise interactions of multimodal fake news. However, the fine-grained feature mapping of multimodal fake news may go beyond binary relations. To address these issues, we propose a Multi-Hop Attention Diffusion Graph Neural Networks in this work to capture and complement the multivariate mapping relationships between fine-grained features, while extracting cross-modal higher-order alignment representations for multimodal fake news detection via cross-modal contrastive learning and modal matching. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in the multimodal fake news detection task. © 2025 IEEE.

关键词： attention diffusion fake news graph neural networks multivariate mapping relationships

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Puwei Sun, Zhouxing Li, Rui Chen, Jinchuan Gong, Ping Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China Renmin University of China School of Mathematics China Fujian Normal University College of Computer and Cyber Security China

ISBN: (纸本)9798350304855

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently. © 2023 IEEE.

关键词： Blockchain

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Context-Aware Semantic Type Identification for Relational Attributes

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Journal of Computer Science & Technology 2023年第4期38卷 927-946页

作者：丁玥郭雨荷卢卫李海翔张美慧李晖潘安群杜小勇 Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China Tencent(Beijing)Technology Company Limited Beijing 100080China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China Tencent(Shenzhen)Technology Company Limited Shenzhen 518057China

Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search in ***,due to a lack of unified naming standards across prevalent information systems(*** islands),AST identification still remains as an open *** tackle this problem,we propose a context-aware method to figure out the ASTs for relations in this *** transform the AST identification into a multi-class classification problem and propose a schema context aware(SCA)model to learn the representation from a collection of relations associated with attribute values and schema *** on the learned representation,we predict the AST for a given attribute from an underlying relation,wherein the predicted AST is mapped to one of the labeled *** improve the performance for AST identification,especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs,we then introduce knowledge base embeddings(***)to enhance the above representation and construct a schema context aware model with knowledge base enhanced(SCA-KB)to get a stable and robust *** experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin,up to 6.14%and 25.17%in terms of macro average F1 score,and up to 0.28%and 9.56%in terms of weighted F1 score over high-quality and low-quality datasets respectively.

关键词： attribute semantic type(AST)identification context-aware semantic embedding knowledge base embedding

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv

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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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Regulation of morphology and visible light-driven photocatalysis of WO_(3)nanostructures by changing pH

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Rare Metals 2021年第7期40卷 1738-1745页

作者： You-Shu Fan Xiao-Li Xi Yang-Si Liu Zuo-Ren Nie Lin-Yan Zhao Qing-Hua Zhang College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.

关键词： h-WO_(3) Morphology pH Photocatalysis Charge transfer Optoelectrical properties

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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A knowledge Graph Completion Model with Path-Specific Learning

A Knowledge Graph Completion Model with Path-Specific Learni...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiulin Zheng Zan Zhang Rui Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and also with the School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Inspur Academy of Science and Technology Shandong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

knowledge graphs (KGs) play an increasingly im-portant role in many knowledge-aware tasks. However, existing KGs are struggle with incompleteness, which motivates knowledge graph completion (KGC), that is, predicting the lost links between entities based on observed triples. Reasoning over relation paths in incomplete KGs is popular. Nonetheless, some significant issues are still remained to be addressed, such as path noise and ambiguity of inferred relation. To address these problems, we propose a novel path augmented _Reasoning model with avoidance of Path noise and Disambiguation of inferred relation in this paper, referred to as RPD. In this model, we calculate the sum of resource allocation for each relation path to measure its reliability to avoid the inference of path noise. To address the ambiguity of an inferred relation, we introduce position embedding to denote the relation position along the path when learning path representation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our proposal RPD model in the handling of KGC tasks compared to SOTAs.

关键词： Heavily-tailed distribution Noise knowledge graphs Benchmark testing Transformers Cognition Reliability Resource management Proposals Noise measurement

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