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arXiv 2024年

作者： Yu, Xinquan Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Fake detection

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TAMP-S2GCNETS: COUPLING TIME-AWARE MULTIPERSISTENCE KNOWLEDGE REPRESENTATION WITH SPATIO-SUPRA GRAPH CONVOLUTIONAL NETWORKS FOR TIME-SERIES FORECASTING 10

TAMP-S2GCNETS: COUPLING TIME-AWARE MULTIPERSISTENCE KNOWLEDG...

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10th International Conference on Learning Representations, ICLR 2022

作者： Chen, Yuzhou Segovia-Dominguez, Ignacio Coskunuzer, Baris Gel, Yulia R. Department of Electrical and Computer Engineering Princeton University United States Department of Mathematical Sciences University of Texas Dallas United States Lawrence Berkeley National Laboratory United States Jet Propulsion Laboratory Caltech United States National Science Foundation United States

Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex dependencies in multivariate spatio-temporal processes. However, most existing GNNs have inherently static architectures, and as a result, do not explicitly account for time dependencies of the encoded knowledge and are limited in their ability to simultaneously infer latent time-conditioned relations among entities. We postulate that such hidden time-conditioned properties may be captured by the tools of multipersistence, i.e., a emerging machinery in topological data analysis which allows us to quantify dynamics of the data shape along multiple geometric dimensions. We make the first step toward integrating the two rising research directions, that is, time-aware deep learning and multipersistence, and propose a new model, Time-Aware Multipersistence Spatio-Supra Graph Convolutional Network (TAMP-S2GCNets). We summarize inherent time-conditioned topological properties of the data as time-aware multipersistence Euler-Poincaré surface and prove its stability. We then construct a supragraph convolution module which simultaneously accounts for the extracted intra- and inter-dependencies in the data. Our extensive experiments on highway traffic flow, Ethereum token prices, and COVID-19 hospitalizations demonstrate that TAMP-S2GCNets outperforms the state-of-the-art tools in multivariate time series forecasting tasks. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Graph neural networks

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(θ,θ^∗)-Operator-Driven Intuitionistic Fuzzy Matrix Composite Operation and Its Optimal Application in Medical Diagnosis 11th

(θ,θ<sup>∗</sup>)-Operator-Driven Intuitionistic Fuzzy Ma...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Kang, Yuqi Feng, Huarong Zhang, Xianyong Yang, Jilin Liang, Qian School of Mathematical Sciences Sichuan Normal University Chengdu 610066 China National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Research Center of Sichuan Normal University Chengdu 610066 China College of Science Xihua University Chengdu 610039 China College of Computer Science Sichuan Normal University Chengdu 610066 China

ISBN: (纸本)9783031927430

Intuitionistic fuzzy sets (IFSs) extend fuzzy sets, and they provide a more accurate representation of uncertainty in realistic scenarios. Fuzzy implication operators play a crucial role in fuzzy reasoning. Accordingly, this paper introduces fuzzy implication operators θ and θ^∗ to IFSs, and it correspondingly proposes two types of intuitionistic fuzzy matrix composite operations, where the (θ, θ^∗) induction is comparatively accompanied with a direct formulation from (∧,∨). Firstly, we investigate concrete properties of fuzzy implication operators θ,θ^∗. Then, two IFSs matrix composite operations are proposed, respectively from (∧,∨) and (θ, θ^∗). Finally, the two composite operations of IFSs matrices are applied to medical diagnosis, and their application feasibility is verified through a practical medical example;by comparative analyses, the (θ,θ^∗)-driven operation better provides right diagnosis results in contrast to previous studies. This study resorts to specific fuzzy implication operators to enrich theoretical research and practical applications of IFSs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fuzzy implication operators Intuitionistic fuzzy sets Medical diagnosis Synthetic composite operation Three-way decision

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Parallel DGTD Algorithm with LTS Scheme for Electrical Large Problems Based on Supercomputer

Parallel DGTD Algorithm with LTS Scheme for Electrical Large...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Wenhao Liu Liang Chen Ping Li State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai Jiao Tong University Shanghai China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In this paper, we present a nodal discontinuous Galerkin time-domain (NDGTD) algorithm with a parallel scheme for solving transient Maxwell's equations. With the objective of analyzing the electromagnetic characteristics of electrical-large problems, the NDGTD algorithm is implemented on the supercomputer named “Tian- He” utilizing the message passing interface (MPI) for inter-thread communication. Additionally, to enhance time-marching efficiency in dealing with multi-scale problems, the local time stepping (L TS) technique is implemented based on the leapfrog integration scheme. To assess the efficiency and accuracy of the proposed parallel NDGTD algorithm, an investigation into the scattering properties of a stealthy aircraft model and the far-field characteristics of a patch antenna array is conducted.

关键词： Patch antennas Message passing Scattering Aerospace electronics Supercomputers Time-domain analysis Transient analysis

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Reasoning about Strategic Abilities in Stochastic Multi-agent Systems

arXiv

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arXiv 2024年

作者： Zhang, Yedi Song, Fu Chen, Taolue Wu, Xuzhi Department of Computing Science National University of Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom College of Computer Science and Software Engineering Shenzhen University China

Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of the alternating-time μ-calculus (AMC), named PAMC, for reasoning about the strategic abilities of agents in stochastic multi-agent systems. We show that PAMC subsumes two existing logics AMC and PμTL (a probabilistic extension of the modal μ-calculus), but is incomparable with the probabilistic alternating-time temporal logic (PATL). We study the problems of model checking and satisfiability checking for PAMC. We first give a model checking algorithm by leveraging algorithms for solving normal-form games and AMC model checking. We establish that the model checking problem of PAMC remains in UP∩co-UP, the same complexity class as the model checking problem for AMC and PμTL. We also provide a new reduction from the satisfiability problem of PAMC to solving parity games, by which we obtain an EXPTIME decision procedure, as well as the small model property which allows to construct a model for each satisfiable PAMC formula. Satisfiability in PAMC has the same complexity as in the modal μ-calculus, unlike PCTL and PATL whose satisfiability checking problems remain open. We have implemented both the model checking and satisfiability checking algorithms as open-source tools. Experimental results are reported, showcasing the practical applications and effectiveness of our approaches. © 2024, CC BY.

关键词： Stochastic systems

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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Energy-Efficient Federated Learning-enabled Digital Twin in UAV-aided Vehicular Networks

Energy-Efficient Federated Learning-enabled Digital Twin in ...

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Asia-Pacific Conference on Communications

作者： Giang H. Pham Hoang D. Le Thanh V. Pham Chuyen T. Nguyen Anh T. Pham Computer Communications Laboratory The University of Aizu Japan Department of Mathematical and Systems Engineering Shizuoka University Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Vietnam

Federated learning (FL)-enabled digital twin (DT) has recently attracted research attention to bring intelligent applications. However, enabling the FL-enabled DT in vehicular networks becomes challenging due to vehicle mobility’s impact on communication channels. In this regard, we propose to deploy an unmanned aerial vehicle (UAV) as a relay node to support the vehicular network. The objective is to minimize energy consumption under the trade-off with the latency and accuracy constraints of the DT model via a joint optimization of local accuracy, the local computation frequency, relay decision, and transmission power. To do so, we derive instantaneous formulas to update the accuracy and latency constraints, then solve the proposed problem using an iterative algorithm with convex optimization techniques. Numerical results show that the proposed dynamic optimization for UAV-aided vehicular networks can reduce up to 39.9% of consumption energy compared to conventional methods.

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Early Detection and Classification of Diabetic Retinopathy through Analysis of Retinal Medical Images Using Deep Learning

Early Detection and Classification of Diabetic Retinopathy t...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Youssef Siyah Khalid Minaoui Fatima Zahra El Biach Samir Saoudi Laboratory for research in computer science and telecommunications (LRIT) Faculty of Sciences in Rabat Mohammed V University in Rabat Morocco dept. Mathematical and Electrical Engineering STICC Laboratory UMR CNRS 6285 IMT-Atlantique in Brest France

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

In this paper, we propose a model based on deep learning (DL) frameworks for the detection and classification of diabetic retinopathy (DR), along with the assessment of its severity. DL algorithms have been applied to fundus photographs, which are images of the back of the eye, to automatically detect and classify signs of DR. These algorithms can analyze retinal images and identify anomalies, including microaneurysms, hemorrhages, exudates, as well as fluid accumulation in the macula, all of which serve as indicators of the presence of diabetic retinopathy. We utilized the APTOS 2019 dataset sourced from Kaggle, comprising high-resolution retinal images processed with Gaussian filters for diabetic retinopathy detection. Experiments were conducted on six different models, including Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19), Residual Network 50 (ResNet50), Inception Version 3 (InceptionV3), basic Convolutional Neural Networks (CNN), and Mobile Network Version 2 (MobileNetV2). The results obtained demonstrate high accuracy, suggesting that this model could contribute to expediting and enhancing the diabetic retinopathy diagnostic process. By automating the detection of anomalies in retinal images, the system proposes an intelligent approach that may help alleviate the workload on medical personnel.

关键词： Geometry Deep learning Diabetic retinopathy Visualization Retina Classification algorithms Convolutional neural networks

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Optimizing Different Methods of Fabricating 3D Printed UV Blocking Sheets for Solar Cells

Optimizing Different Methods of Fabricating 3D Printed UV Bl...

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Renewable Energy Technologies and Modern Communications Systems: Future and Challenges, Conference on

作者： Amjad Fawzi Alhindi Lujain Khalid Baawad Fahad Allam Nazek Elatab Rabab R. Bahabry Smart Advanced Memory Devices and Applications (SAMA) Laboratory Electrical and Computer Engineering Computer Electrical Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Materials Science and Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Physical Sciences University of Jeddah (UJ) College of Science Jeddah Saudi Arabia

ISBN: (数字)9798331519445

ISBN: (纸本)9798331519452

This study investigates the fabrication of innovative UV-blocking sheets that effectively transmit visible light while simultaneously obstructing harmful ultraviolet (UV) radiation, utilizing Cerium Oxide (CeO2) and Zinc Oxide (ZnO) nanoparticles as UV-blocking agents. Three distinct fabrication methods were explored: The Powder Method, where nanoparticles were incorporated directly into polydimethylsiloxane (PDMS); the Ball milling Mixture method, which involved mixing nanoparticles through ball milling; and the Solvent-Based Mixture method, where nanoparticles were dissolved, mixed, and dried; before incorporation into PDMS. Samples were produced using a 3D printing technique, which is Direct Ink Writing (DIW) and characterized for their optical properties via UV-Vis spectroscopy. The results indicated CeO2 demonstrated superior UV-blocking capabilities (a maximum blockage of ~99%) compared to ZnO, while maintaining high transmittance for visible light (a maximum of ~90%). Notably, the solvent-based method exhibited better results than the ball milling method; however, the powder method had the best results. Therefore, this investigation offers a promising solution for solar panel protection that aligns with Saudi Arabia's Vision 2030 energy objectives. The relentless exposure of devices, particularly solar cells, to harmful UV radiation poses a formidable challenge and leads to annual degradation rates of between -0.8% and -4.9%, besides other factors. These findings underscore the critical importance of developing effective UV-blocking solutions.

关键词： Nanoparticles Powders II-VI semiconductor materials Photovoltaic cells Ink Ball milling Writing Zinc oxide Three-dimensional printing Substrates

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A New Extension of the Topp–Leone-Family of Models with Applications to Real Data

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Annals of Data science 2023年第1期10卷 225-250页

作者： Muhammad, Mustapha Liu, Lixia Abba, Badamasi Muhammad, Isyaku Bouchane, Mouna Zhang, Hexin Musa, Sani Department of Mathematics Guangdong University of Petrochemical Technology Maoming525000 China School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China School of Mathematics and Statistics Central South University Changsha China Department of Mathematics Yusuf Maitama Sule University Kano Nigeria School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China Department of Mathematics and Computer Science Sule Lamido University Jigawa Kafin-Hausa Nigeria

In this article, we proposed a new extension of the Topp–Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress–strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp–Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice;the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp–Leone’s and exponential related distributions based on the real data illustrations. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Maximum likelihood estimation

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