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  • Open Access


    Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

    Weijian Fan1,*, Ziwei Shi2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

    Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency and inconsistency… More >

  • Open Access


    Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

    Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754

    Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale and segmentation information, enabling the… More >

  • Open Access


    Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

    Jiao Wang, Bin Wu*, Hongying Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011

    Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >

  • Open Access


    Identifying Brand Consistency by Product Differentiation Using CNN

    Hung-Hsiang Wang1, Chih-Ping Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

    Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >

  • Open Access


    Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

    Xingli Wu1,{{sup}}#{{/sup}}, Huchang Liao1,{{sup}}#{{/sup}}, Shuxian Sun1, Zhengjun Wan2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3125-3146, 2024, DOI:10.32604/cmes.2023.047031

    Abstract Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decision-makers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare pairs of alternatives with which… More >

  • Open Access


    RESTlogic: Detecting Logic Vulnerabilities in Cloud REST APIs

    Ziqi Wang*, Weihan Tian, Baojiang Cui

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1797-1820, 2024, DOI:10.32604/cmc.2023.047051

    Abstract The API used to access cloud services typically follows the Representational State Transfer (REST) architecture style. RESTful architecture, as a commonly used Application Programming Interface (API) architecture paradigm, not only brings convenience to platforms and tenants, but also brings logical security challenges. Security issues such as quota bypass and privilege escalation are closely related to the design and implementation of API logic. Traditional code level testing methods are difficult to construct a testing model for API logic and test samples for in-depth testing of API logic, making it difficult to detect such logical vulnerabilities. We propose RESTlogic for this purpose.… More >

  • Open Access


    A Nonstandard Computational Investigation of SEIR Model with Fuzzy Transmission, Recovery and Death Rates

    Ahmed H. Msmali1, Fazal Dayan2,*, Muhammad Rafiq3, Nauman Ahmed4, Abdullah Ali H. Ahmadini1, Hassan A. Hamali5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2251-2269, 2023, DOI:10.32604/cmc.2023.040266

    Abstract In this article, a Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model is considered. The equilibrium analysis and reproduction number are studied. The conventional models have made assumptions of homogeneity in disease transmission that contradict the actual reality. However, it is crucial to consider the heterogeneity of the transmission rate when modeling disease dynamics. Describing the heterogeneity of disease transmission mathematically can be achieved by incorporating fuzzy theory. A numerical scheme nonstandard, finite difference (NSFD) approach is developed for the studied model and the results of numerical simulations are presented. Simulations of the constructed scheme are presented. The positivity, convergence and consistency of the… More >

  • Open Access


    A Data Consistency Insurance Method for Smart Contract

    Jing Deng1, Xiaofei Xing1, Guoqiang Deng2,*, Ning Hu3, Shen Su3, Le Wang3, Md Zakirul Alam Bhuiyan4

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3783-3795, 2023, DOI:10.32604/cmc.2023.034116

    Abstract As one of the major threats to the current DeFi (Decentralized Finance) ecosystem, reentrant attack induces data inconsistency of the victim smart contract, enabling attackers to steal on-chain assets from DeFi projects, which could terribly do harm to the confidence of the blockchain investors. However, protecting DeFi projects from the reentrant attack is very difficult, since generating a call loop within the highly automatic DeFi ecosystem could be very practicable. Existing researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing, and no method could promise the non-existent of reentrant vulnerabilities. In this paper, we introduce… More >

  • Open Access


    Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

    Waqas Ahmad1, Hikmat Ullah Khan1,2,*, Fawaz Khaled Alarfaj3,*, Saqib Iqbal4, Abdullah Mohammad Alomair3, Naif Almusallam3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3101-3124, 2023, DOI:10.32604/iasc.2023.040614

    Abstract Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative, positive, or neutral while associating them with their identified aspects from the corresponding context. In this regard, prior methodologies widely utilize either word embedding or tree-based representations. Meanwhile, the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss. Generally, word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence. Besides, the tree-based structure conserves the grammatical and logical dependencies of context. In addition, the sentence-oriented word position describes… More >

  • Open Access


    Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

    Qi Liu1,2, Jing Li1,2,*, Xianmin Wang1,*, Wenpeng Zhao1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600

    Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish… More >

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