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  • Open Access

    ARTICLE

    BCCLR: A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

    Yunhe Wang1, Yuxin Xia2, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813

    Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action and context clues, leading to… More >

  • Open Access

    ARTICLE

    Extraction et mise en contexte spatial des propositions relatives au transport dans le Grand Débat National

    Jacques Fize1, Lucile Sautot2, Martin Lentschat3, Laurence Dujourdy4, Ludovic Journaux5, Mohamed Hilal6

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 329-354, 2022, DOI:10.3166/RIG.31.329-354© 2022

    Abstract The Great National Debate, launched by Emmanuel Macron in early 2019 to respond to the “Gilets jaunes” social movement, allowed the collection of citizens’ contributions on the ecological transition via an online platform. In this article, we use the corpus constituted by these contributions to identify locations where participants are asking for the development of bicycle paths and railway facilities. For this purpose, we have created a classification model to identify answers related to the theme of transportation and proposed a method for extracting contributions that reflect the contributors’ proposals. We then sought to explain the observed spatial frequency of… More >

  • Open Access

    ARTICLE

    Implementation of a solar model and shadow plotting in the context of a 2D GIS

    A validation based on radiometric measurements

    Thomas Leduc, Xenia Stavropulos-Laffaille, Ignacio Requena-Ruiz

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 241-263, 2022, DOI:10.3166/RIG.31.241-263©2022

    Abstract The adaptation of public spaces to episodes of intense heat is now a major challenge for cities. With this in mind, this article presents a contribution aimed at delineating and handling the shadows on the ground or in a horizontal plane at a given height, whether it comes from buildings, street furniture or the tree cover. After a comparison with shadows obtained via two reference tools, we present two urban sites that mix shadows of different origins and, in addition, different indicators. The results of the simulations are compared with pyranometric surveys carried out on site. The aim of these… More >

  • Open Access

    ARTICLE

    Towards Lessening Learners’ Aversive Emotions and Promoting Their Mental Health: Developing and Validating a Measurement of English Speaking Demotivation in the Chinese EFL Context

    Chili Li1, Xinxin Zhao2, Ziwen Pan3, Ting Yi4, Long Qian5,6,*

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 161-175, 2024, DOI:10.32604/ijmhp.2023.029896

    Abstract While a plethora of studies has been conducted to explore demotivation and its impact on mental health in second language (L2) education, scanty research focuses on demotivation in L2 speaking learning. Particularly, little research explores the measures to quantify L2 speaking demotivation. The present two-phase study attempts to develop and validate an English Speaking Demotivation Scale (ESDS). To this end, an independent sample of 207 Chinese tertiary learners of English as a Foreign Language (EFL) participated in the development phase, and another group of 188 Chinese EFL learners was recruited for the validation of the scale. Exploratory Factor Analysis (EFA)… More >

  • Open Access

    ARTICLE

    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

    Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify… More >

  • Open Access

    ARTICLE

    Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

    Nadeem Salamat1, Muhammad Kamran1,2,*, Shahzaib Ashraf1, Manal Elzain Mohammed Abdulla3, Rashad Ismail3, Mohammed M. Al-Shamiri3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1893-1933, 2024, DOI:10.32604/cmes.2023.044697

    Abstract The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy, accessibility, and cost-effectiveness. This paper investigates the potential applications of intuitionistic fuzzy sets (IFS) with rough sets in the context of sparse data. When it comes to capture uncertain information emanating from both upper and lower approximations, these intuitionistic fuzzy rough numbers (IFRNs) are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets, respectively. We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties. We present numerous… More > Graphic Abstract

    Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

  • Open Access

    ARTICLE

    CALTM: A Context-Aware Long-Term Time-Series Forecasting Model

    Canghong Jin1,*, Jiapeng Chen1, Shuyu Wu1, Hao Wu2, Shuoping Wang1, Jing Ying3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230

    Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

  • Open Access

    ARTICLE

    Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

    Yujun Zhang*, Dezhi Han, Peng Chen

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311

    Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >

  • Open Access

    ARTICLE

    A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

    Salma Khan1, Muhammad Gulistan1, Nasreen Kausar2, Seifedine Kadry3,4,5, Jungeun Kim6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1951-1979, 2024, DOI:10.32604/cmes.2023.030896

    Abstract Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the ROFHS values to support multi-attribute decision-making algorithms. By implementing… More >

  • Open Access

    ARTICLE

    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 >

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