Chunhua Wang^1,2, Wenqian Shang^1,2,*, Tong Yi^3,*, Haibin Zhu⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135 - 15 May 2024

Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

Litian Huang, Xinguo Yu, Lei Niu^*, Zihan Feng

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 517-539, 2023, DOI:10.32604/cmc.2023.041206 - 31 October 2023

Abstract Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and… More >

Xiaozhong Chen^*

Molecular & Cellular Biomechanics, Vol.20, No.1, pp. 1-14, 2023, DOI:10.32604/mcb.2022.026964 - 20 June 2023

Abstract Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface, the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are More >

Yigang Wang¹, Hong Li¹, Wanbin Pan^1,*, Weijuan Cao¹, Jie Miao¹, Xiaofei Ai¹, Enya Shen²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 859-878, 2023, DOI:10.32604/cmes.2023.027320 - 23 April 2023

Abstract Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First,… More >

Yunbo Rao^1,2, Hongyu Mu¹, Zeyu Yang¹, Weibin Zheng¹, Faxin Wang¹, Jiansu Pu¹, Shaoning Zeng²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1039-1054, 2022, DOI:10.32604/cmes.2022.020331 - 27 June 2022

Abstract Multi-scale object detection is a research hotspot, and it has critical applications in many secure systems. Although the object detection algorithms have constantly been progressing recently, how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors, such as the deformation and occlusion of the object in the actual scene. The more interference factors, the more complicated the semantic information, so we need a deeper network to extract deep information. However, deep neural networks often suffer from network degradation. To prevent the occurrence of… More >

Chenxi Yu, Xin Li^*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2023-2034, 2022, DOI:10.32604/iasc.2022.029447 - 25 May 2022

Abstract Machine reading comprehension (MRC) is a task in natural language comprehension. It assesses machine reading comprehension based on text reading and answering questions. Traditional attention methods typically focus on one of syntax or semantics, or integrate syntax and semantics through a manual method, leaving the model unable to fully utilize syntax and semantics for MRC tasks. In order to better understand syntactic and semantic information and improve machine reading comprehension, our study uses syntactic and semantic attention to conduct text modeling for tasks. Based on the BERT model of Transformer encoder, we separate a text… More >

Mamoona Majid¹, Muhammad Faisal Hayat², Farrukh Zeeshan Khan³, Muneer Ahmad^4,*, NZ Jhanjhi⁵, Mohammad Arif Sobhan Bhuiyan⁶, Mehedi Masud⁷, Mohammed A. AlZain⁸

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 373-386, 2021, DOI:10.32604/iasc.2021.017840 - 26 July 2021

Abstract Choosing the right university program can be very challenging for students. This is especially the case in developing countries such as India and Pakistan, where university admission depends on not only the program of interest but also other factors such as the candidate’s financial standing. Since information on the Internet can be highly scattered, university candidates often need counseling from qualified people to decide their educational programs. Traditional database systems cannot effectively organize the large unstructured data related to university programs. It is challenging, then, for prospective students to acquire the information needed to make More >

Ramsha Saeed¹, Saddaf Rubab¹, Sara Asif¹, Malik M. Khan¹, Saeed Murtaza¹, Seifedine Kadry², Yunyoung Nam^3,*, Muhammad Attique Khan^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355 - 19 April 2021

Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically More >

Tingxin Wei^{1, 2}, Weiguang Qu^{2, 3, *}, Junsheng Zhou³, Yunfei Long⁴, Yanhui Gu³, Zhentao Xia³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1897-1913, 2020, DOI:10.32604/cmc.2020.010813 - 30 June 2020

Abstract The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning More >

Ning Cao^1,2, Shengfang Li¹, Keyong Shen¹, Sheng Bin³, Gengxin Sun^3,*, Dongjie Zhu⁴, Xiuli Han⁵, Guangsheng Cao⁵, Abraham Campbell⁶

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 227-241, 2019, DOI:10.32604/cmc.2019.06125

Abstract Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics More >