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  • Open Access

    ARTICLE

    Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

    Ronghao Pan, José Antonio García-Díaz*, Rafael Valencia-García

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631 - 08 July 2024

    Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

  • Open Access

    ARTICLE

    A Dual Discriminator Method for Generalized Zero-Shot Learning

    Tianshu Wei1, Jinjie Huang1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1599-1612, 2024, DOI:10.32604/cmc.2024.048098 - 25 April 2024

    Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can… More >

  • Open Access

    ARTICLE

    A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

    Bishrul Haq1, Sher Muhammad Daudpota1, Ali Shariq Imran2, Zenun Kastrati3,*, Waheed Noor4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638 - 31 October 2023

    Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a… More >

  • Open Access

    ARTICLE

    Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

    Vidit Kumar1,*, Hemant Petwal2, Ajay Krishan Gairola1, Pareshwar Prasad Barmola1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047 - 03 April 2023

    Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to… More >

  • Open Access

    ARTICLE

    A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

    Nagwa Elaraby*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288 - 22 September 2022

    Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two… More >

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