Muhammad Naeem Ul Hassan^1,2, Zhengtao Yu^1,2,*, Jian Wang^1,2, Ying Li^1,2, Shengxiang Gao^1,2, Shuwan Yang^1,2, Cunli Mao^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673 - 15 October 2024

Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >

Huan Wang¹, Hong Wang¹, Zhongyuan Jiang^2,*, Qing Qian¹, Yong Long¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.053740 - 12 September 2024

Abstract Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses… More >

Yunxiang Liu, Haili Ma, Jianlin Zhu^*, Qiangbo Zhang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710 - 15 August 2024

Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862 - 20 June 2024

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Yuecheng Yu^1,*, Liming Cai¹, Anqi Ning¹, Jinlong Shi¹, Xudong Chen², Shixin Huang¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1915-1931, 2024, DOI:10.32604/cmc.2024.046125 - 27 February 2024

Abstract Under the influence of air humidity, dust, aerosols, etc., in real scenes, haze presents an uneven state. In this way, the image quality and contrast will decrease. In this case, It is difficult to detect the target in the image by the universal detection network. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed in this paper. Firstly, in the training phase, the Gated Context Aggregation Network (GCANet) is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes. In the test phase, only the… More >

Chaoyu Fan^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1719-1738, 2023, DOI:10.32604/cmc.2023.041395 - 29 November 2023

Abstract There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them. However, the volume of internet data is beyond the processing capabilities of the current internet infrastructure. Therefore, engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia. The goal of this paper is to explore the entity relationship based on deep learning, introduce semantic knowledge by using the prepared language model, develop an More >

Hengyang Liu, Chuncheng Huang^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710 - 30 August 2023

Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity… More >

Xiuye Liu, Aihua Wu^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 543-559, 2023, DOI:10.32604/cmc.2023.039004 - 08 June 2023

Abstract The human motion generation model can extract structural features from existing human motion capture data, and the generated data makes animated characters move. The 3D human motion capture sequences contain complex spatial-temporal structures, and the deep learning model can fully describe the potential semantic structure of human motion. To improve the authenticity of the generated human motion sequences, we propose a multi-task motion generation model that consists of a discriminator and a generator. The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17… More >

Kaiyue Wang¹, Jian Gao^1,2,*, Xinyan Lei¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 619-638, 2023, DOI:10.32604/iasc.2023.036701 - 29 April 2023

Abstract Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that… More >

Arwa Saif Fadel^1,2,*, Osama Ahmed Abulnaja¹, Mostafa Elsayed Saleh¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112 - 31 March 2023

Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical… More >