Chunhong Guo¹, Huifang Liu², Jingfu Guo^3,*

Journal of Psychology in Africa, Vol.35, No.3, pp. 411-418, 2025, DOI:10.32604/jpa.2025.065763 - 31 July 2025

Abstract We explored the effects of algorithmic opacity on employees’ playing dumb and evasive hiding rather than rationalized hiding. We examined the mediating role of job insecurity and the moderating role of employee-AI collaboration. Participants were 421 full-time employees (female = 46.32%, junior employees = 31.83%) from a variety of organizations and industries that interact with AI. Employees filled out data on algorithm opacity, job insecurity, knowledge hiding, employee-AI collaboration, and control variables. The results of the structural equation modeling indicated that algorithm opacity exacerbated employees’ job insecurity, and job insecurity mediated between algorithm opacity and More >

Ying Shen¹, Shichao Zhao¹, Yanfei Lv¹, Fei Chen¹, Li Fu^1,*, Hassan Karimi-Maleh^2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1921-1950, 2025, DOI:10.32604/cmc.2025.067427 - 03 July 2025

Abstract This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature,… More >

Haotong Wang^#,*, Liyan Wang^#, Yves Lepage

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 305-324, 2025, DOI:10.32604/cmc.2025.066351 - 09 June 2025

Abstract Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs (KGs) remains challenging. Prior research exhibits a narrow focus on structural statistics, such as the shortest path length, while the correctness of graphs in representing the associated text is rarely explored. To address this gap, we introduce a dual-perspective evaluation framework for KG-text data, based on the computation of structural adequacy and semantic alignment. From a structural perspective, we propose the Weighted Incremental Edge Method (WIEM) to quantify graph completeness by leveraging agreement between relation models… More >

Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1839-1861, 2025, DOI:10.32604/cmc.2025.064489 - 09 June 2025

Abstract Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator… More >

Yinuo Chen¹, Xu Wu¹, Jiaxin Fan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

Haocheng Sun, Ping Li, Ying Li^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4899-4914, 2025, DOI:10.32604/cmc.2025.063029 - 19 May 2025

Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >

Haotong Wang^*, Yves Lepage

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4141-4166, 2025, DOI:10.32604/cmc.2025.062806 - 19 May 2025

Abstract Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks, and generating descriptive text based on these graphs places significant emphasis on content consistency. However, knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes, making it challenging to ensure content coherence in generating text that spans multiple sentences. This lack of coherence can further compromise the overall consistency of the content within a paragraph. In this work, we present the generation of scientific abstracts by leveraging knowledge graphs, with a focus on enhancing both… More >

Shaoming Qiu, Xinchen Huang^*, Liangyu Liu, Bicong E, Jingfeng Ye

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5657-5678, 2025, DOI:10.32604/cmc.2025.062482 - 19 May 2025

Abstract Most existing knowledge graph relationship prediction methods are unable to capture the complex information of multi-relational knowledge graphs, thus overlooking key details contained in different entity pairs and making it difficult to aggregate more complex relational features. Moreover, the insufficient capture of multi-hop relational information limits the processing capability of the global structure of the graph and reduces the accuracy of the knowledge graph completion task. This paper uses graph neural networks to construct new message functions for different relations, which can be defined as the rotation from the source entity to the target entity… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5345-5371, 2025, DOI:10.32604/cmc.2025.062340 - 19 May 2025

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >

Huansha Wang^*, Ruiyang Huang^*, Qinrang Liu, Xinghao Wang

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5747-5760, 2025, DOI:10.32604/cmc.2025.061902 - 19 May 2025

Abstract Multi-modal Named Entity Recognition (MNER) aims to better identify meaningful textual entities by integrating information from images. Previous work has focused on extracting visual semantics at a fine-grained level, or obtaining entity related external knowledge from knowledge bases or Large Language Models (LLMs). However, these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches. In this paper, we present MMAVK, a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion, which aims to leverage the Multi-modal Large Language Model… More >