Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Qiang Ma^1,2,3,4, Zepeng Li^1,2, Kai Yang^1,2,*, Shaofeng Zhang^1,2, Zhuopei Wei^1,2

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876 - 17 November 2025

Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >

Tsu-Yang Wu^1,2, Haozhi Wu², Maoxin Tang³, Saru Kumari⁴, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1715-1732, 2025, DOI:10.32604/cmc.2025.065560 - 29 August 2025

Abstract With the rapid development and widespread adoption of Internet of Things (IoT) technology, the innovative concept of the Internet of Vehicles (IoV) has emerged, ushering in a new era of intelligent transportation. Since vehicles are mobile entities, they move across different domains and need to communicate with the Roadside Unit (RSU) in various regions. However, open environments are highly susceptible to becoming targets for attackers, posing significant risks of malicious attacks. Therefore, it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs, particularly in scenarios where More >

Jiahao Wu^1,2, Jinzhong Xu¹, Xiaoming Liu^1,*, Guan Yang^1,3, Jie Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2809-2828, 2025, DOI:10.32604/cmc.2025.061359 - 16 April 2025

Abstract Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain, due to the entity bias arising from the variation of entity information between different domains, which makes large language models prone to spurious correlations problems when dealing with specific domains and entities. In order to solve this problem, this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement, which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing… More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

Mohamad Khairulamirin Md Razali^1,*, Masri Ayob², Abdul Hadi Abd Rahman², Razman Jarmin³, Chian Yong Liu³, Muhammad Maaya³, Azarinah Izaham³, Graham Kendall^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1233-1288, 2025, DOI:10.32604/cmes.2025.060481 - 27 January 2025

Abstract The Cross-domain Heuristic Search Challenge (CHeSC) is a competition focused on creating efficient search algorithms adaptable to diverse problem domains. Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process. Numerous selection hyper-heuristics have different implementation strategies. However, comparisons between them are lacking in the literature, and previous works have not highlighted the beneficial and detrimental implementation methods of different components. The question is how to effectively employ them to produce an efficient search heuristic. Furthermore, the algorithms that competed in the inaugural CHeSC have not been collectively reviewed. This… More >

Binyong Li^1,2,3, Liangming Deng^1,*, Jie Zhang¹, Xianhui Deng¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 407-426, 2025, DOI:10.32604/cmc.2024.056730 - 03 January 2025

Abstract To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants, this research focuses on addressing the complex supply relationships in the automotive market, improving data sharing and interactions across various platforms, and achieving more detailed integration of data and operations. We propose a trust evaluation permission delegation method based on the automotive industry chain. The proposed method combines smart contracts with trust evaluation mechanisms, dynamically calculating the trust value of users based on the historical behavior of the delegated entity, network environment, and other More >

Zhaobin Li^*, Xiantao Liu^*, Nan Zhang, Zhanzhen Wei

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3835-3854, 2024, DOI:10.32604/cmc.2024.053950 - 12 September 2024

Abstract The Industrial Internet of Things (IIoT) consists of massive devices in different management domains, and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange. To address the above challenges, a viable solution that combines Certificateless Public Key Cryptography (CL-PKC) with blockchain technology can be utilized. However, as many existing schemes rely on a single Key Generation Center (KGC), they are prone to problems such as single points of failure and high computational overhead. In this case, this paper proposes a novel blockchain-based certificateless cross-domain authentication… More >

Youngho Park¹, Su Jin Shin², Sang Uk Shin^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 671-688, 2024, DOI:10.32604/cmes.2024.052378 - 20 August 2024

Abstract Data trading enables data owners and data requesters to sell and purchase data. With the emergence of blockchain technology, research on blockchain-based data trading systems is receiving a lot of attention. Particularly, to reduce the on-chain storage cost, a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform. Moreover, the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace. In the data marketplace, it is a challenge how to trade the data securely… More >

Feng Zhao¹, Hongtao Ding², Chunhai Li^1,*, Zhaoyu Su², Guoling Liang², Changsong Yang³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 567-585, 2024, DOI:10.32604/cmc.2024.052233 - 18 July 2024

Abstract The Internet of Vehicles (IoV) is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network. However, due to the open and variable nature of its network topology, vehicles frequently engage in cross-domain interactions. During such processes, directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers, thus compromising the security of the cross-domain authentication process. Additionally, IoV imposes high real-time requirements, and existing cross-domain authentication schemes for IoV often encounter efficiency issues. To mitigate More >