Hao Jiang¹, Yuerong Liao¹, Dongdong Zhao², Wenjian Luo³, Xingyi Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653

Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Yingchao Li^1,*, Jianbin Wang¹, Haibin Wang²

Energy Engineering, Vol.121, No.4, pp. 1049-1065, 2024, DOI:10.32604/ee.2023.045228

Abstract With the increasing demand for electrical services, wind farm layout optimization has been one of the biggest challenges that we have to deal with. Despite the promising performance of the heuristic algorithm on the route network design problem, the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored. In this paper, the wind farm layout optimization problem is defined. Then, a multi-objective algorithm based on Graph Neural Network (GNN) and Variable Neighborhood Search (VNS) algorithm is proposed. GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the… More >

Guilhem Paillard-Brunet^*, Audrey Couillet

Psycho-Oncologie, Vol.18, No.1, pp. 23-31, 2024, DOI:10.32604/po.2023.047888

Abstract Cette étude qualitative visait à recueillir chez les professionnels d’un Centre de Lutte Contre le Cancer les attentes et les réticences qu’ils pouvaient avoir vis-à-vis des interventions de pair-aidance. Des entretiens individuels semi-structurés ont été menés auprès de 12 professionnels issus de professions différentes. Une retranscription intégrale des entretiens puis une analyse thématique de leur contenu ont été conduites. L’analyse des données a permis de faire émerger trois thèmes principaux quant aux attentes exprimées par les soignants : le besoin d’un accompagnement plus soutenant des patients, rompre leur isolement dans la maladie et enrichir le réseau de soins. L’analyse a… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

Yong Cui, Yongju Zhao, Shengnan Ouyang, Changchang Shao, Liangliang Li, Honglang Duan^*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 291-312, 2024, DOI:10.32604/phyton.2024.047281

Abstract Plant temperature acclimation is closely related to maintaining a positive carbon gain under future climate change. However, no systematic summary of the field has been conducted. Based on this, we analyzed data on plant temperature acclimation from the Web of Science Core Collection database using bibliometric software R, RStudio and VOSviewer. Our study demonstrated that a stabilized upward trajectory was noted in publications (298 papers) from 1986 to 2011, followed by a swift growth (373 papers) from 2012 to 2022. The most impactful journals were Plant Cell and Environment, boasting the greatest count of worldwide citations and articles, the highest… More >

Shihu Liu, Chunsheng Yang^*, Yingjie Liu

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 203-223, 2023, DOI:10.32604/iasc.2023.040818

Abstract Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved… More >

Yajing Ma^1,2,3, Gulila Altenbek^1,2,3,*, Yingxia Yu¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486

Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations,… More >

Lanze Zhang, Yijun Gu^*, Jingjie Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129

Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

Xiaoyi Wang¹, Xinyue Chang², Wenxuan Wang^1,*, Zijie Qiao³, Feng Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1775-1796, 2024, DOI:10.32604/cmes.2023.043913

Abstract The objective of reliability-based design optimization (RBDO) is to minimize the optimization objective while satisfying the corresponding reliability requirements. However, the nested loop characteristic reduces the efficiency of RBDO algorithm, which hinders their application to high-dimensional engineering problems. To address these issues, this paper proposes an efficient decoupled RBDO method combining high dimensional model representation (HDMR) and the weight-point estimation method (WPEM). First, we decouple the RBDO model using HDMR and WPEM. Second, Lagrange interpolation is used to approximate a univariate function. Finally, based on the results of the first two steps, the original nested loop reliability optimization model is… More >