Pub Date : 2025-03-18DOI: 10.1016/j.asoc.2025.112977
Luan Nguyen-Huynh , Tai Vo-Van
This study proposes a new forecasting model for time series based on the improvement and combination of the cluster analysis (CA) algorithm and deep learning with Convolutional Neural Network (CNN) and Bi-Long Short Term Memory (BiLSTM) model. The proposed model is considered pioneering in this research direction with significant contributions to three main phases. For the first phase, the original series is converted into the percentage change series and is divided into clusters of an appropriate number using the CA algorithm. The next phase involves extracting the features of the new series based on the CNN with suitable parameters and input data enhancement from the results of the first phase. In the final phase, the BiLSTM model is applied to the series established from the second phase, and the forecasting principle for the future is established. The proposed model is detailed in the implementation steps, proving convergence, illustrated by numerical examples, and can be applied to real series using a Matlab procedure. The effectiveness of the proposed model is quite impressive as it surpasses many strong forecasting models on reputable benchmark datasets , including the M3-Competition dataset with 3,003 series, and M4-Competition dataset with 100,000 series.
{"title":"Developing a forecasting model for time series based on clustering and deep learning algorithms","authors":"Luan Nguyen-Huynh , Tai Vo-Van","doi":"10.1016/j.asoc.2025.112977","DOIUrl":"10.1016/j.asoc.2025.112977","url":null,"abstract":"<div><div>This study proposes a new forecasting model for time series based on the improvement and combination of the cluster analysis (CA) algorithm and deep learning with Convolutional Neural Network (CNN) and Bi-Long Short Term Memory (BiLSTM) model. The proposed model is considered pioneering in this research direction with significant contributions to three main phases. For the first phase, the original series is converted into the percentage change series and is divided into clusters of an appropriate number using the CA algorithm. The next phase involves extracting the features of the new series based on the CNN with suitable parameters and input data enhancement from the results of the first phase. In the final phase, the BiLSTM model is applied to the series established from the second phase, and the forecasting principle for the future is established. The proposed model is detailed in the implementation steps, proving convergence, illustrated by numerical examples, and can be applied to real series using a Matlab procedure. The effectiveness of the proposed model is quite impressive as it surpasses many strong forecasting models on reputable benchmark datasets , including the M3-Competition dataset with 3,003 series, and M4-Competition dataset with 100,000 series.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112977"},"PeriodicalIF":7.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-16DOI: 10.1016/j.asoc.2025.112955
Xiaoxu Guo , Siyan Liang , Yachao Cui , Juxiang Zhou , Lei Wang , Han Cao
Accurate assessment of post quality frequently necessitates complex relational reasoning skills that emulate human cognitive processes, thereby requiring the modeling of nuanced relationships. However, existing research on post-quality assessment suffers from the following problems: (1) They are often categorization tasks that rely solely on unimodal data, which inadequately captures information in multimodal contexts and fails to differentiate the quality of students’ posts finely. (2) They ignore the noise in the multimodal deep fusion between posts and topics, which may produce misleading information for the model. (3) They do not adequately capture the complex and fine-grained relationships between post and topic, resulting in an inaccurate evaluation, such as relevance and comprehensiveness. Based on the above challenges, the Multimodal Fine-grained Topic-post Relational Reasoning(MFTRR) framework is proposed for modeling fine-grained cues by simulating the human thinking process. It consists of the local–global semantic correlation reasoning module and the multi-level evidential relational reasoning module. Specifically, MFTRR addresses the challenge of unimodal and categorization task limitations by framing post-quality assessment as a ranking task and integrating multimodal data to more effectively distinguish quality differences. To capture the most relevant semantic relationships, the Local–Global Semantic Correlation Reasoning Module enables deep interactions between posts and topics at both local and global scales. It is complemented by a topic-based maximum information fusion mechanism to filter out noise. Furthermore, to model complex and subtle relational reasoning, the Multi-Level Evidential Relational Reasoning Module analyzes topic-post relationships at both macro and micro levels by identifying critical cues and delving into granular relational cues. MFTRR is evaluated using three newly curated multimodal topic-post datasets, in addition to the publicly available Lazada-Home dataset. Experimental results indicate that MFTRR outperforms state-of-the-art baselines, achieving a 9.52% improvement in the NDCG@3 metric compared to the best text-only method on the Art History course dataset.
{"title":"Multimodal fine-grained reasoning for post quality evaluation","authors":"Xiaoxu Guo , Siyan Liang , Yachao Cui , Juxiang Zhou , Lei Wang , Han Cao","doi":"10.1016/j.asoc.2025.112955","DOIUrl":"10.1016/j.asoc.2025.112955","url":null,"abstract":"<div><div>Accurate assessment of post quality frequently necessitates complex relational reasoning skills that emulate human cognitive processes, thereby requiring the modeling of nuanced relationships. However, existing research on post-quality assessment suffers from the following problems: (1) They are often categorization tasks that rely solely on unimodal data, which inadequately captures information in multimodal contexts and fails to differentiate the quality of students’ posts finely. (2) They ignore the noise in the multimodal deep fusion between posts and topics, which may produce misleading information for the model. (3) They do not adequately capture the complex and fine-grained relationships between post and topic, resulting in an inaccurate evaluation, such as relevance and comprehensiveness. Based on the above challenges, the Multimodal Fine-grained Topic-post Relational Reasoning(MFTRR) framework is proposed for modeling fine-grained cues by simulating the human thinking process. It consists of the local–global semantic correlation reasoning module and the multi-level evidential relational reasoning module. Specifically, MFTRR addresses the challenge of unimodal and categorization task limitations by framing post-quality assessment as a ranking task and integrating multimodal data to more effectively distinguish quality differences. To capture the most relevant semantic relationships, the Local–Global Semantic Correlation Reasoning Module enables deep interactions between posts and topics at both local and global scales. It is complemented by a topic-based maximum information fusion mechanism to filter out noise. Furthermore, to model complex and subtle relational reasoning, the Multi-Level Evidential Relational Reasoning Module analyzes topic-post relationships at both macro and micro levels by identifying critical cues and delving into granular relational cues. MFTRR is evaluated using three newly curated multimodal topic-post datasets, in addition to the publicly available Lazada-Home dataset. Experimental results indicate that MFTRR outperforms state-of-the-art baselines, achieving a 9.52% improvement in the NDCG@3 metric compared to the best text-only method on the Art History course dataset.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112955"},"PeriodicalIF":7.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-16DOI: 10.1016/j.asoc.2025.112979
Jing Chen , Haocheng Ye , Zhian Ying , Yuntao Sun , Wenqiang Xu
Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the Dynamic Spatial-Temporal Trend Transformer (DST2former) is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the Dynamic Trend Representation Transformer (DTRformer) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.
{"title":"Dynamic trend fusion module for traffic flow prediction","authors":"Jing Chen , Haocheng Ye , Zhian Ying , Yuntao Sun , Wenqiang Xu","doi":"10.1016/j.asoc.2025.112979","DOIUrl":"10.1016/j.asoc.2025.112979","url":null,"abstract":"<div><div>Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the <strong>D</strong>ynamic <strong>S</strong>patial-<strong>T</strong>emporal <strong>T</strong>rend Trans<strong>former</strong> (<strong>DST<sup>2</sup>former</strong>) is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the <strong>D</strong>ynamic <strong>T</strong>rend <strong>R</strong>epresentation Trans<strong>former</strong> (<strong>DTRformer</strong>) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112979"},"PeriodicalIF":7.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-16DOI: 10.1016/j.asoc.2025.113008
Xian Mo , Jun Pang , Zihang Zhao
Recommender systems are a vital tool to guide the overwhelming amount of online information for users, which has been successfully applied to online retail platforms, social networks, etc. Recently, contrastive learning has revealed outstanding performance in recommendation by data augmentation strategies to handle highly sparse data. Most existing work fails to leverage the original network’s topology to construct attention-aware modules that identify user–item interaction importance for guiding node aggregation while preserving key semantics and reducing noise in the reconstructed graph during data augmentation. In this paper, our work proposes an Attention-aware Graph Contrastive Learning architecture with Topological Relationship (AteGCL) for recommendation. In particular, our AteGCL proposes an attention-aware mechanism with topological relationships to learn the importance between users and items for extracting the local graph dependency, which identifies the importance between nodes by constructing an attention-aware matrix into graph convolutional networks using a random walk with a restart strategy for generating node feature aggregation. We then employ principal component analysis (PCA) for contrastive augmentation and utilize the attention-aware matrix to ease noise from the reconstructed graph generated by PCA and to generate a new view with global collaborative relationships and less noise. Comprehensive experiments on three real-world user–item networks reveal the superiority of our AteGCL over diverse state-of-the-art recommendation approaches. Our code is available at https://github.com/ZZHCodeZera/AteGCL.
{"title":"Attention-aware graph contrastive learning with topological relationship for recommendation","authors":"Xian Mo , Jun Pang , Zihang Zhao","doi":"10.1016/j.asoc.2025.113008","DOIUrl":"10.1016/j.asoc.2025.113008","url":null,"abstract":"<div><div>Recommender systems are a vital tool to guide the overwhelming amount of online information for users, which has been successfully applied to online retail platforms, social networks, etc. Recently, contrastive learning has revealed outstanding performance in recommendation by data augmentation strategies to handle highly sparse data. Most existing work fails to leverage the original network’s topology to construct attention-aware modules that identify user–item interaction importance for guiding node aggregation while preserving key semantics and reducing noise in the reconstructed graph during data augmentation. In this paper, our work proposes an <u>At</u>t<u>e</u>ntion-aware <u>G</u>raph <u>C</u>ontrastive <u>L</u>earning architecture with Topological Relationship (AteGCL) for recommendation. In particular, our AteGCL proposes an attention-aware mechanism with topological relationships to learn the importance between users and items for extracting the local graph dependency, which identifies the importance between nodes by constructing an attention-aware matrix into graph convolutional networks using a random walk with a restart strategy for generating node feature aggregation. We then employ principal component analysis (PCA) for contrastive augmentation and utilize the attention-aware matrix to ease noise from the reconstructed graph generated by PCA and to generate a new view with global collaborative relationships and less noise. Comprehensive experiments on three real-world user–item networks reveal the superiority of our AteGCL over diverse state-of-the-art recommendation approaches. Our code is available at <span><span>https://github.com/ZZHCodeZera/AteGCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 113008"},"PeriodicalIF":7.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1016/j.asoc.2025.112994
Xiaoling Song , Guangxia Xu , Yongfei Huang
Self-sovereign identity (SSI) technology has advantages and potential for application in the metaverse. However, the decentralization and anonymous interaction of SSI create convenience for malicious attacks, frauds, and conspiracies in the metaverse. It leads to various trust risks and threats to the meta-universe system. To address these challenges, we analyze the risks of SSI systems and constructed a reputation index system. Moreover, we propose a blockchain-based reputation management framework (BBRMF), which can constrain users from engaging in illegal activities such as forgery, fraud, and conspiracy, thereby guaranteeing the security and trustworthiness of the entities involved in the metaverse. In BBRMF, we constructed a reputation evaluation model based on fuzzy analytical hierarchy process (FAHP) to assess the user’s reputation in three dimensions: reliability, trustworthiness and security. To motivate users to accumulate more positive reputation, we set the user’s reputation score into a reputation credential in the form of non-fungible token (NFT), through which users can obtain more benefits and opportunities. Finally, we calculated the reputation value of SSI related entities from multiple perspectives through simulation experiments and comparative analysis. The feasibility of the proposed method is verified, and it is proved that it can effectively resist the interference and attack of malicious scoring nodes. Moreover, the scheme adopts multi-dimensional evaluation indexes and behavioral feature values, which significantly improves the comprehensiveness and accuracy of the reputation assessment. Meanwhile, the weights of the evaluation indexes are derived through objective calculation, ensuring the fairness of the evaluation results, and improving the credibility and repeatability of the reputation assessment.
{"title":"A Fuzzy AHP-based trust management mechanism for self-sovereign identity in the metaverse","authors":"Xiaoling Song , Guangxia Xu , Yongfei Huang","doi":"10.1016/j.asoc.2025.112994","DOIUrl":"10.1016/j.asoc.2025.112994","url":null,"abstract":"<div><div>Self-sovereign identity (SSI) technology has advantages and potential for application in the metaverse. However, the decentralization and anonymous interaction of SSI create convenience for malicious attacks, frauds, and conspiracies in the metaverse. It leads to various trust risks and threats to the meta-universe system. To address these challenges, we analyze the risks of SSI systems and constructed a reputation index system. Moreover, we propose a blockchain-based reputation management framework (BBRMF), which can constrain users from engaging in illegal activities such as forgery, fraud, and conspiracy, thereby guaranteeing the security and trustworthiness of the entities involved in the metaverse. In BBRMF, we constructed a reputation evaluation model based on fuzzy analytical hierarchy process (FAHP) to assess the user’s reputation in three dimensions: reliability, trustworthiness and security. To motivate users to accumulate more positive reputation, we set the user’s reputation score into a reputation credential in the form of non-fungible token (NFT), through which users can obtain more benefits and opportunities. Finally, we calculated the reputation value of SSI related entities from multiple perspectives through simulation experiments and comparative analysis. The feasibility of the proposed method is verified, and it is proved that it can effectively resist the interference and attack of malicious scoring nodes. Moreover, the scheme adopts multi-dimensional evaluation indexes and behavioral feature values, which significantly improves the comprehensiveness and accuracy of the reputation assessment. Meanwhile, the weights of the evaluation indexes are derived through objective calculation, ensuring the fairness of the evaluation results, and improving the credibility and repeatability of the reputation assessment.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112994"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1016/j.asoc.2025.112980
Yijie Wang , Xiao Wu , Jiaying Zhang , Weiping Wang , Linjiang Zheng , Jiaxing Shang
Multivariate time series forecasting (MTSF) is widely employed in research-intensive domains, such as weather forecasting. Recently, Transformer-based models have outstanding ability to achieve SOTA performance, benefiting from its self-attention mechanism. However, existing models fall short in capturing multivariate inter-dependencies and local semantic representations. To tackle the above limitations, we propose a series clustering and dynamic periodic patching-based Transformer model named CMDPPformer, with two distinctive characteristics: (1) A channel-mixing module based on series clustering is proposed which can strengthen the association between variables with high sequence similarity, and weaken the effect of uncorrelated variables. Concretely, we use whole-time series clustering to group multivariate time series into clusters. After that, variables in the same cluster share the same Transformer backbone while variables in different clusters do not affect each other. (2) A dynamic periodic patching module is introduced which can better capture semantic information and improve Transformer’s local semantic representation. Concretely, multivariate time series after clustering are dynamically segmented into periodic patches as Transformer’s input token. Experimental results show that CMDPPformer can achieve an overall 13.76% and 10.16% relative improvements than SOTA Transformer-based models on seven benchmarks, covering four real-world applications: energy, weather, illness and economic.
{"title":"Series clustering and dynamic periodic patching-based transformer for multivariate time series forecasting","authors":"Yijie Wang , Xiao Wu , Jiaying Zhang , Weiping Wang , Linjiang Zheng , Jiaxing Shang","doi":"10.1016/j.asoc.2025.112980","DOIUrl":"10.1016/j.asoc.2025.112980","url":null,"abstract":"<div><div>Multivariate time series forecasting (MTSF) is widely employed in research-intensive domains, such as weather forecasting. Recently, Transformer-based models have outstanding ability to achieve SOTA performance, benefiting from its self-attention mechanism. However, existing models fall short in capturing multivariate inter-dependencies and local semantic representations. To tackle the above limitations, we propose a series clustering and dynamic periodic patching-based Transformer model named CMDPPformer, with two distinctive characteristics: (1) A channel-mixing module based on series clustering is proposed which can strengthen the association between variables with high sequence similarity, and weaken the effect of uncorrelated variables. Concretely, we use whole-time series clustering to group multivariate time series into clusters. After that, variables in the same cluster share the same Transformer backbone while variables in different clusters do not affect each other. (2) A dynamic periodic patching module is introduced which can better capture semantic information and improve Transformer’s local semantic representation. Concretely, multivariate time series after clustering are dynamically segmented into periodic patches as Transformer’s input token. Experimental results show that CMDPPformer can achieve an overall 13.76% and 10.16% relative improvements than SOTA Transformer-based models on seven benchmarks, covering four real-world applications: energy, weather, illness and economic.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112980"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medical image segmentation involves partitioning different tissues or lesion areas within medical images. Achieving automatic segmentation can markedly improve efficiency and accuracy, which is significant for biomedical clinical diagnosis. With the rapid development of deep convolutional neural networks (DCNN), U-Net has been widely used in medical image segmentation due to its encoder-decoder structure and skip connection. However, it is still hard for U-Net to handle certain challenging cases. In this study, we propose an adaptive encoding and comprehensive attention decoding network (AA-Net), which is derived from U-Net to address the issues of the semantic gap as well as the loss of spatial information during convolutions. AA-Net takes into account the different characteristics of the encoder and decoder. In the encoder, we design a simple Adaptive Calibration Module (ACM) to improve the representation ability of candidate features. In the decoder, we introduce a Comprehensive Attention Feature Extraction (CAFE) module, which employs multiple attention mechanisms after feature fusion to alleviate the semantic gap. Benefiting from CAFE, AA-Net can better handle the challenging cases where the segmentation targets vary in position, size, and scale. Additionally, we suggest a weighted hybrid loss function for precise boundary segmentation. We validate the effectiveness of AA-Net and each component on three biomedical image datasets. The results demonstrate that our method outperforms state-of-the-art methods in different medical segmentation tasks, proving it is lightweight, efficient, and general.
{"title":"Adaptive encoding and comprehensive attention decoding network for medical image segmentation","authors":"Xin Shu , Aoping Zhang , Zhaoyang Xu , Feng Zhu , Wei Hua","doi":"10.1016/j.asoc.2025.112990","DOIUrl":"10.1016/j.asoc.2025.112990","url":null,"abstract":"<div><div>Medical image segmentation involves partitioning different tissues or lesion areas within medical images. Achieving automatic segmentation can markedly improve efficiency and accuracy, which is significant for biomedical clinical diagnosis. With the rapid development of deep convolutional neural networks (DCNN), U-Net has been widely used in medical image segmentation due to its encoder-decoder structure and skip connection. However, it is still hard for U-Net to handle certain challenging cases. In this study, we propose an adaptive encoding and comprehensive attention decoding network (AA-Net), which is derived from U-Net to address the issues of the semantic gap as well as the loss of spatial information during convolutions. AA-Net takes into account the different characteristics of the encoder and decoder. In the encoder, we design a simple Adaptive Calibration Module (ACM) to improve the representation ability of candidate features. In the decoder, we introduce a Comprehensive Attention Feature Extraction (CAFE) module, which employs multiple attention mechanisms after feature fusion to alleviate the semantic gap. Benefiting from CAFE, AA-Net can better handle the challenging cases where the segmentation targets vary in position, size, and scale. Additionally, we suggest a weighted hybrid loss function for precise boundary segmentation. We validate the effectiveness of AA-Net and each component on three biomedical image datasets. The results demonstrate that our method outperforms state-of-the-art methods in different medical segmentation tasks, proving it is lightweight, efficient, and general.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112990"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.asoc.2025.112959
Zecheng Peng , Bingwen Feng , Xiaotao Xu , Jilian Zhang , Donghong Cai , Wei Lu
To enhance the visual diversity of Quick Response (QR) codes while ensuring their robust decoding capabilities, this paper introduces an innovative invisible hyperlink generation system. The system can use a message sequence to directly generate a hyperlink image. By harnessing the latent space of a suggested feature point generation network, the system extends the robustness of image feature points to the hyperlink images it generates. Specially, an image generation network is first designed to synthesize high-quality images based on feature point data. Subsequently, a set of lightweight message encoder and decoder are introduced to embed message bits into the latent space of the image generation network. Experimental results show that the proposed invisible hyperlink generation system can successfully generate images containing hyperlinks, exhibiting remarkable resilience against common signal processing and geometric distortions. It harbors diverse potential applications, encompassing website URLs, contact information, product specifics, and numerous other use cases.
{"title":"Geometrical invariant generative invisible hyperlinks based on feature points","authors":"Zecheng Peng , Bingwen Feng , Xiaotao Xu , Jilian Zhang , Donghong Cai , Wei Lu","doi":"10.1016/j.asoc.2025.112959","DOIUrl":"10.1016/j.asoc.2025.112959","url":null,"abstract":"<div><div>To enhance the visual diversity of Quick Response (QR) codes while ensuring their robust decoding capabilities, this paper introduces an innovative invisible hyperlink generation system. The system can use a message sequence to directly generate a hyperlink image. By harnessing the latent space of a suggested feature point generation network, the system extends the robustness of image feature points to the hyperlink images it generates. Specially, an image generation network is first designed to synthesize high-quality images based on feature point data. Subsequently, a set of lightweight message encoder and decoder are introduced to embed message bits into the latent space of the image generation network. Experimental results show that the proposed invisible hyperlink generation system can successfully generate images containing hyperlinks, exhibiting remarkable resilience against common signal processing and geometric distortions. It harbors diverse potential applications, encompassing website URLs, contact information, product specifics, and numerous other use cases.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112959"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.asoc.2025.112986
Licheng Sun, Ao Ding, Hongbin Ma
Currently, multi-agent reinforcement learning (MARL) has been applied to various domains such as communications, network management, power systems, and autonomous driving, showcasing broad application scenarios and significant research potential. However, in complex decision-making environments, agents that rely solely on temporal value functions often struggle to capture and extract hidden features and dependencies within long sequences in multi-agent settings. Each agent’s decisions are influenced by a sequence of prior states and actions, leading to complex spatiotemporal dependencies that are challenging to analyze directly in the time domain. Addressing these challenges requires a paradigm shift to analyze such dependencies from a novel perspective. To this end, we propose a Multi-Agent Reinforcement Learning system framework based on Fourier Topological Space from the foundational level. This method involves transforming each agent’s value function into the frequency domain for analysis. Additionally, we design a lightweight weight calculation method based on historical topological relationships in the Fourier topological space. This addresses issues of instability and poor reproducibility in attention weights, along with various other interpretability challenges. The effectiveness of this method is validated through experiments in complex environments such as the StarCraft Multi-Agent Challenge (SMAC) and Google Football. Furthermore, in the Non-monotonic Matrix Game, our method successfully overcame the limitations of non-monotonicity, further proving its wide applicability and superiority. On the application level, the proposed algorithm is also applicable to various multi-agent system domains, such as robotics and factory robotic arm control. The algorithm can control each joint in a coordinated manner to accomplish tasks such as enabling a robot to stand upright or controlling the movements of robotic arms.
{"title":"Multi-agent reinforcement learning system framework based on topological networks in Fourier space","authors":"Licheng Sun, Ao Ding, Hongbin Ma","doi":"10.1016/j.asoc.2025.112986","DOIUrl":"10.1016/j.asoc.2025.112986","url":null,"abstract":"<div><div>Currently, multi-agent reinforcement learning (MARL) has been applied to various domains such as communications, network management, power systems, and autonomous driving, showcasing broad application scenarios and significant research potential. However, in complex decision-making environments, agents that rely solely on temporal value functions often struggle to capture and extract hidden features and dependencies within long sequences in multi-agent settings. Each agent’s decisions are influenced by a sequence of prior states and actions, leading to complex spatiotemporal dependencies that are challenging to analyze directly in the time domain. Addressing these challenges requires a paradigm shift to analyze such dependencies from a novel perspective. To this end, we propose a Multi-Agent Reinforcement Learning system framework based on Fourier Topological Space from the foundational level. This method involves transforming each agent’s value function into the frequency domain for analysis. Additionally, we design a lightweight weight calculation method based on historical topological relationships in the Fourier topological space. This addresses issues of instability and poor reproducibility in attention weights, along with various other interpretability challenges. The effectiveness of this method is validated through experiments in complex environments such as the StarCraft Multi-Agent Challenge (SMAC) and Google Football. Furthermore, in the Non-monotonic Matrix Game, our method successfully overcame the limitations of non-monotonicity, further proving its wide applicability and superiority. On the application level, the proposed algorithm is also applicable to various multi-agent system domains, such as robotics and factory robotic arm control. The algorithm can control each joint in a coordinated manner to accomplish tasks such as enabling a robot to stand upright or controlling the movements of robotic arms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112986"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.asoc.2025.112991
Ran Liu , Hu-Chen Liu , Qi-Zhen Zhang , Hua Shi
Nowadays, occupational health and safety risk assessment (OHSRA) has gained more importance since occupational hazards can cause loss of life, injuries, delays, and cost overruns in an organization. The OHSRA is a critical activity for identifying, analyzing and reducing the potential occupational hazards arising from workplace for corrective actions. In this study, a new OHSRA model is proposed for the risk assessment and classification of occupational hazards by utilizing the criteria importance through inter-criteria correlation (CRITIC) method and three-way decision (TWD). First, the 2-tuple linguistic variables are utilized to express the complex and uncertain risk assessments of occupational hazards provided by experts. Second, an extended CRITIC method is employed to compute the weights of risk criteria by considering their interactions. Then the TWD is improved to determine the risk classifications of occupational hazards by considering their correlations. Finally, a practical case in the healthcare industry is provided to illustrate the feasibility and strengths of the proposed OHSRA model. The results show that the proposed OHSRA model can generate more credible risk classifications of occupational hazards and offer a flexible way for analyzing the risk of occupational hazards.
{"title":"A three-way decision-based model for occupational risk assessment and classification in the healthcare industry","authors":"Ran Liu , Hu-Chen Liu , Qi-Zhen Zhang , Hua Shi","doi":"10.1016/j.asoc.2025.112991","DOIUrl":"10.1016/j.asoc.2025.112991","url":null,"abstract":"<div><div>Nowadays, occupational health and safety risk assessment (OHSRA) has gained more importance since occupational hazards can cause loss of life, injuries, delays, and cost overruns in an organization. The OHSRA is a critical activity for identifying, analyzing and reducing the potential occupational hazards arising from workplace for corrective actions. In this study, a new OHSRA model is proposed for the risk assessment and classification of occupational hazards by utilizing the criteria importance through inter-criteria correlation (CRITIC) method and three-way decision (TWD). First, the 2-tuple linguistic variables are utilized to express the complex and uncertain risk assessments of occupational hazards provided by experts. Second, an extended CRITIC method is employed to compute the weights of risk criteria by considering their interactions. Then the TWD is improved to determine the risk classifications of occupational hazards by considering their correlations. Finally, a practical case in the healthcare industry is provided to illustrate the feasibility and strengths of the proposed OHSRA model. The results show that the proposed OHSRA model can generate more credible risk classifications of occupational hazards and offer a flexible way for analyzing the risk of occupational hazards.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112991"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号