Information Sciences最新文献_第9页

Probability completion and consensus reaching based on kernel density estimation for incomplete probabilistic linguistic multi-attribute group decision making 基于核密度估计的不完备概率语言多属性群体决策的概率补全与共识达成

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-25 DOI: 10.1016/j.ins.2025.122207

Jinglin Xiao, Xinxin Wang, Ying Gao, Zeshui Xu

{"title":"Probability completion and consensus reaching based on kernel density estimation for incomplete probabilistic linguistic multi-attribute group decision making","authors":"Jinglin Xiao, Xinxin Wang, Ying Gao, Zeshui Xu","doi":"10.1016/j.ins.2025.122207","DOIUrl":"10.1016/j.ins.2025.122207","url":null,"abstract":"<div><div>Multi-attribute group decision-making is a hot topic in the study of uncertain decision-making processes, particularly when linguistic variables are employed to express evaluative information. However, incomplete information often arises due to cognitive disparities among decision-makers and their diverse evaluation preferences. To address these challenges, this paper proposes a novel multi-attribute group decision-making method that incorporates incomplete probabilistic linguistic term sets and considers nonlinear semantics. First, we introduce an innovative application of kernel density estimation to complete incomplete term sets, employing Gaussian kernel functions to model the nonlinear perceptual variations of decision-makers. The bandwidth and skewness parameters are utilized to reflect perceptual granularity and evaluation bias, respectively. Second, we modify the Kolmogorov-Smirnov distance measure and propose a novel comparison rule tailored to probabilistic linguistic term sets with semantic imbalance, enhancing the computational accuracy of attribute weight determination. Furthermore, two optimization models are developed to determine the bandwidths for completing incomplete information and aggregating individual evaluations. A dynamic adjustment mechanism is introduced to support decision-maker interaction in achieving consensus. The effectiveness of the proposed methods is demonstrated through a case study on gas meter selection. Sensitivity analysis and comparative experiments highlight its superior performance in handling incomplete information and managing uneven semantics.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122207"},"PeriodicalIF":8.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881408","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}

引用次数: 0

Multimodal cross-domain contrastive learning: A self-supervised generative and geometric framework for visual perception 多模态跨域对比学习：视觉感知的自监督生成和几何框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-25 DOI: 10.1016/j.ins.2025.122239

S. Muhammad Ahmed Hassan Shah , Atif Rizwan , Muhammad Sardaraz , Muhammad Tahir , Nagwan Abdel Samee , Mona M. Jamjoom

{"title":"Multimodal cross-domain contrastive learning: A self-supervised generative and geometric framework for visual perception","authors":"S. Muhammad Ahmed Hassan Shah , Atif Rizwan , Muhammad Sardaraz , Muhammad Tahir , Nagwan Abdel Samee , Mona M. Jamjoom","doi":"10.1016/j.ins.2025.122239","DOIUrl":"10.1016/j.ins.2025.122239","url":null,"abstract":"<div><div>Self-Supervised Contrastive Representation Learning (SSCRL) has gained significant attention for its ability to learn meaningful representations from unlabeled data by leveraging contrastive learning principles. However, existing SSCRL approaches struggle with effectively handling heterogeneous data formats, particularly discrete and binary representations, limiting adaptability across multiple domains. This limitation hinders the generalization of learned representations, especially in applications requiring structured feature encoding and robust cross-domain adaptability. To address this, we propose the Modular QCB Learner, a novel algorithm designed to enhance representation learning for heterogeneous data types. This framework builds upon SSCRL by incorporating a Real Non-Volume Preserving transformation to optimize continuous representations, ensuring alignment with a Gaussian distribution. For discrete representation learning, vector quantization is utilized along with a Poisson distribution, while binary representations are modeled through nonlinear transformations and the Bernoulli distribution. Multi-Domain Mixture Optimization (MiDO) is introduced to facilitate joint optimization of different representation types by integrating multiple loss functions. To evaluate effectiveness, synthetic data generation is performed on extracted representations and compared with baselines. Experiments on CIFAR-10 confirm the Modular QCB Learner improves representation quality, demonstrating robustness across diverse data domains with applications in synthetic data generation, anomaly detection and multimodal learning.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122239"},"PeriodicalIF":8.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881411","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}

引用次数: 0

Biometric untargeted attacks: A case study on near-collisions 生物识别非目标攻击：近距离碰撞的案例研究

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-24 DOI: 10.1016/j.ins.2025.122217

Axel Durbet , Paul-Marie Grollemund , Kevin Thiry-Atighehchi

{"title":"Biometric untargeted attacks: A case study on near-collisions","authors":"Axel Durbet , Paul-Marie Grollemund , Kevin Thiry-Atighehchi","doi":"10.1016/j.ins.2025.122217","DOIUrl":"10.1016/j.ins.2025.122217","url":null,"abstract":"<div><div>Biometric recognition systems are now integral to many authentication and identification processes, prompting the need to understand their resilience under various attack scenarios. In this work, we analyze the security of such systems against <em>untargeted attacks</em>, where an adversary aims to impersonate any user without focusing on a specific target. Assuming a minimal leakage model—where only a binary acceptance or rejection is revealed—we derive upper and lower bounds on the attack complexity as functions of the template size, decision threshold, and database size. Our contributions apply to templates following a uniform distribution, such as randomized biometric templates or those derived from high-entropy secret sources. Many biometric template protection schemes, such as BioHashing or random projection-based transformations, combine biometric data with a high-entropy secret (e.g., a password or token). This combination is designed to produce pseudo-random outputs, making the uniform distribution a reasonable assumption for the transformed template space. As a result, our analysis covers two-factor authentication systems where biometrics are combined with a stored random secret or strong password. We use probabilistic modeling to assess the theoretical security limits of such systems. We investigate two practical attack scenarios: naive outsiders submitting random guesses, and multiple simultaneous attackers increasing the overall trial rate. We also introduce the notion of <em>weak near-collisions</em> to evaluate the risk of mutual impersonation due to close templates in the database. Our theoretical analysis is validated on real biometric datasets (LFW and FVC) using transformation schemes such as BioHashing. Finally, we provide practical recommendations for configuring system parameters to mitigate untargeted attacks and near-collision risks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122217"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918482","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}

引用次数: 0

Reward shaping in DRL: A novel framework for adaptive resource management in dynamic environments DRL中的奖励形成：动态环境下自适应资源管理的新框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-24 DOI: 10.1016/j.ins.2025.122238

Mario Chahoud , Hani Sami , Rabeb Mizouni , Jamal Bentahar , Azzam Mourad , Hadi Otrok , Chamseddine Talhi

{"title":"Reward shaping in DRL: A novel framework for adaptive resource management in dynamic environments","authors":"Mario Chahoud , Hani Sami , Rabeb Mizouni , Jamal Bentahar , Azzam Mourad , Hadi Otrok , Chamseddine Talhi","doi":"10.1016/j.ins.2025.122238","DOIUrl":"10.1016/j.ins.2025.122238","url":null,"abstract":"<div><div>In edge computing environments, efficient computation resource management is crucial for optimizing service allocation to hosts in the form of containers. These environments experience dynamic user demands and high mobility, making traditional static and heuristic-based methods inadequate for handling such complexity and variability. Deep Reinforcement Learning (DRL) offers a more adaptable solution, capable of responding to these dynamic conditions. However, existing DRL methods face challenges such as high reward variability, slow convergence, and difficulties in incorporating user mobility and rapidly changing environmental configurations. To overcome these challenges, we propose a novel DRL framework for computation resource optimization at the edge layer. This framework leverages a customized Markov Decision Process (MDP) and Proximal Policy Optimization (PPO), integrating a Graph Convolutional Transformer (GCT). By combining Graph Convolutional Networks (GCN) with Transformer encoders, the GCT introduces a spatio-temporal reward-shaping mechanism that enhances the agent's ability to select hosts and assign services efficiently in real time while minimizing the overload. Our approach significantly enhances the speed and accuracy of resource allocation, achieving, on average across two datasets, a 30% reduction in convergence time, a 25% increase in total accumulated rewards, and a 35% improvement in service allocation efficiency compared to standard DRL methods and existing reward-shaping techniques. Our method was validated using two real-world datasets, MOBILE DATA CHALLENGE (MDC) and Shanghai Telecom, and was compared against standard DRL models, reward-shaping baselines, and heuristic methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122238"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881410","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}

引用次数: 0

Diversity-induced fuzzy clustering with Laplacian regularization 基于拉普拉斯正则化的多样性诱导模糊聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-24 DOI: 10.1016/j.ins.2025.122225

Yunlong Gao , Qinting Wu , Zhenghong Xu , Jinyan Pan , Guifang Shao , Qingyuan Zhu , Feiping Nie

{"title":"Diversity-induced fuzzy clustering with Laplacian regularization","authors":"Yunlong Gao , Qinting Wu , Zhenghong Xu , Jinyan Pan , Guifang Shao , Qingyuan Zhu , Feiping Nie","doi":"10.1016/j.ins.2025.122225","DOIUrl":"10.1016/j.ins.2025.122225","url":null,"abstract":"<div><div>Fuzzy clustering is a fundamental technique in unsupervised learning for exploring data structures. However, fuzzy c-means (FCM), as a representative fuzzy clustering algorithm, performs relatively poorly when handling noisy data and outliers since it only considers global data characteristics while ignoring the local information. Additionally, FCM overlooks data diversity, making it difficult to handle complex data and leading to cluster center overlapping. To address these challenges, this paper proposes a novel approach called diversity-induced fuzzy clustering with Laplacian regularization (DiFCMLR). DiFCMLR incorporates Hilbert-Schmidt Independence Criterion (HSIC) to maximize the independence among clusters, thereby enhancing clustering diversity. In addition, DiFCMLR introduces Laplacian regularization to consider the local information of data and determine the affinity relationship between samples. Furthermore, it corrects the Euclidean distance between samples, thereby reducing the impact of the normal distribution prior assumption of FCM and improving the applicability of algorithm to complex data or size-imbalance problems. During the optimization, DiFCMLR utilizes iterative reweighting and the alternating direction method of multipliers, which enhance robustness against noise and outliers and achieve faster convergence towards better solutions. The effectiveness of DiFCMLR is confirmed through theoretical analysis and experimental evaluation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122225"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879129","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}

引用次数: 0

Direction-aware convolutional autoencoder based on positional encoding for one-dimensional anomaly detection 基于位置编码的一维异常检测方向感知卷积自编码器

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-24 DOI: 10.1016/j.ins.2025.122227

Qien Yu , Qiong Chang , Tinghui Ouyang , Takio Kurita , Ran Dong

{"title":"Direction-aware convolutional autoencoder based on positional encoding for one-dimensional anomaly detection","authors":"Qien Yu , Qiong Chang , Tinghui Ouyang , Takio Kurita , Ran Dong","doi":"10.1016/j.ins.2025.122227","DOIUrl":"10.1016/j.ins.2025.122227","url":null,"abstract":"<div><div>One-dimensional anomaly detection remains challenging owing to existing methods inadequately modeling interactions between widely separated elements and neglecting local interaction patterns. To address these limitations, this study proposes a space transformation-based direction-aware convolutional autoencoder framework using positional encoding (SDP-CAE) for one-dimensional anomaly detection. Unlike previous approaches, SDP-CAE uniquely applies multiple space-filling curves to transform one-dimensional data into two-dimensional data, effectively capturing rich local feature interactions and overcoming the limitations posed by traditional one-dimensional modeling. Space-filling curves can enhance the interactions among different elements of one-dimensional data to enrich local feature patterns in a two-dimensional space. Furthermore, we introduce an asymmetric two-stream convolutional autoencoder architecture that employs horizontal and vertical convolution operations to explicitly capture direction-specific interactions within the transformed data. This architecture significantly improves anomaly detection by modeling interactions that are sensitive to the local spatial context. Positional encoding (PE), employed as an auxiliary mechanism, enhances the representation of high-frequency details to further prompt the sensitivity of the model to subtle anomalies. Extensive experiments conducted on benchmark datasets demonstrate that our method significantly outperforms state-of-the-art methods, clearly validating the effectiveness and necessity of the proposed space transformation and direction-aware modeling mechanisms.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122227"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913066","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}

引用次数: 0

DGDO-BiLSTM: Dominance Guiding Defense Optimization-based Bidirectional Long Short-Term Memory for Sentiment Analysis using Multilingual text and emojis DGDO-BiLSTM：基于优势引导防御优化的双向长短期记忆，用于多语言文本和表情符号情感分析

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-24 DOI: 10.1016/j.ins.2025.122193

MOHD MISKEEN ALI, SYED MOHAMED E

{"title":"DGDO-BiLSTM: Dominance Guiding Defense Optimization-based Bidirectional Long Short-Term Memory for Sentiment Analysis using Multilingual text and emojis","authors":"MOHD MISKEEN ALI, SYED MOHAMED E","doi":"10.1016/j.ins.2025.122193","DOIUrl":"10.1016/j.ins.2025.122193","url":null,"abstract":"<div><div>Sentiment analysis plays an essential role in identifying someone’s emotional state, opinion, and perspectives, which in turn effectually utilized for obtaining product information and strategic decision-making process. However, the sentiment analysis exhibits some challenges, like performance degradation, difficult to categorize sentiment polarity, interpretation issues, and complexity problems. To resolve these drawbacks, proposed a Dominance Guiding Defense Optimization based Bidirectional Long short-term memory classifier (DGDO-BiLSTM) to evaluate the sentiment polarity of multilingual text and emoji classification. In this context, the DGDO-BiLSTM utilized Multilingual text and emoji-based review information to recognize the sentiments and attain certain information about the products. Further, the DGDO algorithm is utilized for enhancing the ability and efficacy of the model with the combination of Hippopotamus, and Walrus optimization algorithms, which effectually reduced the local optima issues and achieved an accurate convergence rate significantly. Meanwhile, the hybrid angular loss function is incorporated with the developed model to attain the superiority property and discriminative power that effectually minimizes the error rate gradually. Based on this effectiveness, the DGDO-BiLSTM model achieves better performance as 82.04 %, 95.31 %, 95.37 %, and 95.70 %, for negative predictive value (NPV), Accuracy. F1-Score, and Positive Predictive Value (PPV).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122193"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899361","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}

引用次数: 0

Automatic consensus models to balance consensus cost, consistency level and consensus degree with attitudinal trust mechanism 利用态度信任机制平衡共识成本、一致性水平和共识程度的自动共识模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122222

Yaya Liu , Yue Wang , Rosa M. Rodríguez , Zhen Zhang , Luis Martínez

{"title":"Automatic consensus models to balance consensus cost, consistency level and consensus degree with attitudinal trust mechanism","authors":"Yaya Liu , Yue Wang , Rosa M. Rodríguez , Zhen Zhang , Luis Martínez","doi":"10.1016/j.ins.2025.122222","DOIUrl":"10.1016/j.ins.2025.122222","url":null,"abstract":"<div><div>In light of the inevitable consensus costs incurred by preference adjustments of decision makers during the consensus reaching process (CRP), multiple minimum cost driven consensus models have been developed, which either prioritize the attainment of a high consensus degree, or focus on the consistency maintenance of individual opinions. However, the strategic equilibrium of consensus cost, consistency level and consensus degree, which shapes the cogency of the decision-making outcome, becomes one of the main challenges which should be overcome in the CRP. To address this scenario, this study proposes three novel trust attitude-based consensus models to balance these three factors. These consensus models are implemented through optimization models, tailored to distinct primary objectives, resulting in outputs that encompass attitudinal parameters to realize the balance of consensus cost, consistency level and consensus degree. Correspondingly, the proposed consensus models have been applied to solve severe air pollution emergency management decision problems. Comparative analysis with existing works is provided to show the validity of the proposed models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122222"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899359","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}

引用次数: 0

An analysis on the effects of evolving the Monte Carlo tree search upper confidence for trees selection policy on unimodal, multimodal and deceptive landscapes 演化蒙特卡罗树搜索上置信度对单峰、多峰和欺骗性景观树木选择策略的影响分析

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122226

Edgar Galván , Fred Valdez Ameneyro

{"title":"An analysis on the effects of evolving the Monte Carlo tree search upper confidence for trees selection policy on unimodal, multimodal and deceptive landscapes","authors":"Edgar Galván , Fred Valdez Ameneyro","doi":"10.1016/j.ins.2025.122226","DOIUrl":"10.1016/j.ins.2025.122226","url":null,"abstract":"<div><div>Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing a crucial role. A particularly effective selection policy in MCTS is the Upper Confidence Bounds for Trees (UCT). While MCTS/UCT generally performs well, there may be variants that outperform it, leading to efforts to evolve selection policies for use in MCTS. However, these efforts have often been limited in their ability to demonstrate when these evolved policies might be beneficial. They frequently rely on single, poorly understood problems or on new methods that are not fully comprehended. To address these limitations, we use three evolutionary-inspired methods: Evolutionary Algorithm (EA)-MCTS, Semantically-inspired EA (SIEA)-MCTS as well as Self-adaptive (SA)-MCTS, which evolve online selection policies to be used in place of UCT. We compare these three methods against five variants of the standard MCTS on ten test functions of varying complexity and nature, including unimodal, multimodal, and deceptive features. By using well-defined metrics, we demonstrate how the evolution of MCTS/UCT can yield benefits in multimodal and deceptive scenarios, while MCTS/UCT remains robust across all functions used in this work.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122226"},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143885963","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}

引用次数: 0

Geodesic fuzzy rough sets based on overlap functions and its applications in feature extraction 基于重叠函数的测地线模糊粗糙集及其在特征提取中的应用

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-04-23 DOI: 10.1016/j.ins.2025.122224

Chengxi Jian , Junsheng Qiao , Shan He

引用次数: 0