Complexity最新文献

This paper investigates the input delay analysis and H_∞ control problem for networked control systems with finite-time stochastic boundedness (FTSB). First, a novel control scheme is used to handle the Markovian jump parameters, and an event-triggered rule is introduced to a networked control system with FTSB, which can ensure the control performance of the system and effectively improve the resource utilization of the networked control system. Simultaneously, a more accurate expression for input delay compared to traditional methods is obtained. Then, the sufficient condition for the networked control system to have FTSB is derived. Additionally, an H_∞ state feedback controller for the stochastic networked control system with FTSB performance is obtained. Finally, an illustrative example is provided to verify the effectiveness of the method proposed in this paper, especially the good control effect of the H_∞ state feedback controller.

While deep learning–based layered feature extraction methods have achieved remarkable success, their reliance on large-scale annotated datasets limits their applicability in small-sample scenarios. To address this challenge, a novel feature extraction method has been proposed within the traditional image processing framework. This technique is specifically designed for scenarios with limited training data, aiming to enhance performance and efficiency in such conditions. Inspired by image separation algorithms and multifeature fusion strategies, the proposed approach employs guided filtering combined with the Sobel gradient operator to decompose the original finger vein image into a foreground layer and a background layer. Texture features are extracted from the foreground layer, while structural features are derived from the background layer, resulting in two complementary feature maps that capture multidimensional information. These maps are then encoded into a unified one-dimensional feature vector using block-wise histogram descriptors, which enhances feature representation and ensures translation invariance. By separately extracting and effectively fusing multilevel features, the method significantly alleviates the impact of noise on feature extraction and discriminative performance. Without relying on large-scale data, it improves the robustness and practicality of finger vein recognition. Extensive experiments on public datasets validate the effectiveness and generalization capability of the proposed approach.

RETRACTION: Y. Xu and X. Liu, “Interactive Algorithms in Complex Image Processing Systems Based on Big Data,” Complexity 2020 (2020): 5929584, https://doi.org/10.1155/2020/5929584.

The above article, published online on 05 May 2020 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal’s Chief Editor, Hiroki Sayama; and John Wiley & Sons Ltd.

The retraction has been agreed due to the authors finding that the content of the article is considered unreliable.

Real-parameter single-objective optimization has become a prominent focus within artificial intelligence in recent years. Among population-based metaheuristics, differential evolution (DE) and covariance matrix adaptation evolution strategy (CMA-ES) have consistently demonstrated strong performance. However, the difficulty of solving optimization problems increases exponentially with the dimensionality of the objective function, resulting in a corresponding rise in the number of required function evaluations. To address this challenge, a novel algorithm—the Gaining-Sharing Knowledge (GSK)–based algorithm—has emerged as a promising solution. GSK’s development trajectory currently resembles the early stages of DE. Nevertheless, further enhancements are necessary to unlock its full potential. In this paper, we propose an evolutionary external archive (EEA) for GSK and its variants, inspired by the external archive mechanism used in DE. The proposed EEA integrates individuals from both the current population and the archive into the evolutionary process. To promote diversity, we apply an evolutionary procedure based on CMA-ES within the archive and exclude individuals from the archive if identical counterparts exist in the current generation. We evaluate our approach using three benchmark test suites from the Congress on Evolutionary Computation (CEC) and real-world optimization problems from CEC 2011. Our experimental analysis compares GSK and its variants with and without the EEA. Results show that the EEA significantly improves the performance of GSK and its variants. Consequently, the GSK variant, AGSK, with the EEA is selected for further comparison against benchmark algorithms. Experimental results confirm that our proposed method is highly competitive.

RETRACTION: C. Jia, J. Ma, Q. Liu, Y. Zhang, and H. Han, “Linkboost: A Link Prediction Algorithm to Solve the Problem of Network Vulnerability in Cases Involving Incomplete Information,” Complexity (2020): 7348281, https://doi.org/10.1155/2020/7348281.

The above article, published online on 08 April 2020 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by agreement between the authors; the journal Editor-in-Chief, Dr. Gonzalo Farias; and John Wiley & Sons Ltd.

The retraction has been agreed due to errors noted by the authors in the network attack experiments performed. Specifically, the proportion of attacked/removed nodes was miscalculated, leading to errors in the results and conclusions presented in the article.

The authors apologise and agree to the retraction.

Organizational systems are inherently complex, with decision-making processes influenced by interactions between individual perceptions, social norms, and systemic structures. In project management, unconscious gender biases represent a hidden layer of complexity, subtly shaping evaluations of competences and leadership potential. This study explores how unconscious gender biases emerge as part of the complex dynamics within organizational decision-making systems. It investigates the interplay between individual cognitive biases and systemic factors in defining what constitutes a “good project manager” and how these biases influence hiring and promotion decisions. Using a sample of project management professionals, we applied noise-based reverse correlation (NBRC) to reveal participants’ unconscious mental representations of an ideal project manager by generating faces that best represented project managers. The study then compared these representations with conscious competence evaluations based on the International Project Management Association (IPMA) Competence Baseline, incorporating statistical methods to identify patterns of bias and preference. The findings reveal that unconscious gender biases align with entrenched stereotypes, favoring traits associated with masculinity in leadership roles. However, when consciously evaluating specific competences, participants displayed preferences that challenged these biases, suggesting a misaligned relationship between unconscious perceptions and explicit decisions. Unconscious gender bias operates as a hidden variable within the complex system of organizational decision-making, creating feedback loops that reinforce traditional stereotypes. Understanding these dynamics requires a system-level approach that integrates cognitive and organizational perspectives. Our findings highlight the need for interventions that address both individual biases and structural factors to foster equitable decision-making in complex organizational environments.