Publicly verifiable sealed electronic auctions are proposed. The schemes enjoy the following advantages. They require no special trusted parties. After bid opening phase, only the winning price is revealed and the rel...
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Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the trade-off between generalization and performance prevents models from performing as they should. Model performa...
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Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the trade-off between generalization and performance prevents models from performing as they should. Model performance decreases when trained on multiple degraded images due to the inter-class and intra-class imbalances in degradation prediction, which consists of degradation sampling and estimation. The inter-class imbalance in degradation estimation causes inaccurate estimates, leading to severe artifacts in images. The intra-class imbalance in degradation sampling causes a long-tail problem, leading to model collapse and satisfactory results only in specific applications. To tackle these challenges, we propose adaptive alignment contrastive learning (AACL), which includes adaptive degradation sampling (ADS) and \(\sigma\)-alignment. ADS utilizes nonlinear sampling by weighting the parameters of the degradation process for training uniformly degraded images, avoiding the long-tail problem. \(\sigma\)-alignment controls the standard deviation among positive samples, we identify a subset with small degraded distance, which aids contrastive learning in extracting representations more effectively. We extend AACL to several CNN-based and Transformer-based methods by coming up with a 6\(\times\)6 fair architecture with degradation representation fusion block (DRFB) and degradation representation fusion group (DRFG). DRFB and DRFG are designed for degradation representation fusion and image reconstruction, respectively. We evaluate on 6 types of degradation, and the improvement experiments on synthesized images show that our method balances performance and generalization, and is applicable to networks with different architectures. The comparison experiments show that our improved methods achieve promising results compared to SOTA methods. Code is available at: https://***/para999/AACL.
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial...
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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, scienceDirect, Springer, Web of science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.
Dynamic multimodal optimization problems (DMMOPs) demand algorithms capable of swiftly locating and tracking multiple optimal solutions over time. The primary challenge lies in controlling the population diversity to ...
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Dynamic multimodal optimization problems (DMMOPs) demand algorithms capable of swiftly locating and tracking multiple optimal solutions over time. The primary challenge lies in controlling the population diversity to facilitate effective exploration, all within the limitation of computational resources between consecutive environmental changes. In this paper, we study the utilization of density information derived from both current and historical populations to enhance exploration. First, for each active sub-population, we construct a density landscape based on the distribution of concurrently active sub-populations, and establish dominance relationships between candidate solutions in the sub-population based on density and fitness values, directing this sub-population towards exploring low-density promising areas. Then, for each converged sub-population, we construct a density landscape based on the distribution of sub-populations that have historically become extinct, guiding the restart of this sub-population in low-density unexploited areas. Finally, we develop a comprehensive framework of density-assisted evolutionary algorithm (DAEA), which encompasses density-assisted search and restart, also combined with initialization. Moreover, we employ prediction and memory strategies to enhance the performance of DAEA in dynamic environments. Notably, the algorithm relies on an external monitor to detect environmental changes and trigger the dynamic response strategy. DAEA is tested on the CEC’2022 dynamic multimodal optimization benchmark suite, and is compared against several state-of-the-art dynamic multimodal optimization algorithms. The experimental results demonstrate the competitiveness of DAEA in handling DMMOPs. Additionally, experimental results from the berth allocation problem further confirm the applicability of DAEA to real-world dynamic multimodal optimization tasks.
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