Permissioned blockchains are gaining traction for enterprise applications due to their enhanced security and performance characteristics. However, they face significant scalability challenges, particularly in environments with high concurrency and diverse workload demands. In this paper, we present a novel architecture for scaling permissioned blockchains by disaggregating Log-Structured Merge (LSM) trees between execution and storage nodes. Our approach leverages the inherent structure of LSM-trees to efficiently handle write-intensive workloads by separating recent updates in execution nodes and long-term data storage in storage nodes. This design supports elastic scaling of both compute and memory resources, enabling independent and efficient resource utilization. We introduce the concept of Semi-stateful Nodes, which balance the benefits of fully-stateful and stateless nodes. This approach reduces communication overhead during parallel transaction execution while simultaneously improving scalability. Our architecture also includes a parallel transaction execution algorithm to minimize Cross-Shard Transactions (CSTs) and enhance overall system performance. Through our experimentation, we demonstrate that our system achieves significant performance improvements, including up to $15 \times$ increase in throughput compared to traditional architectures. The results indicate that our approach significantly reduces the overhead and time consumption during scalability operations in permissioned blockchains, making it a system that can achieve high scalability and good performance at a low cost.

Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. This requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallel processing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. The idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless Scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations.

Semantic segmentation is one of the most important tasks in the field of remote sensing. As the spatial resolution increases, the remote sensing images can capture more detailed information and make hierarchical semantic interpretation possible. However, hierarchical semantic segmentation encounters high heterogeneity not only within the intra-layer classes but also among inter-layer classes. It brings challenges to semantic segmentation methods such as the convolutional neural network (CNN). In this article, a hierarchical self-learning knowledge inference model (HSKIM) based on the Markov random field (MRF) model is proposed for hierarchical semantic segmentation of remote sensing images. The HSKIM model introduces a new framework that integrates the advantages of CNN-based data feature learning and MRF-based hierarchical semantic inference. It contains three modules: data learning module ($\boldsymbol {D}$), inference units generation module ($\boldsymbol {I}$), and self-learning knowledge inference module ($\boldsymbol {S}$). The module$\boldsymbol {D}$uses CNN to learn specific data features layer by layer and extract preliminary geographical objects as the initial results. The module I refines the geographical objects using a novel boundary-preservation trick to generate more accurate inference units with clear geographical meaning. The module S introduces a hierarchical object-based MRF model to implement semantic inference among intra-layer and inter-layer inference units, guided by the spatial interactions and geographical criteria. This module can self-learn and update the relationship between classes iteratively and provide the final result. Experiments on the GID dataset with hierarchical classes, alongside 12 state-of-the-art CNN-based methods, validate the effectiveness and robustness of the proposed HSKIM model. The code of this article is available athttps://github.com/iichengzi/HSKIM.

Neural language models have achieved superior performance.However, these models also suffer from the pathology of overconfidence in the out-of-distribution examples, potentially making the model difficult to interpret and making the interpretation methods fail to provide faithful attributions.In this paper, we explain the model pathology from the view of sentence representation and argue that the counter-intuitive bias degree and direction of the out-of-distribution examples' representation cause the pathology.We propose a Contrastive learning regularization method using Adversarial examples for Alleviating the Pathology (ConAAP), which calibrates the sentence representation of out-of-distribution examples.ConAAP generates positive and negative examples following the attribution results and utilizes adversarial examples to introduce direction information in regularization.Experiments show that ConAAP effectively alleviates the model pathology while slightly impacting the generalization ability on in-distribution examples and thus helps interpretation methods obtain more faithful results.

Microservice architecture is embraced by a growing number of enterprises due to the benefits of modularity and flexibility. However, being composed of numerous interdependent microservices, it is prone to cascading failures and afflicted by the arising problem of troubleshooting, which entails arduous efforts to identify the root cause node and ensure service availability. Previous works use call graph to characterize causality relation-ships of microservices but not completely or comprehensively, leading to an insufficient search of potential root cause nodes and consequently poor accuracy in culprit localization. In this paper, we propose ImpactTracer to address the above problems. ImpactTracer builds impact graph to provide a com-plete view of fault propagation in microservices and uses a novel backward tracing algorithm that exhaustively traverses the impact graph to identify the root cause node accurately. Extensive experiments on a real-world dataset demonstrate that ImpactTracer is effective in identifying the root cause node and outperforms the state-of-the-art methods by at least 72%, significantly facilitating troubleshooting in microservices.

Convolutional neural network (CNN) has been widely used in semantic segmentation for remote sensing images, and it has achieved great success. Due to the diversity of the spatial distribution of terrestrial objects in remote sensing images, it is difficult to effectively learn general geographical laws and apply them to a specific image. To introduce geographical knowledge into the CNN model more effectively, a self-learning-update CNN model (SLU-CNN) is proposed in this letter. It learns the representation of specific spatial dependence among different objects according to the CNN result, and then incorporates it with the CNN result to make semantic inference available. The proposed method mainly involves two modules. First, geographical objects generated from the CNN result are used as inference units. Second, the spatial dependence between inference units is learned to build a specific adaptive geographical relationship. And then, it is embedded as an adaptive penalty term into an object-based Markov random field model to achieve the collaboration between the CNN result and the semantic inference. Our method provides a general data-knowledge dual-driven framework for the deep neural network. Experiments of the GID and Sentinel-2 datasets validate the effectiveness of the proposed method by comparing it with different state-of-the-art CNN methods.

Deep learning methods have been widely studied in the semantic segmentation field of the remote sensing image. Training images play an important role in these methods; however, each training image usually contains not only the generalization information of each land category but also the specific interclass context between different categories. The specific interclass context prevents deep learning methods from focusing on generalization information learning during training and limits the performance on different data distributions. This article proposes a generalization sampling learning method of deep convolutional neural network (GSL-CNN) to emphasize generalization information learning for the semantic segmentation of remote sensing images. The proposed method develops a new CBR sampling strategy that contains three modules: category grouping ($\mathbf {\boldsymbol {C}}$), basic unit extraction ($\mathbf {\boldsymbol {B}}$), and random combination ($\mathbf {\boldsymbol {R}}$). Module$\mathbf {\boldsymbol {C}}$collects each land category map and strips away the specific interclass context from the raw annotated image. Module$\mathbf {\boldsymbol {B}}$extracts basic units with different granularities from each land category map, and each basic unit can keep the generalization information of this category. Module$\mathbf {\boldsymbol {R}}$aims to enhance the robustness against different data distributions by randomly picking basic units of different categories and randomly generating their interclass context. The new GSL-CNN method integrates the CBR sampling strategy with the convolutional neural network (CNN) model for semantic segmentation. Experiments on different remote sensing datasets and 15 state-of-the-art CNN models validated that the proposed method has the potential of improving the generalization ability of the CNN method from a sampling perspective.

This paper investigates the parameter optimization problem of membership functions for fuzzy-model-based control under imperfect premise matching. A novel frequency domain algorithm which can clearly relate the membership function parameters to the targeted control performance is developed, and consequently an optimization approach to the membership function parameters is then established based on the resulting nonlinear characteristic output spectrum function (nCOS). Compared to traditional search-based optimization approach, this method can give a more detailed result with less time consuming and an in-depth understanding of nonlinear influence rather than just optimal results. With this novel method, performance of the fuzzy-model-based controller is further enhanced. Finally, the fuzzy membership functions optimization method is applied to nonlinear systems to obtain improved system performance.