Different BFT (Byzantine Fault Tolerance) consensus algorithms have distinct characteristics and their optimal use cases. Rarely does a protocol demonstrate outstanding performance across different environments. Sometimes, systems need to switch consensus algorithms to adapt to different environments. This paper aims to investigate how to achieve seamless consensus algorithm switching without system downtime and compromising the security and consistency of the original consensus module. To address this issue, we propose Cox, a reliable online switching protocol for BFT consensus algorithms, and design a corresponding recovery sub-protocol for the lagging nodes. The experimental results indicate that, under the assumption of a partially synchronous network, Cox can ensure both the liveness and safety of the consensus module, and achieve reliable switching in milliseconds.

Various salient object detection (SOD) approaches have been developed to identify visually attractive objects in scenes captured in RGB-D (RGB and depth) images. High-level features often provide abstract semantics, and low-level features include more details such as textures and spatial structures. Hence, effectively fusing multimodal information from different levels has become a major area of development. We propose a multiple-strategy network (MSNet) with bidirectional fusion for RGB-D SOD that incorporates multilevel feature fusion and cross-modal aggregation into a multisupervised framework. We first use a multiple-strategy fusion module to transmit high-level semantic features along a top-down progressive pathway to generate a series of appearance features. Thereafter, a self-refinement module further refines and optimizes the saliency map. Furthermore, a depth optimization module strengthens depth information extraction, especially from low-quality depth maps. Extensive experimental results on seven benchmark datasets reveal the superiority and efficacy of the proposed MSNet, compared with state-of-the-art RGB-D SOD approaches.Note to Practitioners—This study introduces a RGB-D SOD network known as Multiple-Strategy Network (MSNet) with bidirectional fusion. Initially, we employ a multiple-strategy fusion module to transmit high-level semantic features in a top-down progressive manner, generating a sequence of appearance features. Subsequently, we apply a self-refinement module to further enhance and optimize the saliency map. Additionally, we incorporate a depth optimization module to improve the extraction of depth information, particularly from low-quality depth maps.

Mirrors play a significant role in our daily lives and are ubiquitous. However, deep learning computer vision models find them challenging owing to the negative impact of reflected information on scene understanding. This study addresses two key challenges faced by multimodal models. First, the cross-modal variability of features at different stages is generally overlooked by contemporary backbone networks. Second, good performance has only been achieved at an unacceptable computational expense, owing to the numerous parameters used. To address the first challenge, we propose a differential-mode multistage adaptive fusion network (differential mode refers to images generated by different sensors that are differentiated to complement each other) that incorporates two-step fusion in the coding stage to account for the degrees of difference among the cross-modal features. In the first stage, wherein considerable differences in modal features exist, multi-angle fusion is performed. In the second stage, wherein the differences are smaller, a hierarchical adaptive fusion strategy is employed. Regarding the second challenge, we introduce a companion training framework for mirror segmentation that combines knowledge distillation and contrastive learning. Our proposed scheme achieves state-of-the-art performance on an available mirror segmentation dataset without requiring numerous parameters.

Abstract When using untrusted third parties to compress and transmit images in real‐life scenarios, it is vital to encrypt them before compression. In order to better address the issues of low security in the original image and poor reconstruction quality of the encrypted image during compressed sensing, this paper proposes a 2D compressed sensing scheme for encrypted images based on complex‐valued measurement matrix (2DCS‐CVM). Firstly, the SHA‐256 algorithm generates keys for the hyperchaotic Lorenz system, and then the chaotic sequences are used to create encrypted images with increased security through subtractive diffusion and global permutation. Secondly, the complex‐valued Vandermonde measurement matrix is utilized for 2D compressed sensing on the encrypted image, and the two‐dimensional projected gradient with embedding decryption algorithm is used to generate recovered images with improved reconstruction performance. Finally, the measurement matrix's computational complexity and transmission bandwidth are reduced through structural sparsification with sparse random matrices. Simulation results demonstrate that this scheme offers an optimal balance between storage, computational complexity, hardware implementation, and reconstruction performance while providing excellent security and robustness.

Semantic segmentation of remote sensing images is a critical research domain. The integration of cross-modal features enhances stability in intricate environments. Despite the impressive performance of existing methods, their complexity and parameter demands remain significant. Our proposed STONet-S$^{\ast }$, a stepped transmission optimization network (STONet) with knowledge distillation (KD), extracts insights from a pretrained extensive teacher network and transfers them to an untrained compact student network. Initially, a group enhancement and interaction unit (GEIU) correct for background noise influence and seamlessly integrates cross-modal features. Additionally, we introduce a stepped transmission decoder (STD) comprising a stepped capture module (SCM) and a self-reverse revision module (SRRM) to capture multiscale information from the ground up. Furthermore, leveraging the frequency domain, we employ frequency-awareness KD using a discrete cosine transform (DCT) and octave convolution to separate high and low-frequency maps, which are subsequently transferred to the student network. Last, detail-delivery and stepped-response KD (SRKD) mechanisms enhance the learning capacity of the student network. Through extensive experimentation on two datasets, STONet-S$^{\ast }$demonstrates superior segmentation accuracy by achieving remarkable results with only 7.19 M parameters. The corresponding code repository can be accessed at:https://github.com/MAXHAN22/STONet.

Though designed with security in mind, blockchains are vulnerable to various kinds of attacks, especially when the network computational power is low. Selfish mining is one of the most rudimentary and notorious attacks, which maliciously renders blocks found by honest miners orphaned by strategically withholding and revealing the found blocks. In this paper, we analyze the profitability of selfish mining under the checkpoint mechanism—a mechanism that has been adopted as a finality gadget by many blockchains like Ethereum and Bitcoin Cash. We develop a rigorous analysis method and conduct quantitative evaluations in various scenarios to explore the mechanism's suppression effect on selfish mining. The results illustrate that the checkpoint mechanism can restrict the profit of selfish mining and increase the threshold of computational power that makes selfish mining profitable, suggesting that it is a practical defense mechanism against selfish mining.

This paper addresses the challenges associated with storing large amounts of state data on a blockchain by proposing a scalable verifiable ledger database. The rise of digital cryptocurrencies has drawn attention to blockchain technology and its potential benefits in terms of data security, system stability, and trust facilitation. However, existing blockchain-oriented systems face limitations in terms of performance and verifiability. Similarly, traditional ledger databases lack the ability to ensure verifiability of state data. To overcome these challenges, the proposed system introduces a scalable verifiable independent architecture named VI-Store, an improved Merkle tree structure called MMB-tree for state data at the billion level. The evaluation results demonstrate the effectiveness of the proposed system, MMB-tree outperforms traditional MBT and MPT index structures, and VI-Store is able to provide stable storage of billion-level state data over a 7*24 period. The findings suggest VI-Store can be used in real blockchain environments.

Blockchain technology is gaining broader attention. Owing to its immutability property and byzantine fault-tolerance consensus protocol, blockchain offers a brand new trusted data-sharing solution. Some researchers use blockchain to drive autonomous collaboration among smart devices, which face massive spatial data updates and usage. The key challenge lies in designing an authenticated data structure (ADS) that can efficiently process spatial data and queries. However, the previous schemes could not handle spatial data efficiently or did not consider the efficiency of frequent data updates. In this paper, we take a step toward implementing a maintenance-efficient ADS on the blockchain, called ESM2-Tree, which is not only good at processing spatial data but also effective in supporting authenticated spatial queries by partitioning and merging data at different granularities. Theoretical analysis and empirical evaluation validate the performance of our ADS, which reduces the overall data structure maintenance overhead by about 50% in a uniform data distribution scenario.

Abstract Here, a novel image‐based quantized compressive sensing (QCS) framework based on complex‐valued Vandermonde (Vander) matrix is proposed. In the proposed QCS framework, a discrete wavelet transform (DWT) serves as a sparse basis and a partial complex‐valued Vander matrix serves as a measurement matrix. The theoretical analysis based on mutual coherence metric of compressive sensing (CS) theory shows that the proposed Vander measurement matrix has the best reconstruction performance among other conventional measurement matrices. The simulation results also show that the recovery quality using the proposed measurement matrix can be greatly improved compared with the other existing real‐valued measurement matrices. In particular, the experiment results also show that under the same measurement matrix, the reconstruction performance of Smoothed l 0 norm (SL0) algorithm is better than that of Orthogonal Matching Pursuit (OMP) algorithm, sparsity adaptive matching pursuit (SAMP) algorithm and approximate message passing (AMP) algorithm. In addition, a sparse measurement matrix scheme is further proposed to achieve a trade‐off between recovery performance and computational complexity. The theoretical analysis and simulation results both show the proposed image‐based QCS is efficient.

Surface defect inspection of railways is important to ensure safe transportation. However, challenging conditions, such as uneven illumination and similar foreground and background, hinder defect inspection. With the development of deep learning and the wide application of the computer vision, defect inspection has made great progress. Accordingly, we propose a depth repeated-enhancement RGB (red–green–blue) network (DRERNet) for rail surface defect inspection. DRERNet fully uses depth and RGB information to better inspect defects on rail surfaces using an encoder–decoder architecture. In the encoder, a novel cross modality enhancement fusion module uses details from RGB maps and location information from depth maps to perform cross-modality fusion. In the decoder, the details and location information in a multimodality complementation module are repeatedly used to progressively refine the DRERNet prediction. We performed extensive experiments, and compared the proposed DRERNet with 10 state-of-the-art methods on the industrial NEU RSDDS-AUG RGB-depth dataset. The comparison results demonstrate that DRERNet consistently performs better than other methods in the all evaluation measures.

The blind quality evaluation of screen content images (SCIs) and natural scene images (NSIs) has become an important, yet very challenging issue. In this paper, we present an effective blind quality evaluation technique for SCIs and NSIs based on a dictionary of learned local and global quality features. First, a local dictionary is constructed using local normalized image patches and conventional -means clustering. With this local dictionary, the learned local quality features can be obtained using a locality-constrained linear coding with max pooling. To extract the learned global quality features, the histogram representations of binary patterns are concatenated to form a global dictionary. The collaborative representation algorithm is used to efficiently code the learned global quality features of the distorted images using this dictionary. Finally, kernel-based support vector regression is used to integrate these features into an overall quality score. Extensive experiments involving the proposed evaluation technique demonstrate that in comparison with most relevant metrics, the proposed blind metric yields significantly higher consistency in line with subjective fidelity ratings.

The applying of third party libraries is an integral part of many mobile applications. With the rapid development of mobile technologies, there are many free third party libraries for developers to download and use. However, there are a large number of third party libraries which always iterate rapidly, it is hard for developers to find available libraries within them. Several previous studies have proposed approaches to recommend third party libraries, which works in the scenario where a developer knows some required libraries, and needs to find other relevant libraries with limited knowledge. In the paper, to further improve the performance of app library recommendation, we propose an approach which combines collaborative filtering and topic modeling techniques. In the collaborative filtering component, given a new app, our approach recommends libraries by using its similar apps. In the topic modelling component, our approach first extracts the topics from the textual description of mobile apps, and given a new app, our approach recommends libraries based on the libraries used by the apps which has similar topic distributions. We perform experiments on a set of 1,013 apps, and the results show that our approach improves the state-of-the-art by a substantial margin.

Quality of Services (QoS) is an important criterion to evaluate Web services recommendation system. Due to factors including various network conditions, QoS values are dynamic and time-varying. In reality, the data is too spare to fit in with traditional time series forecasting model (e.g., ARIMA). To address this crucial challenge, this paper proposes a novel time-aware and sparsity-tolerant QoS values prediction approach based on collaborative filtering. Our approach combines limited historical QoS value with collaborative filtering method to forecast the personalized QoS values. Based on the limited data, our approach firstly forecasts user-service pairs that have historical usage experiences, and then uses CF-based method to predict personalized QoS values. Finally, we combine the results from temporal forecasting with those from CF prediction as the final forecasted QoS values. The extensive experiments show that the proposed approach efficiently improves the forecasting coverage and accuracy.

QoS prediction for Web services is a hot research problem in the field of services computing. As one of the most important methods for QoS prediction, Collaborative Filtering (CF) makes prediction based on the historical QoS data contributed by similar users and services. The key issue in this process is to detect the unreliable data offered by untrustworthy users, which has attracted limited attentions so far. The utilization of unreliable data decreases the prediction accuracy greatly. In this paper, we propose a novel credibility-aware QoS prediction method (named CAP) to address this problem. Our method first employs two-phase K-means clustering to identify the untrustworthy users, which clusters QoS values for untrustworthy index calculation in the first phase and clusters users according to their index in the second phase, and then predicts the missing QoS value based on the credible clustering information. The evaluation results demonstrate that CAP provides considerable improvement on the prediction accuracy compared with other approaches and is robust against various percentages of untrustworthy users.

Configuration bugs are one of the dominant causes of software failures. Previous studies show that a configuration bug could cause huge financial losses in a software system. The importance of configuration bugs has attracted various research studies, e.g., To detect, diagnose, and fix configuration bugs. Given a bug report, an approach that can identify whether the bug is a configuration bug could help developers reduce debugging effort. We refer to this problem as configuration bug reports prediction. To address this problem, we develop a new automated framework that applies text mining technologies on the natural-language description of bug reports to train a statistical model on historical bug reports with known labels (i.e., Configuration or non-configuration), and the statistical model is then used to predict a label for a new bug report. Developers could apply our model to automatically predict labels of bug reports to improve their productivity. Our tool first applies feature selection techniques (e.g., Information gain and Chi-square) to pre-process the textual information in bug reports, and then applies various text mining techniques (e.g., Naive Bayes, SVM, naive Bayes multinomial) to build statistical models. We evaluate our solution on 5 bug report datasets including accumulo, activemq, camel, flume, and wicket. We show that naive Bayes multinomial with information gain achieves the best performance. On average across the 5 projects, its accuracy, configuration F-measure and non-configuration F-measure are 0.811, 0.450, and 0.880, respectively. We also compare our solution with the method proposed by Arshad et al. The results show that our proposed approach that uses naive Bayes multinomial with information gain on average improves accuracy, configuration F-measure and non-configuration F-measure scores of Arshad et al.'s method by 8.34%, 103.7%, and 4.24%, respectively.

The on-demand use, high scalability, and low maintenance cost nature of cloud computing have attracted more and more enterprises to migrate their legacy applications to the cloud environment. Although the cloud platform itself promises high reliability, ensuring high quality of service is still one of the major concerns, since the enterprise applications are usually complicated and consist of a large number of distributed components. Thus, improving the reliability of an application during cloud migration is a challenging and critical research problem. To address this problem, we propose a reliability-based optimization framework, named ROCloud, to improve the application reliability by fault tolerance. ROCloud includes two ranking algorithms. The first algorithm ranks components for the applications that all their components will be migrated to the cloud. The second algorithm ranks components for hybrid applications that only part of their components are migrated to the cloud. Both algorithms employ the application structure information as well as the historical reliability information for component ranking. Based on the ranking result, optimal fault-tolerant strategy will be selected automatically for the most significant components with respect to their predefined constraints. The experimental results show that by refactoring a small number of error-prone components and tolerating faults of the most significant components, the reliability of the application can be greatly improved.

Fault-tolerant real-time scheduling algorithm is one of the most important means to ensure the timeliness and high availability characteristics of fault-tolerant real-time systems. Existing scheduling models for periodic real-time task in heterogeneous platforms typically require the number of processors in the systems to be determined in advance; hence prohibit the scalability and the performance of distributed systems. The algorithms based on these models also require a large number of schedubility tests which lead to long execution time. To address these problems, we propose a primary and backup replica partition based fault-tolerant scheduling algorithm (PBPFT) based on a scalable scheduling model using heterogeneity that does not have to determine the scale of the distributed system in advance. The PBPFT approach also takes advantage of backup copy overlapping and phasing delay techniques to minimize system redundancy, and adopts the processor grouping technique to simplify algorithm complexity. Comprehensive experiments are conducted, and the results validate high resource utilization and commendable performance of our proposed approach.