Action recognition has attracted increasing interest in computer vision due to its potential applications in many vision systems. One of the main challenges in action recognition is to extract powerful features from videos. Most existing approaches exploit either hand-crafted techniques or learning-based methods to extract features from videos. However, these methods mainly focus on extracting the dynamic motion features, which ignore the static form features. Therefore, these methods cannot fully capture the underlying information in videos accurately. In this article, we propose a novel feature representation method for action recognition, which exploits hierarchical sparse coding to learn the underlying features from videos. The learned features characterize the form and motion simultaneously and therefore provide more accurate and complete feature representation. The learned form and motion features are considered as two modalities, which are used to represent both the static and motion features. These modalities are further encoded into a global representation via a pairwise dictionary learning and then fed to an SVM classifier for action classification. Experimental results on several challenging datasets validate that the proposed method is superior to several state-of-the-art methods.

Validation methods for hard real-time jobs are usually performed based on the maximum execution time. The actual execution time of jobs are assumed to be known only when the jobs arrive or not known until they finish. A predictable algorithm must guarantee that it can generate a schedule for any set of jobs such that the finish time for the actual execution time is no later than the finish time for the maximum execution time. It is known that any job-level fixed priority algorithm (such as Earliest Deadline First) is predictable. However, job-level dynamic priority algorithms (such as Least Laxity First) may or may not. In this paper, we investigate the predictability of a job-level dynamic priority algorithm EDZL (Earliest Deadline Zero Laxity). We show that EDZL is predictable on the domain of integers regardless of the knowledge of the actual execution times. Based on this result, furthermore, we also show that EDZL can successfully schedule any periodic task set if the total utilization is not greater than (m + 1)/2, where m is the number of processors.

As a typical representative of a permissioned blockchain system, Hyperledger Fabric has garnered substantial attention in recent years. Despite the application and promotion of Hyperledger Fabric in fields such as smart grid and smart healthcare, challenges persist with respect to its performance, especially its transaction latency. High latency always discourages Hyperledger Fabric from latency-sensitive applications. In this paper, we focus on the execution phase of the unique Execute-Order-Validate architecture of Hyperledger Fabric and conduct a comprehensive analysis of its endorsement policies. We summarize three theorems and give corresponding mathematical proofs based on the definition of the endorsement policy. To better analyze the generation of latency during the endorsement process, we further developed a theoretical model using queuing theory. Subsequently, we conducted multiple experiments on Hyperledger Fabric v2.0 for performance evaluation. With the experiment results, we discuss the reasons for the performance differences between different endorsement strategies and between equivalent logical expressions. Eventually, this paper offers some suggestions for endorsement policy selection which can provide a reference for the application of Hyperledger Fabric in the actual production environment.

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain‐based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph‐based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real‐world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading.