The surge in online interactions and advancement in Big Data related techniques have generated vast amounts of hybrid data in the sense that the data are symbolic, numerical or missing features, and usually only a small number of data objects possess true labels due to high annotation costs. A necessary step of fully releasing the potential of these partially labeled hybrid data lies in feature selection, for which the neighborhood rough set (NRS) is an efficient mathematical method to apply. In NRS, setting proper neighborhood granules greatly influences the effectiveness and robustness of algorithms atop it. However, existing methods usually determine the optimal neighborhood radius of neighborhood granule via computationally intensive grid search, where the neighborhood radius for each object is the same, i.e., “unadaptive”. Some methods investigate adaptive granulation strategies, yet they inevitably hinge on preset parameters or a-prior knowledge. To tackle this problem, we propose an adaptive granules-enabled semi-supervised feature selection method that can adaptively generate suitable neighborhood radii for both labeled and unlabeled objects. The core idea lies in using the purity of decision labels as the threshold for granularity maximization construction. Then, by combining with neighborhood entropy and local density, a feature metric is designed to measure the feature significance. A semi-supervised feature selection algorithm is utilized to select feature subset by using the information from both labeled and unlabeled objects. Instead of hinging on expert knowledge, the proposed method only rely on the data per se. Experimental results on real-world datasets demonstrate the effectiveness of the designed method and its superiority over other state-of-the-art.

Microservice architecture has demonstrated immense advantages in solving the scalability and maintainability problems confronted by traditional monolithic systems. However, existing microservice architectures still encounter the problem of blurred boundaries, which significantly impact the system performance. The primary reason being the lack of unified evaluation indicators and efficient microservice splitting strategies. Specifically, some approaches divide entities like classes subjectively, resulting in inconsistent microservice boundaries, while others utilize objective criteria, but their complexity makes practical implementation challenging. To this end, we formalize the optimal microservice granulation problem, show its NPcompleteness and propose the granulation solution based on granular-ball computing. Our designs encompass a microservicebased information decision system to quantify the structural similarity between entities, a microservice granulation representation method based on granular-ball computing, and an optimal microservice granulation method given fixed budget constraint. Extensive experiments are conducted on open-source microservice projects to showcase the granulation result and the experimental results also show the efficacy and effectiveness.

Peer-to-peer (P2P) content delivery networks (CDNs) have demonstrated immense potential to mitigate escalating network traffic. Meanwhile, blockchain emerges as a promising technology that can overcome the shortcomings confronted by P2P CDNs via acting as a trusted third party (TTP) to ensure critical security properties such as fairness and offering monetary incentivization. However, existing protocols for blockchain-based P2P CDNs still encounter the delivery efficiency issue, one of the primary reasons being the absence or failure to identify the most suitable deliverers. Specifically, some overlook the process of selecting deliverers, entrusting them haphazardly, and some merely consider a single evaluation dimension for a deliverer, resulting in a lack of comprehensiveness. Even when multiple dimensions are considered, the outcome of each dimension is not verifiable, leading to unreliability. To this end, we propose an optimized deliverer selection method in blockchain-based P2P CDNs. Our designs encompass a proof of delivery quality (PoQD) protocol to quantify the accumulative verifiable contributions of a deliverer, a comprehensive credibility evaluation mechanism based on neighborhood entropy, and a provider budget constraint optimized deliverer selection algorithm. Extensive experiments are conducted on Ethereum test network to justify our adaptive selection of k optimized deliverers, desired delivery efficiency and feasible on-chain costs. The experiment results indicate the efficacy and effectiveness of our proposed method.

Feature selection can reduce the dimensionality of data effectively. Most of the existing feature selection approaches using rough sets focus on the static single type data. However, in many real-world applications, data sets are the hybrid data including symbolic, numerical and missing features. Meanwhile, an object set in the hybrid data often changes dynamically with time. For the hybrid data, since acquiring all the decision labels of them is expensive and time-consuming, only small portion of the decision labels for the hybrid data is obtained. Therefore, in this paper, incremental feature selection algorithms based on information granularity are developed for dynamic partially labeled hybrid data with the variation of an object set. At first, the information granularity is given to measure the feature significance for partially labeled hybrid data. Then, incremental mechanisms of information granularity are proposed with the variation of an object set. On this basis, incremental feature selection algorithms with the variation of a single object and group of objects are proposed, respectively. Finally, extensive experimental results on different UCI data sets demonstrate that compared with the non-incremental feature selection algorithms, incremental feature selection algorithms can select a subset of features in shorter time without losing the classification accuracy, especially when the group of objects changes dynamically, the group incremental feature selection algorithm is more efficient.