Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users’ perspectives, users have to personally select the optimal Web services according to quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, QoS data vary over time due to changes in user reputation; therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called OPRC, to efficiently provide a personalized reputation for each user. Based on the users’ observed QoS data, OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.

Echelon-Ferrers is an important method to improve lower bounds for constant-dimension codes, which can be applied on various parameters. Fagang Li [12] combined the linkage construction and echelon-Ferrers to obtain some new lower bounds of constant-dimension codes. In this letter, we generalize this linkage construction to obtain new lower bounds.

Energy balance consumption is an important research topic in the field of wireless sensor networks (WSNs). Clustering protocols are widely used to reduce WSN energy consumption. However, this many-to-one cluster head (CH) model can also easily lead to energy exhaustion of some critical nodes near the base station results in the decline of the network quality and lifetime. This article proposes a clustering election game-based and two-level management clustering protocol (CEGT) for WSNs; the network is divided into layers of equal size, and the layer heads (LHs) are selected within the layers based on the location and energy information. Each layer is further divided into clusters of equal size, and the nodes within the clusters undergo a two-stage screening followed by local clustering and multiple rounds of election games to dynamically select the staged CHs. This protocol effectively prevents low-energy nodes from participating in the CH competitions, and selects high-quality nodes through election games which are based on customized qualification values, and ensures reasonable energy utilization as well as efficient network load balancing. Finally, our simulations show that our CEGT protocol outperforms other clustering protocols in terms of the network lifetime, the number of surviving nodes, and the average number of clusters, and the throughput, which also indicate that the CEGT protocol is more suitable for large-scale networks.

One of the most fundamental topics in subspace coding is to explore the maximal possible value ${\mathbf{A}}_{q}(n,d,k)$ of a set of $k$ -dimensional subspaces in $\mathbb F_{q}^{n}$ such that the subspace distance satisfies $\text {d}_{\text {S}}(U,V) = \dim (U+V)-\dim (U\cap V)\,\,\geq d$ for any two different $k$ -dimensional subspaces $U$ and $V$ in this set. In this letter, we propose a construction for constant dimension subspace codes by inserting a composite structure composing of an MRD code and its sub-codes. Its vast advantage over the previous constructions has been confirmed through extensive examples. At least 49 new constant dimension subspace codes which exceeds the currently best codes are constructed.