The multicast routing problem in software-defined networking (SDN) is an NP-hard problem. The existing solution methods based on deep strength learning suffer from the problems of branch redundancy, an excessively large action space and slow convergence of the intelligent models. In this paper, an intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the optimal multicast tree problem is decomposed into two subproblems: fork node selection and the construction of an optimal path from a fork node to a destination node. Second, a multichannel matrix is designed as the state space for the internal and external controllers of hierarchical reinforcement learning based on the global network-aware information characteristics of SDN. Then, different action spaces are designed for the upper and lower subproblems, four action selection policies are designed for constructing multicast paths, and different reward policies are designed at different levels. Finally, a series of experiments and their results show that the designed algorithm not only searches the multicast tree efficiently but also converges faster and without redundant branches, with better performance in terms of bandwidth, delay and packet loss rate than the current mainstream solution algorithms. The codes for DHRL-FNMR are open and available at https://github.com/GuetYe/DHRL-FNMR.

Sentence representation is an important research hotspot in natural language processing (NLP) since it can map the semantics of sentences into semantics vectors, thereby effectively solving complex semantics computing problems. Recently, sentence representations are mainly obtained by indirect means. Specifically, for sentence representations obtained by unsupervised means, they are often calculated by the weighted sum of embeddings of tokens in sentences; for sentence representations obtained by self-supervised or supervised means, they are often derived from intermediate encodings of sentences in prediction tasks. For example, Google's BERT and MUSE respectively use the embedding of [CLS] in the next sentence prediction task and intermediate encodings of sentences in the translation bridge task as sentence representations. In this paper, we use the observed semantics increment feature of sentences to directly model the semantics function of sentences. To be able to use the existing neural network language model to approximate the semantics function, we first implement the first-order Taylor expansion on the semantics function to obtain a difference semantics model and then add it to BERT as a subtask to perform self-supervised fine-tuning. Finally, we get a new sentence representation model S-BERT. S-BERT achieves the state-of-the-art performance on many datasets in Chinese, English, and Vietnamese.

As a storage method for a distributed storage system, an erasure code can save storage space and repair the data of failed nodes. However, most studies that discuss the repair of fault nodes in the erasure code mode only focus on the condition that the bandwidth of heterogeneous links restricts the repair rate but ignore the condition that the storage node is heterogeneous, the cost of repair traffic in the repair process, and the influence of the failure of secondary nodes on the repair process. An optimal repair strategy based on the minimum storage regenerative (MSR) code and a hybrid genetic algorithm is proposed for single‐node fault scenarios to solve the above problems. In this work, the single‐node data repair problem is modeled as an optimization problem of an optimal Steiner tree with constraints considering heterogeneous link bandwidth and heterogeneous node processing capacity and takes repair traffic and repair delay as optimization objectives. After that, a hybrid genetic algorithm is designed to solve the problem. The experimental results show that under the same scales used in the MSR code cases, our approach has good robustness and its repair delay decreases by 10% and 55% compared with the conventional tree repair topology and star repair topology, respectively; the repair flow increases by 10% compared with the star topology, and the flow rate of the conventional tree repair topology decreases by 40%.

Privacy protection has recently attracted increasingly attention in location-based services (LBS) with the development of wireless communication and mobile positioning technology. Many location-based cloaking algorithms are proposed to protect location privacy of mobile users. However, there is a scenario where the anonymous server only utilizes users defined as current users in our algorithm, who are requiring LBS requests at the current time, to form the cloaking region. If there are not enough current users at the current time, the generated cloaking region will be larger than expected. To solve this problem, we propose the probability-base location prediction ( PLP ) algorithm. Mobile users are divided into current users and historical users. According to predicting probability, historical users are also utilized to generate the cloaking region when current users are not enough to achieve the k-anonymity model. Experimental results show that PLP can predict the cloaking region as high as 90% and the cloaking region can also decrease remarkably.

Summary Cloud storage has been gaining tremendous popularity among individuals and corporations because of its low maintenance cost and on‐demand services for the clients. To improve the availability and the reliability of critical data, storing multiple replicas on multiple servers is a commonly used strategy. Currently, several provable data possession (PDP) protocols for multiple replicas of dynamic data have been proposed to ensure the integrity of outsourced multi‐copy data, but the efficiency of these protocols on verifying multiple replicas one by one is not satisfactory. In this paper, we propose a provable multiple replication data possession protocol with full dynamics, named MR‐DPDP. In MR‐DPDP, we utilize a novel authenticated data structure called Merkle hash tree with rank to support both full dynamic data updates and efficient integrity verification. In addition, our construction with RSA signature can support both variable‐sized file blocks and public verification. Through security proof and performance evaluation, we demonstrate that MR‐DPDP not only is sound but also incurs less communication overhead when updating data blocks as well as verifying a proof of the integrity of multiple replicas. Copyright © 2015 John Wiley & Sons, Ltd.

With the rapid development of the cloud computing service, utilizing traditional access control models was difficult to meet the complex requirements of data protection in cloud environment. In cloud environment, the definition of data and its protection are gradually varied when contents shifting from one virtual machine to another; in these new scenarios, the multi-tenancy pattern has been taken as a core attribute. For this reason, many users need to change their roles according to different situations; certifications has impact much more complicated challenges in cloud environment while access control was suitable for the static status but no longer for the changing situation. In this paper, a new usage control protocol model—multi-UCON (MUCON) based on usage control (UCON), combined with encryption technology and the digital watermarking technology, is proposed with the characteristics of flexible accrediting, feature binding, and off-line controlling. The analysis and simulation experiments indicate that the proposed protocol model is secure, reliable, and easy to be implemented, which can be deployed in cloud computing environments for data protection.