Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domains, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.

Dynamic adaptive streaming over HTTP (DASH) has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data-pushing in HTTP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTTP/2 with the aim to improve the quality of experience (QoE) of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transition probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.

The Very Fast Decision Tree (VFDT) algorithm is a classification algorithm for data streams. When processing large amounts of data, VFDT requires less time than traditional decision tree algorithms. However, when training samples become fewer, the label values of VFDT leaf nodes will have more errors, and the classification ability of single VFDT decision tree is limited. The Random Forest algorithm is a combinational classifier with high prediction accuracy and noise-tolerant ability. It is constituted by multiple decision trees and can make up for the shortage of single decision tree. In this paper, in order to improve the classification accuracy on data streams, the Random Forest algorithm is integrated into the process of tree building of the VFDT algorithm, and a new Random Forest Based Very Fast Decision Tree algorithm named RFVFDT is designed. The RFVFDT algorithm adopts the decision tree building criterion of a Random Forest classifier, and improves Random Forest algorithm with sliding window to meet the unboundedness of data streams and avoid process delay and data loss. Experimental results of the classification of KDD CUP data sets show that the classification accuracy of RFVFDT algorithm is higher than that of VFDT. The less the samples are, the more obvious the advantage is. RFVFDT is fast when running in the multi-thread mode.

The development of cloud computing has made container technology a hot research issue in recent years. The container technology provides a basic support for micro service architecture, while container networking plays an important role in application of the container technology. In this paper, we study the technical implementation of the Flannel module, a network plug-in for Docker containers, including its functions, implementation principle, utilization, and performance. The performance of Flannel in different modes is further tested and analyzed in real application scenarios.

Cloud computing faces a series of challenges, such as insufficient bandwidth, unsatisfactory real-time, privacy protection, and energy consumption. To overcome the challenges, edge computing emerges. Edge computing refers to a process where the open platform that converges the core capabilities of networks, computing, storage, and applications provides intelligent services at the network edge near the source of the objects or data to meet the critical requirements for agile connection, real-time services, data optimization, application intelligence, security and privacy protection of industry digitization. Edge computing consists of three elements: edge, computing, and intelligence. Edge computing and the Internet of Things (IoT) mutually create, and edge computing and cloud computing complement each other. In the architecture of edge computing, resources are distributed to the edge nodes, and therefore the storage system is near users while the computation function is near data. In this way, the stress on the backbone network can be lessened. With this architecture, the existing key technologies for computation, networks, and storage will change significantly. ZTE’s edge computing solutions can ensure the service quality of operators and greatly enhance the experience of mobile users.

The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories (NVM). NVMs are generally faster than traditional secondary storage devices, write persistently and many offer byte addressing capability. Despite these appealing features, NVMs are difficult to manage and program, which makes it hard to use them as a drop-in replacement for dynamic random-access memory (DRAM). Instead, a majority of modern systems use NVMs through the IO and the file system abstractions. Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework. In this article, we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.

In recent years, Hyper Text Transfer Protocol (HTTP) spreads quickly and steadily in the usage of mobile applications as a common web protocol, so that the mobile applications can also benefit from HTTP/2, which is the new version of HTTP based on SPDY developed by Google to speed up the Internet transmission speed. HTTP/2 enables a more efficient use of network resources and a reduced perception of latency by introducing header field compression and allowing multiple concurrent exchanges on the same connection. However, what HTTP/2 focuses on is visiting websites through a browser, and mobile applications are not considered much. In this paper, firstly, mobile applications are classified based on the data flow characteristics. Based on the classification, we propose an optimization of HTTP/2 for mobile applications, called HTTP/2-Advance, which uses multiple Transmission Control Protocol (TCP) connections to multiplex HTTP requests and responses. Then we build a tiny system which simulates actual requests and responses between mobile applications and servers. We figure out the best choice of the number of multiple TCP connections for mobile applications, and compare the performance of HTTP, HTTP/2 and HTTP/2-Advance in both simulated and in-situ experiments in our system.

Batch processing mode is widely used in the training process of human motion recognition. After training, the motion classifier usually remains invariable. However, if the classifier is to be expanded, all historical data must be gathered for retraining. This consumes a huge amount of storage space, and the new training process will be more complicated. In this paper, we use an incremental learning method to model the motion classifier. A weighted decision tree is proposed to help illustrate the process, and the probability sampling method is also used. The results show that with continuous learning, the motion classifier is more precise. The average classification precision for the weighted decision tree was 88.43% in a typical test. Incremental learning consumes much less time than the batch processing mode when the input training data comes continuously.

Android OS provides such security mechanisms as application signature, privilege limit and sandbox to protect the security of operational system. However, these methods are unable to protect the applications of Android against anti-reverse engineering and the codes of such applications face the risk of being obtained or modified, which are always the first step for further attacks. In this paper, a security enhancement system with online authentication (SeSoa) for Android APK is proposed, in which the code of Android application package (APK) can be automatically encrypted. The encrypted code is loaded and run in the Android system after being successfully decrypted. Compared with the exiting software protecting systems, SeSoa uses online authentication mechanism to ensure the improvementof the APK security and good balance between security and usability.

With the development of message service in telecom networks as well as the Internet, multiple message service formats have been emerging, including Short Message Service (SMS ), Multimedia Messaging Service (MMS ), Mobile Email Message (MEM ) and Instant Message (IM ). Based on the latest research achievements of OMA CPM Organization and GSMA RCS Organization, the message service evolution may have two phases: Integration and convergence, with corresponding overall framework realization methods.

Among the open access standards for telecom service capabilities, Java Community Process (JCP ) and Parlay series are two mainstream standards, which provide service capability opening standards at different levels for different user objects. The JCP specifications include Java Specification Request (JSR ) 21, JSR 32, JSR 116 and JSR 289 especially for Java application developers, while Parlay brings out specifications including Parlay and ParlayX. The service capability open technologies feature different benefits and vitality due to their diversified implementations. As the community of service developers is continuously growing and the demands for integrated service development become more and more manifest, fast, effective and easy-to-use open access technologies for telecom service capabilities have become a very important research subject.

The rapid development of Peer- to - Peer (P2P ) technologies and applications has a great impact on telecom services and operations . Operators should refine the pipeline operation and develop characteristic P2P services . They must comply with the rule of Internet services , and design manageable and operational P2P solutions combining the advantages in network, resource and subscriber, providing users with better P2P service experience . ZTE s manageable and operational P2P solution provides operators with customized authorization and accounting functions . Its development is based on the broadband service management platform widely used in the live networks . ZTE s P2P system is stable and reliable .