This paper addresses the optimum routing problem of multipoint connection in large-scale networks. A number of algorithms for routing of multipoint connection have been studied so far, most of them, however, assume the availability of complete network information. Herein, we study the problem under the condition that only partial information is available to routing nodes and that routing decision is carried out in a distributed cooperative manner. We consider the network being partitioned into clusters and propose a cluster-based routing approach for multipoint connection. Some basic principles for network clustering are discussed first. Next, the original multipoint routing problem is defined and is divided into two types of subproblems. The global optimum multicast tree then can be obtained asymptotically by solving the subproblems one after another iteratively. We propose an algorithm and evaluate it with computer simulations. By measuring the running time of the algorithm and the optimality of resultant multicast tree, we show analysis on the convergent property with varying network cluster sizes, multicast group sizes and network sizes. The presented approach has two main characteristics, 1) it can yield asymptotical optimum solutions for the routing of multipoint connection, and 2) the routing decisions can be made in the environment where only partial information is available to routing nodes.

With the development of cloud computing, the Mobile Edge Computing has emerged and attracted widespread attentions. In this paper, we focus on the load balancing in MEC with energy harvesting. We first introduce the load balancing in MEC as a problem of minimizing both the energy consumption and queue redundancy. Thereafter, we adapt such a optimization problem to the Lyapunov algorithm and solve this optimization problem. Finally, extensive simulation results validate that the obtained strategy improves the capabilities of MEC systems.

Due to the increasing demand for 3D video transmission over wireless networks, managing the quality of experience (QoE) of wireless 3D video clients is becoming increasingly important. However, the variability of compressed 3D video bit streams and the wireless channel condition as well as the complexity of 3D video viewing experience assessment make it difficult to properly allocate wireless transmission resources. In this paper, we discuss the characteristics of H.264 3D videos and QoE assessment of 3D video clients, and further propose a transmission scheme for 3D video transmission over a wireless communication system. The purpose of our scheme is to minimize the average ratio of stalls among all video streaming clients. By taking into account the playout lead and its change, we periodically evaluate the degree of urgency of each client as regards bitstream receipt based on fuzzy logic, and then allocate the transmission resource blocks to clients jointly considering their degrees of urgency and channel conditions. The adaptive modulation and coding scheme (MCS) is applied to ensure a low transmission error rate. Our proposed scheme is suitable for practical implementation since it has low complexity, and can be easily applied in 2D video transmission and in non-OFDM systems. Simulation results, based on three left-and-right-views 3D videos and the Long Term Evolution (LTE) system, demonstrate the validity of our proposed scheme.

As a further investigation of the image captioning task, some works extended the vision-text dataset for specific subtasks, such as the stylized caption generating. The corpus in such dataset is usually composed of obvious sentiment-bearing words. While, in some special cases, the captions are classified depending on image category. This will result in a latent problem: the generated sentences are in close semantic meaning but belong to different or even opposite categories. It is a worthy issue to explore an effective way to utilize the image category label to boost the caption difference. Therefore, we proposed an image captioning network with the label control mechanism (LCNET) in this paper. First, to further improve the caption difference, LCNET employs a semantic enhancement module to provide the decoder with global semantic vectors. Then, through the proposed label control LSTM, LCNET can dynamically modulate the caption generation depending on the image category labels. Finally, the decoder integrates the spatial image features with global semantic vectors to output the caption. Using all the standard evaluation metrics shows that our model outperforms the compared models. Caption analysis demonstrates our approach can improve the performance of semantic representation. Compared with other label control mechanisms, our model is capable of boosting the caption difference according to the labels and keeping a better consistent with image content as well.

In this letter, we propose a framework for scalable video multicast, which exploits the scalability of scalable video and the multiuser diversity of OFDMA systems. We further propose a resource allocation algorithm which guarantees the base-quality video for all users, and improves the transmission efficiency for users with good channel conditions.

When communication network planning-design is performed, especially in a short-term case, it is important to utilize existing facilities in the construction of the new network. In this paper, link capacity assignment problem (CA problem) for packet-switched networks is investigated with the consideration of the existing network. To deal with this, per-unit cost of existing link capacity is thought to be less than that of newly installed capacity and a link cost function is modeled by a non-linear, non-differentiable one which is composed of two portions of capacity cost. After formulating the CA problem, two optimum algorithms derived from Lagrange multiplier method are presented and a modified algorithm is used for solving the CA problem in order to reduce the computation time. Some numerical results show that according to the values of link traffic flows, there will be links whose capacities must be set equally to the existing values. Moreover, when link cost difference is introduced in the CA problem, the number of links that the capacities of which have to be changed from existing values is less than that of linear cost function case, i.e., the case without consideration of the cost difference in link capacity.

Pseudo-random sequences with good statistical property, such as low autocorrelation, high linear complexity and 2-adic complexity, have been widely applied to designing reliable stream ciphers. In this paper, we explicitly determine the 2-adic complexities of two classes of generalized cyclotomic binary sequences with order 4. Our results show that the 2-adic complexities of both of the sequences attain the maximum. Thus, they are large enough to resist the attack of the rational approximation algorithm for feedback with carry shift registers. We also present some examples to illustrate the validity of the results by Magma programs.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.