A hybrid EDA for load balancing in multicast with network coding

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms

Interacting model of new agegraphic dark energy: observational constraints and age problem

Many dark energy models fail to pass the cosmic age test because of the old quasar APM 08279+5255 at redshift $z=3.91$, the $\Lambda$CDM model and holographic dark energy models being no exception. In this paper, we focus on the topic of age problem in the new agegraphic dark energy (NADE) model. We determine the age of the universe in the NADE model by fitting the observational data, including type Ia supernovae (SNIa), baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB). We find that the NADE model also faces the challenge of the age problem caused by the old quasar APM 08279+5255. In order to overcome such a difficulty, we consider the possible interaction between dark energy and dark matter. We show that this quasar can be successfully accommodated in the interacting new agegraphic dark energy (INADE) model at the $2\sigma$ level under the current observational constraints.Comment: 12 pages, 5 figures; typos corrected; version for publication in SCIENCE CHINA Physics, Mechanics & Astronom

Investigate the kinetics of coke solution loss reaction with an alkali metal as a catalyst based on the improved genetic algorithm

Abstract The kinetics of coke solution loss reaction with and without sodium carbonate were investigated under the reaction atmosphere of carbon dioxide. The variables of gas flow rate and coke particle size were explored to eliminate the external and internal diffusion, respectively. Then, the improved method combining with the least square and the genetic algorithm was proposed to solve the homogeneous model and the shrinking core model. It was found that the improved genetic algorithm method has good stability by studying the fitness function at each generation. In the homogeneous model, the activation energy with and without sodium carbonate was 54.89 and 95.56 kJ/mol, respectively. And, the activation energy with and without sodium carbonate in the shrinking core model was 49.83 and 92.18 kJ/mol, respectively. Therefore, it was concluded that the sodium carbonate has the catalytic action. In addition, results showed that the estimated conversions were agreed well with the experimental ones, which indicated that the calculated kinetic parameters were valid and the proposed method was successfully developed. Graphical Abstrac

Morphology, Microstructure, and Mechanical Properties of S32101 Duplex Stainless-Steel Joints in K-TIG Welding

In this paper, the S32101 duplex stainless steel welded joints were produced by a K-TIG welding system. The weld geometry parameters under different welding speeds were analyzed by combining the morphological characteristics of the keyhole. The microstructure and impact toughness of the base metal and weld metal zone under different welding speeds were studied. The experiment results show that the welding speed has quite an effect on the geometry profile of the weld. In addition, the characteristic parameters of the keyhole can effectively predict the geometry profile of the weld. The test results prove that the microstructure, Σ3 coincidence site lattice grain boundary, and phase boundary of ferrite and austenite have an effect on the impact property of the weld metal zone. When the proportion of the austenite, Σ3 coincidence site lattice grain boundary and random phase boundary increased, the impact property of the weld metal zone also increased

The Named Entity Recognition of Chinese Cybersecurity Using an Active Learning Strategy

In data-driven big data security analysis, knowledge graph-based multisource heterogeneous threat data organization, association mining, and inference analysis attach increasinginterest in the field of cybersecurity. Although the construction of knowledge graph based on deep learning has achieved great success, the construction of a largescale, high-quality, and domain-specific knowledge graph needs a manual annotation of large corpora, which means it is very difficult. To tackle this problem, we present a straightforward active learning strategy for cybersecurity entity recognition utilizing deep learning technology. BERT pre-trained model and residual dilation convolutional neural networks (RDCNN) are introduced to learn entity context features, and the conditional random field (CRF) layer is employed as a tag decoder. Then, taking advantages of the output results and distribution of cybersecurity entities, we propose an active learning strategy named TPCL that considers the uncertainty, confidence, and diversity. We evaluated TPCL on the general domain datasets and cybersecurity datasets, respectively. The experimental results show that TPCL performs better than the traditional strategies in terms of accuracy and F1. Moreover, compared with the general field, it has better performance in the cybersecurity field and is more suitable for the Chinese entity recognition task in this field

JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center

Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load

Microstructure and Flexural Properties of Z-Pinned Carbon Fiber-Reinforced Aluminum Matrix Composites

Z-pinning can significantly improve the interlaminar shear properties of carbon fiber-reinforced aluminum matrix composites (Cf/Al). However, the effect of the metal z-pin on the in-plane properties of Cf/Al is unclear. This study examines the effect of the z-pin on the flexural strength and failure mechanism of Cf/Al composites with different volume contents and diameters of the z-pins. The introduction of a z-pin leads to the formation of a brittle phase at the z-pin/matrix interface and microstructural damage such as aluminum-rich pockets and carbon fiber waviness, thereby resulting in a reduction of the flexural strength. The three-point flexural test results show that the adding of a metal z-pin results in reducing the Cf/Al composites’ flexural strength by 2–25%. SEM imaging of the fracture surfaces revealed that a higher degree of interfacial reaction led to more cracks on the surface of the z-pin. This crack-susceptible interface layer between the z-pin and the matrix is likely the primary cause of the reduction of the flexural strength

EX-Action: Automatically Extracting Threat Actions from Cyber Threat Intelligence Report Based on Multimodal Learning

With the increasing complexity of network attacks, an active defense based on intelligence sharing becomes crucial. There is an important issue in intelligence analysis that automatically extracts threat actions from cyber threat intelligence (CTI) reports. To address this problem, we propose EX-Action, a framework for extracting threat actions from CTI reports. EX-Action finds threat actions by employing the natural language processing (NLP) technology and identifies actions by a multimodal learning algorithm. At the same time, a metric is used to evaluate the information completeness of the extracted action obtained by EX-Action. By the experiment on the CTI reports that consisted of sentences with complex structure, the experimental result indicates that EX-Action can achieve better performance than two state-of-the-art action extraction methods in terms of accuracy, recall, precision, and F1-score