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Weiqi, also called Go or Baduk, is regarded as the most complicated board game in the world. Weiqi playing has a very long history in East Asia. Its strategic thinking in decision-making even affects militarists there. The availability of the records of almost all Weiqi games in the past decades enables us to perform quantitative studies about this sport with high statistical significance. Here, we construct the directed and undirected Elo transfer networks using over 80,000 Weiqi game records from 1941 to 2019. We investigate the distributions of network measures of the two Elo transfer networks, such as node degree, clustering coefficient, betweenness centrality, and closeness centrality. We find a strong correlation between the average Elo value of a Weiqi player and a few node traits. We also find a rich-club phenomenon among high-Elo players. The two networks are unveiled to possess significant communities, which are consistent with each other and can be well interpreted by player’s affiliation, gender, and Elo value. These findings are different in a large part from those of the chess Elo transfer networks.

Data structure is one of the important courses of basic education for computer students. In this study, aiming at the disadvantages of traditional data structure teaching, we try to apply scaffolding theory to teaching to improve the whole teaching mode and effect to a great extent.

The availability of big data recorded from massively multiplayer online role-playing games (MMORPGs) allows us to gain a deeper understanding of the potential connection between individuals' network positions and their economic outputs. We use a statistical filtering method to construct dependence networks from weighted friendship networks of individuals. We investigate the 30 distinct motif positions in the 13 directed triadic motifs which represent microscopic dependences among individuals. Based on the structural similarity of motif positions, we further classify individuals into different groups. The node position diversity of individuals is found to be positively correlated with their economic outputs. We also find that the economic outputs of leaf nodes are significantly lower than that of the other nodes in the same motif. Our findings shed light on understanding the influence of network structure on economic activities and outputs in socioeconomic system., 19 pages, 5 figures

The transfer of football players is an important part in football games. Most studies on the transfer of football players focus on the transfer system and transfer fees but not on the transfer behavior itself. Based on the 470,792 transfer records from 1990 to 2016 among 23,605 football clubs in 206 countries and regions, we construct a directed footballer transfer network (FTN), where the nodes are the football clubs and the links correspond to the footballer transfers. A systemic analysis is conduced on the topological properties of the FTN. We find that the in-degrees, out-degrees, in-strengths and out-strengths of nodes follow bimodal distributions (a power law with exponential decay), while the distribution of link weights has a power-law tail. We further figure out the correlations between node degrees, node strengths and link weights. We also investigate the general characteristics of different measures of network centrality. Our network analysis of the global footballer transfer market sheds new lights into the investigation of the characteristics of transfer activities., 7 pages, 6 figures

Modern technologies not only provide a variety of communication modes, e.g., texting, cellphone conversation, and online instant messaging, but they also provide detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study the inter-call durations of the 100,000 most-active cellphone users of a Chinese mobile phone operator. We confirm that the inter-call durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the inter-call durations follow a power-law distribution for only 3460 individuals (3.46%). The inter-call durations for the majority (73.34%) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cellphone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom frauds, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operator in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cellphone users. We also discover more information about the calling patterns of these four clusters, e.g., the probability that a user will call the $c_r$-th most contact and the probability distribution of burst sizes. Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users., Comment: 7 pages and 8 figues

Traffic classification is becoming one of the major applications in the data center networks with a lot of cloud services. Recent works about software defined networking (SDN) have found new ways to manage data center networks. However, with the imbalance of the elephant and mice flows is sharpening, the accuracy and efficiency of traffic classification have become more and more important in SDN management. To address this issue, in this paper, we propose a traffic classification method that can deal with the traffic classification in SDN. Our method is based on spectral clustering and Software-Defined Networking (SDN). We propose a real-time flow extraction and representation method by scanning the flow tables in SDN controller. Then we cluster the flow data with spectral analysis. Extensive experiments on different settings have been performed, showing that our method is good at traffic classification with high detection rates and low overhead.

In China the video open courses along with the resource sharing courses constitute the open online courses. Evaluation of the open online course invariably is from education sector, calls "top-down" evaluation. In order to perfect the evaluation of the open online course, the paper describes a kind of "bottom-up” evaluation index system, evaluation is from learner. By using Delphi method, indexes and weights in system are established. Furthermore, the evaluation platform is developed according "bottom-up” open online course evaluation index system. Results of this research can help the open online course with obtaining more comprehensive evaluation.

Much empirical evidence shows that individuals usually exhibit significant homophily in social networks. We demonstrate, however, skill complementarity enhances heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We construct a model to quantify the heterophily by assuming that individuals choose collaborators to maximize utility. Using a huge database of online societies, we find evidence of heterophily in collaboration networks. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of online societies. Furthermore, the degree of skill complementarity is positively correlated with their production output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems.

People in modern societies form different social networks through numerous means of communication. These communication networks reflect different aspects of human's societal structure. The billing records of calls among mobile phone users enable us to construct a directed calling network (DCN) and its Bonferroni network (SVDCN) in which the preferential communications are statistically validated. Here we perform a comparative investigation of the cliques of the original DCN and its SVDCN constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days. We find that the statistical properties of the cliques of the two calling networks are qualitatively similar and the clique members in the DCN and the SVDCN exhibit idiosyncratic behaviors quantitatively. Members in large cliques are found to be spatially close to each other. Based on the clique degree profile of each mobile phone user, the most active users in the two calling networks can be classified in to several groups. The users in different groups are found to have different calling behaviors. Our study unveils interesting communication behaviors among mobile phone users that are densely connected to each other., 18 pages, 10 figures

Latent Dirichlet Allocation (LDA), has been recently used to automatically generate text corpora topics, and applied to sentences extraction based multi-document summarization algorithms. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant or background words, or represent insignificant themes. This paper proposed a topic-sensitive algorithm for multi-document summarization. Our approach is distinguished from existing approaches in that we use LDA model to identify and distinguish significance topic which is used in sentence weight calculation. Moreover, beside topic characteristics, this approach also considered some statistics characteristics, such as term frequency, sentence position, sentence length, etc. This approach not only highlights the advantages of statistics characteristics, but also cooperated with LDA topic model. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on DUC2002 corpus.

This paper introduced mobile communication technology in the field of industrial control, designed and implemented an equipment monitoring system based on the platform of mobile terminal. Which aims to convenient monitoring the running state of equipment in real-time, reduce the failure rate of running equipment and maintenance the running equipment operation timely. The system can monitor the running state of equipment in real-time, analyze and judge the potential failure of the equipment accurately, and transmit monitoring data and maintenance task information timely to the mobile terminal users with the wireless smart sensor technology, multi-sensor information fusion technology and Android mobile terminal technology, so as to achieve the goal of real-time maintenance. In the test, the system can prevent accidents effectively, takes the advantage of convenience, real-time, portable.

Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network. If only reciprocal calls are considered, we obtain an undirected mutual calling network. The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges. We perform a comparative analysis of the statistical properties of these four networks, which are constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days. We find that these networks share many common structural properties and also exhibit idiosyncratic features when compared with previously studied large mobile calling networks. The empirical findings provide us an intriguing picture of a representative large social network that might shed new lights on the modelling of large social networks.

In Power User Electric Energy Data Acquisition System, a deadly drift may be generated and accumulated by the differences between devices and environments every day. Therefore an effective external timing system is imperative for clock synchronization. Based on Beidou and GPS satellite, a multimedia timing device and broadcast algorithm are designed. The algorithm mainly including frequently broadcasting Synchronous frame to calibrate clock frequency and relatively infrequently replying response frame to measure the average delay. And experiments show that the synchronization is always better than 1ms with our scheme.

BP neural network model is advanced in this paper for the analysis of dam deformation monitoring data, aiming at the limitation of traditional method of statistical model. Structure and algorithm of BP neural network model is introduced, together with the normalization of sample data. The application of this model to Taihe Reservoir Dam shows that its precision is prior to that of statistical model.