Social Influence Analysis: Models,Methods, and Evaluation

Social influence analysis (SIA) is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and evaluation aspects related to SIA. We divide SIA models into two types: microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.     

基于卡尔曼滤波的时变水声信道估计

为了能同时利用时变水声信道的簇状稀疏特性和时间相关性,构造了一种时变水声信道模型,并基于该模型对传统的卡尔曼滤波压缩感知算法进行改进。该方法主要利用前一时刻估计的信道状态响应来确定当前时刻信道的候选支撑集,并以此构造时变水声信道的状态转移方程。通过卡尔曼滤波迭代的方法计算候选支撑集上的系数,最后通过阈值法滤除误差原子。仿真结果表明：该方法能有效地利用水声信道间的时间相关性来提高信道估计的性能,同时由于水声信道存在簇状稀疏特性,因此经该方法也具有一定的鲁棒性。     

Influence Diffusion Model in Multiplex Networks

The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence. Up to now, most of the research has tended to focus on monolayer network rather than on multiplex networks. But in the real world, most individuals usually exist in multiplex networks. Multiplex networks are substantially different as compared with those of a monolayer network. In this paper, we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant correlations in each layer of relationships and study the problem of unbalanced distribution between various relationships. Meanwhile, we measure the distribution across the network by the similarity of the links in the different relationship layers and establish a unified propagation model. After that, place on the established multiplex network propagation model, we propose a basic greedy algorithm on it. To reduce complexity, we combine some of the characteristics of triggering model into our algorithm. Then we propose a novel MNStaticGreedy algorithm which is based on the efficiency and scalability of the StaticGreedy algorithm. Our experiments show that the novel model and algorithm are effective, efficient and adaptable.     

Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix is constructed by choosing the k-nearest neighbor set of users within the community, in this case, the collaborative filtering algorithm is used for recommendation. Thus, the execution efficiency of the algorithm is improved without reducing the accuracy of recommendation.     

椭圆特征值问题的局部径向基函数差分法（英）

本文介绍椭圆特征值问题的局部径向基函数差分法．这种方法的主要思想是人为选取各插值节点所对应的影响区域，只考虑影响区域内插值点对该插值节点的影响，忽略影响区域外插值点的影响．这种局部化方法在损失一定计算精度的同时离散得到稀疏矩阵，从而使算法能应用于计算大规模插值节点科学计算．通过数值实验，研究了节点分部，插值点数以及形状参数对该特征值问题计算结果的影响，并选用三种径向基函数进行计算比较．数值结果和解析解非常吻合．     

Identifying spreading influence nodes for social networks

The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.     

An Improved MDS-MAP Localization Algorithm Based on Weighted Clustering and Heuristic Merging for Anisotropic Wireless Networks with Energy Holes

The MDS-MAP (multidimensional scaling-MAP) localization algorithm utilize almost merely connectivity information, and therefore it is easy to implement in practice of wireless sensor networks (WSNs). Anisotropic networks with energy hole, however, has blind communication spots that cause loss of information in the merging phase of MDSMAP. To enhance the positioning accuracy, the authors propose an MDS-MAP (CH) algorithm which can improve the clustering and merging strategy. In order to balance the effect of energy consumption and the network topology stabilization, we present a weighted clustering scheme, which considers the residual energy, the degree of connectivity nodes and node density. As the original MAD-MAP method poses a limitation of merging condition, the authors relax the merging requirement and present a heuristic estimation method for lost connectivity over energy holes. Simulation results show that the improved MDS-MAP (CH) localization algorithm has achieved higher localization accuracy, better-balanced energy consumption and stronger network robustness.     

基于相对关联度的工程选材决策模型及应用

 根据灰色关联度反映候选材料的理想解之间曲线形状的相似性,在综合考虑正、负理想解影响的基础上,通过构造相对关联度,建立工程选材决策模型.以低温存储罐材料选择为例,根据专家评分得到所选8种评价指标的权重,确定理想解和负理想解,继而分别计算10种候选低温材料的相对关联度,进行选材决策.结果表明：全硬态301型不锈钢的相对关联度最高,是最佳的低温存储罐材料,这与实际应用和加权性质分析法得出的选择结果一致,而且所得其他候选材料的测评效果排序也更加合理.该模型同时反映候选材料的理想解和负理想解之间曲线的相似性,物理含义更加明确,分析问题更加全面、客观,是工程选材决策的有力工具.     

一种基于椒盐噪声图像的加权滤波算法研究

针对中值滤波和其改进算法虽然能够在很大程度上改善噪声带来的影响，但是使图像边缘变得模糊这一问题，提出一种滤除椒盐噪声的加权滤波新算法。该算法定义中值相似度和空间临近度函数，并采用双阈值，根据阈值的范围，采用不同的方法获取权值。使用该算法对图像进行加权滤波不仅能够有效地去除椒盐噪声，而且尽可能的保存完整的图像边缘信息。     

Advanced Community Identification Model for Social Networks

Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this study, we propose an advanced community-detection model for social networks in order to identify network communities based on global and local information. Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation. This process is conducted with the same color till nodes reach maximum similarity. Finally, the communities are formed, and a clear community graph is displayed to the user. Our proposed model is completely parameter-free, and therefore, no prior information is required, such as the number of communities, etc. We perform simulations and experiments using well-known synthetic and real network benchmarks, and compare them with well-known state-of-the-art models. The results prove that our model is efficient in all aspects, because it quickly identifies communities in the network. Moreover, it can easily be used for friendship recommendations or in business recommendation systems.     

Temporal reasoning for scenario recognition in video-surveillance using Bayesian networks

The authors propose a high-level scenario recognition algorithm for video sequence interpretation. The recognition of scenarios is based on a Bayesian networks approach. The model of a scenario contains two main layers. The first one allows events from the observed visual features to be highlighted and the second layer is focused on the temporal reasoning stage. The temporal layer uses specific nodes permitting an event-based approach. These nodes focus on the lifetime of events highlighted from the results of the first layer. The temporal layer then estimates the qualitative and quantitative relations between the different temporal events helpful for the recognition task. The global recognition algorithm is illustrated over real indoor image sequences of an abandoned baggage scenario.     

IWO与BPNN混合智能算法在 转子故障诊断中的应用

在经典IWO杂草算法的基础上提出一种适用于神经网络优化的新算法。该算法将多种结构的神经网络权值阈值编码为不同维度的杂草种子，以神经网络均方误差作为种子适应度的统一评价标准，同时对多个维度的杂草种子进行排序筛选，实现了神经网络权值阈值与结构同时优化的目的。应用该方法于转子系统故障分类问题，实验结果表明该方法可以在结合BP算法优势的同时有效优化神经网络各参数，可以得到分类精度高、结构最简且泛化能力强的神经网络故障分类器。     

A Parallel Approach to Discords Discovery in Massive Time Series Data

A discord is a refinement of the concept of an anomalous subsequence of a time series. Being one of the topical issues of time series mining, discords discovery is applied in a wide range of real-world areas (medicine, astronomy, economics, climate modeling, predictive maintenance, energy consumption, etc.). In this article, we propose a novel parallel algorithm for discords discovery on high-performance cluster with nodes based on many-core accelerators in the case when time series cannot fit in the main memory. We assumed that the time series is partitioned across the cluster nodes and achieved parallelization among the cluster nodes as well as within a single node. Within a cluster node, the algorithm employs a set of matrix data structures to store and index the subsequences of a time series, and to provide an efficient vectorization of computations on the accelerator. At each node, the algorithm processes its own partition and performs in two phases, namely candidate selection and discord refinement, with each phase requiring one linear scan through the partition. Then the local discords found are combined into the global candidate set and transmitted to each cluster node. Next, a node performs refinement of the global candidate set over its own partition resulting in the local true discord set. Finally, the global true discords set is constructed as intersection of the local true discord sets. The experimental evaluation on the real computer cluster with real and synthetic time series shows a high scalability of the proposed algorithm.     

基于组合图像特征与分层节点搜索的回环检测方法

目的 文中通过提出一种新的回环解决方案,平衡回环检测系统的高准确率与高运行效率。方法 提出一种利用组合图像特征与分层节点搜索的新方法。首先,计算一种原始图像的下采样二值化全局特征和经过改进的ORB(oriented FAST and rotated BRIEF)局部特征,将其存入图像特征数据库。其次,引入一种分层节点搜索算法,在数据库中搜索与当前图像特征最相似的全局特征作为回环候选。最后,利用改进的ORB特征进行局部特征匹配,验证候选图像,确定回环检测结果。结果 使用该算法在3个不同的数据集上进行验证,测试中每次回环检测的平均处理时间仅需19 ms。结论 实验结果表明,该算法在运行效率、准确率、召回率等方面均达到了领域内的先进水平。     

一种新型NoC拓扑结构的研究

提出一种片上网络(NoC)拓扑结构--Spidemet,并对其网络的主要属性如节点度、网络直径、连通度、平均最短路径和平均最短布线等进行了研究.首先将Spidemet与其它拓扑结构的属性进行比较,并采用模拟退火的布局映射算法,根据 NoC 的布局结构,将不同的节点放入 NoC 网格中,即给出一组被绑定和调度的可供选择IP核,在满足IP核所占用芯片面积的条件下将选择的IP核映射到网络中,目标是最小化平均布线长度.网络拓扑结构图描述文件和IP核任务图作为输入.实验中运行基准程序,结果表明提出的网络拓扑结构更适合于将来的 SoC 的片上网络构造.     

基于改进蚁群算法的码垛机器人路径规划应用研究

目的为解决蚁群算法在码垛机器人路径规划中存在的收敛速度慢、容易陷入局部最优等问题,提出一种人工势场和蚁群算法相结合的方法。方法首先,根据码垛机器人机械手在人工势场中不同节点所受到的合力,对初始信息素进行不均匀分布,以解决蚁群算法初期由于缺乏信息素导致的无效路径搜索。其次,在启发函数的设计中引入码垛机器人机械手在下一节点所受到的合力,以解决蚁群算法容易陷入局部最优的问题。最后,对信息素的更新策略进行改进。按照寻得路径的长度不同,对每次迭代完成后信息素的增量成比例进行更新,并设置最大、最小值,以解决迭代后期路径上信息素过大而使蚁群算法陷入局部最优的问题。结果改进后的蚁群算法收敛速度提升了约51%,寻找到的最短路径提升了约10%。和其他改进的蚁群算法相比,在综合性能上也有一定程度上的提高。结论改进后的蚁群算法收敛更快,寻找的最优路径更短。     

An improved localized radial basis function meshless method for computational aeroacoustics

An improved localized radial basis function collocation method is developed for computational aeroacoustics, which is based on an improved localized RBF expansion using Hardy multiquadrics for the desired unknowns. The method approximates the spatial derivatives by RBF interpolation using a small set of nodes in the neighborhood of any data center. This approach yields the generation of a small interpolation matrix for each data center and hence advancing solutions in time will be of comparatively lower cost. An upwind implementation is further introduced to contain the hyperbolic property of the governing equations by using flux vector splitting method. The 4–6 low dispersion and low dissipation Runge–Kutta optimized scheme is used for temporal integration. Corresponding boundary conditions are enforced exactly at a discrete set of boundary nodes. The performances of the present method are demonstrated through their application to a variety of benchmark problems and are compared with the exact solutions.     

网络拓扑结构对IP组播效率影响的研究

研究了不同网络拓扑结构对IP组播效率的影响,给出了一种度量组播效率的方法,并进行了分析与仿真.结果表明:在随机选择组播树根节点的情况下,平均节点度越大的网络,组播效率越低;限制组播路由的通过性和适当选择组播根节点的位置能够有效提高组播效率;有源最短路径树在不同网络拓扑下的性能差异较大,并且不是一种能够适合于各种网络结构的高效组播树构建算法.     

管带式水冷散热器的换热性能研究

本文对管带式水冷散热器的换热性能进行数值模拟研究并通过实验进行验证,以求提高换热器换热性能。建立模型分别从管带式百叶窗、扁管翅片单元进行全面的性能分析。其中对管带式散热器百叶窗的开窗角度进行了分析,结果表明:管带式散热器的百叶窗开角在23°~27°之间的散热效果最佳,提出并通过数值模拟验证了一种优化翅片结构的方案,与传统结构对比,散热效果有明显提高。搭建实验台并设计正交试验,研究了不同因素对散热器整体散热效果的影响程度。在本实验研究的范围下,通过多组实验数据分析得出,进口水温、循环水流量、风扇转速3种因素对散热器散热量的影响程度为:进口水温>循环水流量>风扇转速。     

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be enhanced with no requirement for a trusted third party. Using the latest developments in blockchain technology, this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem (STFOA-CPP) with blockchain-based intrusion detection in an SDN environment. The major intention of the STFOA-CPP technique is the maximization of lifetime, network connectivity, and load balancing with the minimization of latency. In addition, the STFOA-CPP technique is based on the sea turtles’ food-searching characteristics of tracking the odour path of dimethyl sulphide (DMS) released from food sources. Moreover, the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic. Finally, the blockchain can inspect the data integrity, determine significantly malicious input, and improve the robust nature of developing a trust relationship between several nodes in the SDN. To demonstrate the improved performance of the STFOA-CPP algorithm, a wide-ranging experimental analysis was carried out. The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.