A New Strategy in Boosting Information Spread

Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.     

Information filtering in evolving online networks

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.     

基于在线社交网络的信息传播模型

本文构造了一个基于在线社交网络的信息传播模型.该模型考虑了节点度和传播机理的影响,结合复杂网络和传染病动力学理论,进一步建立了动力学演化方程组.该方程组刻画了不同类型节点随着时间的演化关系,反映了传播动力学过程受到网络拓扑结构和传播机理的影响.本文模拟了在线社交网络中的信息传播过程,并分析了不同类型节点在网络中的行为规律.仿真结果表明:由于在线社交网络的高度连通性,信息在网络中传播的门槛几乎为零;初始传播节点的度越大,信息越容易在网络中迅速传播;中心节点具有较大的社会影响力;具有不同度数的节点在网络中的变 关键词： 在线社交网络 信息传播 微分方程 传染病动力学     

基于有向网络的Email病毒传播模型及其震荡吸引子研究

基于有向Email网络和Email病毒传播特点,运用平均场方法建立Email病毒传播的时滞微分方程模型,研究Email病毒在有向网络中的震荡传播行为.理论上给出了震荡解的全局吸引子存在的充要条件,数值实验验证了吸引子的存在性和控制.研究表明,子图之间的传播概率决定吸引子的存在性,而有效传播率影响吸引子的振幅,因此这两个参数对于有效预测和控制Email病毒在网络上的传播规模具有重要意义.     

超衍射成像中 双螺旋点扩展函数的三维定位精度

开关效应和单分子定位的结合可以实现样品的超衍射分辨成像, 双螺旋点扩展函数将单分子定位纳米分辨从二维扩展到了三维.本文对双螺旋点扩展函数的三维定位精度展开了探讨.首先, 基于费希尔信息量, 计算了双螺旋点扩展函数的无偏估计, 得出其理论定位精度, 并分析了光子数、背景噪声以及有效像元尺寸大小对其定位精度的影响; 其次, 基于单分子定位实验过程中对于数据分析通常采用的高斯拟合质心定位算法, 通过误差传递函数定律求得双螺旋点扩展函数的轴向定位精度.计算机模拟结果表明, 在光子数大于1000的条件下, 高斯拟合质心定位精度和费希尔信息量理论定位精度符合较好. 本文的讨论不仅为双螺旋点扩展函数的三维定位精度提供了理论依据, 同时也可为实验提供理论指导. 关键词： 双螺旋点扩展函数 费希尔信息量 定位精度 高斯拟合     

Fluctuations in Wikipedia access-rate and edit-event data

Internet-based social networks often reflect extreme events in nature and society by drastic increases in user activity. We study and compare the dynamics of the two major complex processes necessary for information spread via the online encyclopedia ‘Wikipedia’, i.e., article editing (information upload) and article access (information viewing) based on article edit-event time series and (hourly) user access-rate time series for all articles. Daily and weekly activity patterns occur in addition to fluctuations and bursting activity. The bursts (i.e., significant increases in activity for an extended period of time) are characterized by a power-law distribution of durations of increases and decreases. For describing the recurrence and clustering of bursts we investigate the statistics of the return intervals between them. We find stretched exponential distributions of return intervals in access-rate time series, while edit-event time series yield simple exponential distributions. To characterize the fluctuation behavior we apply detrended fluctuation analysis (DFA), finding that most article access-rate time series are characterized by strong long-term correlations with fluctuation exponents α≈0.9$\alpha \approx 0.9$. The results indicate significant differences in the dynamics of information upload and access and help in understanding the complex process of collecting, processing, validating, and distributing information in self-organized social networks.     

Personal artist recommendation via a listening and trust preference network

Trust information provided by a user unfolds his/her reliable friends with similar tastes. It not only has the potential to help provide better recommendations but also emancipates the recommendation process from heavy computation for seeking friends. In this paper, by taking into account the latent value of trust information, our personal artist recommendation algorithm via a listening and trust preference network (LTPN for short) is presented. We argue that the excellent recommendation should be acquired via the listening and trust preference network instead of the original listening and trust relation information. Experimental results demonstrate LTPN can not only provide better recommendation but also help relieve the cold start problem caused by new users.     

一种基于用户相对权重的在线社交网络信息传播模型

根据在线社交网络信息传播特点和目前社交网络传播模型研究中存在的问题, 本文定义了网络用户之间的相互影响力函数, 在此基础上提出了一种基于用户相对权重的社交网络信息传播模型, 并对网络中的传播路径及传播过程进行了分析, 讨论了不同路径的信息传播影响力.为验证模型的有效性, 将传统的SIR模型和本文模型在六类不同网络拓扑下进行了仿真实验.仿真结果表明, 两类模型在均匀网络中没有明显差异, 但在非均匀网络中本文模型更能体现真实网络特点, 实验同时验证了节点的地位影响着信息的传播, 并且发现英文社交平台Twitter和中文社交平台新浪微博在拓扑结构上具备一定相似性.     

单幅模糊图像点扩散函数估计的梯度倒谱分析方法研究

针对单幅图像复原算法引入先验信息导致复杂度高、运算效率低的问题, 提出了单幅模糊图像点扩散函数估计的梯度倒谱分析方法. 首先给出了单幅模糊图像梯度倒谱估计其点扩散函数的基本原理, 利用相位恢复策略复原了二维点扩散函数相位信息, 实现了点扩散函数的快速估计; 其次, 为鉴别点扩散函数估计精度, 建立了图像梯度保真约束的全变分正则化图像复原模型, 并采用快速稳定收敛的交替方向策略优化能量函数; 通过对仿真和实拍单幅模糊图像进行的测试实验结果表明, 该方法快速准确地估计出点扩散函数, 克服了传统复原算法收敛速度慢的缺点, 有效抑制了振铃效应、保护了边缘信息, 为大尺寸单幅图像复原的工程化实现提供了理论和技术基础. 关键词： 图像复原 点扩散函数 梯度倒谱分析 全变分     

Use of Average Mutual Information and Derived Measures to Find Coding Regions

One of the important steps in the annotation of genomes is the identification of regions in the genome which code for proteins. One of the tools used by most annotation approaches is the use of signals extracted from genomic regions that can be used to identify whether the region is a protein coding region. Motivated by the fact that these regions are information bearing structures we propose signals based on measures motivated by the average mutual information for use in this task. We show that these signals can be used to identify coding and noncoding sequences with high accuracy. We also show that these signals are robust across species, phyla, and kingdom and can, therefore, be used in species agnostic genome annotation algorithms for identifying protein coding regions. These in turn could be used for gene identification.     

基于COSM的三维显微图像复原算法研究

光学分层显微成像技术(COSM)不仅可用于去除非聚焦层杂散光的影响以及噪声的干扰,而且能复原样本的清晰三维像,将成为提取微流体的三维温度和速度场分布信息的有效工具.本文研究了基于已知点扩展函数的6种COSM成像算法,详细讨论了6种COSM成像算法的原理和实现,构造了三维样本图像和点扩展函数模型,并对复原结果进行了分析....     

基于社交网络的观点传播动力学研究

社交网络和微博是重要的Web2.0应用模式, 其观点传播模式与其他网络媒体以及传统媒体相比有很大差异. 本文提出一种基于在线社交网络的观点传播模型, 研究社交网络中舆论观点扩散的形式与特征. 仿真结果表明: 模型中信息传播的速度与六度分割理论的结论十分符合; 一个带强烈倾向性的观点在固有观点均匀分布的网络中传播的情况下, 稳定时网络中不会出现相反的观点; 稳定时的观点分布与源节点的度和回溯深度有关, 并不受信任界限的限制, 这与Deffuant模型和Hegselmann-Krause模型不同. 同时, 本文还分析了传播意愿、观点变更率和信任界限对弛豫时间的影响.     

一种新的复杂网络影响力最大化发现方法

复杂网络中影响力最大化建模与分析是社会网络分析的关键问题之一, 其研究在理论和现实应用中都有重大的意义. 在给定s值的前提下, 如何寻找发现s个最大影响范围的节点集, 这是个组合优化问题, Kempe等已经证明该问题是NP-hard问题. 目前已有的随机算法时间复杂度低, 但是结果最差; 其他贪心算法时间复杂度很高, 不能适用于大型社会网络中, 并且这些典型贪心算法必须以了解网络的全局信息为前提, 而获取整个庞大复杂且不断发展变化的社会网络结构是很难以做到的. 我们提出了一种新的影响力最大化算法模型RMDN, 及改进的模型算法RMDN++, 模型只需要知道随机选择的节点以及其邻居节点信息, 从而巧妙地回避了其他典型贪心算法中必须事先掌握整个网络全局信息的问题, 算法的时间复杂度仅为O(s log(n)); 然后, 我们利用IC模型和LT模型在4种不同的真实复杂网络数据集的实验显示, RMDN, RMDN++算法有着和现有典型算法相近的影响力传播效果, 且有时还略优, 同时在运行时间上则有显著的提高; 我们从理论上推导证明了方法的可行性. 本文所提出的模型算法适用性更广, 可操作性更强, 为这项具有挑战性研究提供了新的思路和方法.     

正交码元移位键控扩频水声通信

码元移位键控(CSK)作为一种广义的M元扩频技术被广泛应用于水声通信领域来克服扩频增益对通信速率的限制.为了获得更高的通信速率, 减少通道间干扰, 并充分利用 CSK的冗余信息, 本文提出联合利用码元相位信息的正交双通道CSK扩频水声通信技术.首先, 推导了正交CSK每符号积分输出的表达式; 然后, 研究了正交CSK的性能及与传统CSK和双通道CSK对比仿真的误码率; 最后, 通过实验验证了仿真对比的正确性. 实现了正交CSK在10⁴ bits, 4 kHz带宽下580.6 bps通信速率的有效传输. 通过公式推导、 仿真分析和实验研究, 得到正交CSK提供了优良的性能的结论. 关键词： 水声通信 扩频 正交 CSK     

太阳光谱辐射计的信号传递与性能表征模型研究

太阳光谱辐射计作为一种专用光谱辐射测量仪器,对于其宽谱段和大动态范围的测量特点,准确完整地表征分光性能是十分重要的。研究内容旨在为太阳光谱辐射计的研制与验证提供明确的理论依据和测试方法,给出清晰而准确的机理模型和指标模型对光谱仪器系统的设计和评估提供指导。因此,重点阐述了光谱仪器系统的信号传递模型到性能指标模型的推导和建立过程。线谱扩展函数这种串联卷积模型能够综合反映仪器的各个元件对系统的影响,而且很容易通过窄带线谱光源测试而得到,并且线谱扩展函数矩阵能够清晰完整地给出光谱仪器的分光性能细节特性。而在线谱扩展函数的基础上再进一步提取关键几何特征,通过简单的算法定义即可得到半高全宽(FWHM),带外抑制和带外辐射这三项示性函数指标,可以很好地对分光系统的性能进行定量表征,是十分有效的光谱仪器系统性能评估的指标模型。     

盲图像复原研究现状

盲图像复原是在未知或不完全确知相关原始图像与成像点扩展函数的先验知识的情形下,利用所观测到的降质图像对原始图像和点扩展函数进行估计的一种图像处理方法。本文对近年来涌现出的主要盲图像复原算法进行了回顾,并根据相应的理论来源及相互联系将其划分为四大类,对各类复原算法及其改进算法进行了分析和讨论,为更清晰与深刻地认识和理解盲图像复原理论、解决实际图像降质问题提供参考。     

数字水印的鲁棒性评测模型

数字水印的鲁棒性是水印技术实用化的一个重要指标.与通过StirMark测试和各种仿真测试不同,定义互信息作为代价函数,建立水印系统鲁棒性描述和度量的一般模型.以扩频水印和量化水印为范例,推导出评测鲁棒性的互信息度量计算模型,并仿真分析了高斯噪声和JPEG压缩条件下互信息函数对鲁棒性的评估结果.实验以统计误比特率的方法计算图像DCT域中低频系数为载体的扩频水印误码率和一段音频数据三级小波细节系数为载体的量化水印误码率.互信息函数和误码率之间的匹配关系验证了互信息度量模型的有效性,互信息函数可以作为代价函数预测误码率的变化趋势. 关键词： 数字水印 鲁棒性 互信息 误码率     

Chaotic direct sequence spread spectrum for secure underwater acoustic communication

Direct sequence spread spectrum (DSSS) has been widely used in underwater acoustic (UWA) communications. In DSSS communications, spreading sequence’s period and chip rate are important characteristic parameters. However, these characteristic parameters may be detected in hostile environments due to the periodic nature of spreading sequence and their well-known construction process. In this paper, a novel chaotic direct sequence spread spectrum (CD3S) method is proposed for secure UWA communications. In the CD3S communications, chaotic sequences acting as encryption keys are used to encrypt the phases of transmitted signals. Consequently, the application of chaotic sequences disguises these obvious characteristic parameters of spread spectrum (SS) signals. It is difficult for unauthorized users to detect or intercept CD3S signal without knowledge of corresponding chaotic sequences. Hence, CD3S signal has a lower probability of detection and interception. Moreover, CD3S can achieve similar bit error ratio (BER) performance compared with DSSS in actual UWA communications. Also, the receivers that are suitable for DSSS communication can also be applied to CD3S communication. Numerical simulations demonstrate its excellent performance and potential applications in confidential UWA communications.     

A contemporary review on user scheduling techniques and feedback reduction strategies towards the development of 5G and beyond communications

User scheduling (US) is the process of dynamic selection of the set of active users out of all available users to serve in each time slot. This is done to optimize the system performance, such as maximizing the sum rate, achieving better fairness, and quality of service or minimizing the interference. The choice of US method depends on the desired system performance and the trade-off between fairness and efficiency. In order to achieve these performance metrics base station (BS) needs channel state information (CSI) of each user for efficient US. Moreover, US and CSI feedback are closely related in the context of conventional multiple-input multiple-output (MIMO) to massive MIMO (mMIMO) systems based on full and limited CSI, as feedback information is often used to make informed decisions on US. To address these objectives simultaneously, this survey deals with exploring different algorithms used for efficient US, various criteria for US considering different scenarios, key methods for user grouping, methods for reduced feedback, and different standard codebook based feedback methods. To be more specific and concise, this article provides a comprehensive survey on state of the art methods used for US in single cell single tier, dual stage (double tier), multi cell scenarios and feedback mechanisms used in various contexts, e.g., multiuser (MU)-MIMO, MU-mMIMO, frequency division duplexing (FDD) mMIMO framework. Moreover, a synopsis of the recently proposed advanced codebook and non-codebook based methods for the long term evolution standards, fifth generation, and beyond communications are discussed. Finally the research gaps as the future scopes are discussed in this article.     

Theory of rumour spreading in complex social networks

We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.