一种面向分布式网络管理的自适应可扩展模型

本文针对大规模分布式网络的特点，提出了一种灵活、动态、自适应伸展性网络管理方案。它采和层次型体系结构，支持多个管理域的分布式网络管理，适应了大规模网络的规模可变性和弹性的特点。为了解决网络事件大量性、多样性和相关性的问题，本文将网络事件分为简单事件和复合事件两种类型，采用层次型事件处理机制和基于动态时间窗的事件合成方法，保证了事件检测的可靠性，平衡了系统负载，降低了网络资源的占有率。     

Multimodal Event Detection in Twitter Hashtag Networks

Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.     

基于空间相关性的事件驱动无线传感器网络分簇算法

分簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性,该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型,将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,该方法减少了传输数据量,有效节省了网络能量。     

一种基于EDDL的非侵入式负荷检测模型

针对目前非侵入式负荷检测时存在检测精确度低的问题,提出一种基于事件驱动-深度学习(EDDL)的负荷检测模型。通过零交叉检测电流数据,基于事件驱动机制从大量数据中发现关键事件;将包含关键事件的电流序列转换至图像空间,并代入基于深度学习的负荷检测模型,从而实现端对端的非侵入式负荷检测。实验结果表明,与多分类支持向量机(MSVM)、前馈神经网络(FNN)、卷积神经网络(CNN)和长短时记忆网络(LSTM)模型相比,所提EDDL模型综合性能更优,检测准确率和精确度分别为94.67%和91.76%。仿真结果验证了所提模型可基于事件驱动机制挖掘电流数据,并基于深度学习模型有效提取电流数据特征,从而实现高精确度的非侵入式电力负荷检测。该模型对非侵入式电力负荷检测研究具有一定借鉴作用。     

A semantic framework for video genre classification and event analysis

Semantic video analysis is a key issue in digital video applications, including video retrieval, annotation, and management. Most existing work on semantic video analysis is mainly focused on event detection for specific video genres, while the genre classification is treated as another independent issue. In this paper, we present a semantic framework for weakly supervised video genre classification and event analysis jointly by using probabilistic models for MPEG video streams. Several computable semantic features that can accurately reflect the event attributes are derived. Based on an intensive analysis on the connection between video genres and the contextual relationship among events, as well as the statistical characteristics of dominant event, a hidden Markov model (HMM) and naive Bayesian classifier (NBC) based analysis algorithm is proposed for video genre classification. Another Gaussian mixture model (GMM) is built to detect the contained events using the same semantic features, whilst an event adjustment strategy is proposed according to an analysis on the GMM structure and pre-definition of video events. Subsequently, a special event is recognized based on the detected events by another HMM. The simulative experiments on video genre classification and event analysis using a large number of video data sets demonstrate the promising performance of the proposed framework for semantic video analysis.     

基于广义后缀树的事件流频繁情节在线挖掘算法

现有的事件序列频繁情节挖掘多采用Apriori—like算法，此方法无法应用于事件流数据发掘。针对采用滑动窗口的事件流频繁情节发现问题，提出一种广义后缀树结构，在新事件加入时对情节树进行动态维护：为提高时空效率，采用频繁情节发生列表逐层构建的方法实现对搜索空间的划分．通过监控边界情节以发现频繁情节的变化。实验结果表明了算法的有效性和优越性。     

低维流形约束下的事件相机去噪算法

本文主要提出一个新的基于流形约束的事件相机去噪算法。事件相机是一类新型的视觉传感器，以高时间分辨率（微秒）感知场景亮度变化，同时输出具有像素位置、时间及极性的事件流。事件相机在传输亮度变化的同时受到噪声的干扰，带噪的事件流会对后续的应用造成不利的影响。为了解决该问题，本文假设事件分布在高维空间中的低维流形上，利用事件点间相似信息建立图模型以近似流形结构，结合图的流形平滑约束，实现事件流去噪。该算法首次将基于图的流形约束引入事件去噪问题中并且直接处理连续的事件序列。仿真实验和真实数据结果证明了事件去噪算法的有效性。      

一种有效的基于空间关系的复合事件检测模型

随着空间信息的广泛应用,产生了对基于空间关系的复合事件检测需求。本文建立了空间事件模型,在该模型基础上扩展定义了空间事件复合算子及其语义,并证明该定义的复合算子封闭;采用组合着色Petri网构造基于空间关系的复合事件检测模型,充分利用事件公共表达式简化Petri网;采用变迁优先级解决冲突变迁的问题,并提出基于该模型的检测算法;通过实验仿真验证该检测模型是一个简洁、有效的复合事件检测机制。     

在网络安全事件流中异常检测的方法

针对网络安全事件流中异常检测问题,定义网络安全异常事件模式为候选频繁情节,基于无折叠出现的频繁度定义研究网络安全事件流中频繁情节发现方法.该方法中,针对事件流的特点,提出了频繁度密度概念;针对网络安全异常事件模式的时间间隔限制,利用事件流中滑动窗口设计算法;针对复合攻击模式的特点,对算法进行剪枝.实验证明本文方法的时空复杂性、漏报率符合网络安全事件流中异常检测的需求.     

基于深度学习的异常事件检测

面对复杂场景下异常事件检测的准确率偏低的情况,本文提出一种基于深度学习的异常事件检测方法,并将此方法扩展为异常事件分类方法.利用神经网络模型提取特征,将群体发散聚集事件,群体密集聚集事件,群体逃散事件和追赶事件这4种异常事件进行检测和分类.通过PKU-SVD-B测试集对训练出来的模型进行测试实验,并在UMN数据集上与几种方法做了对比实验,验证了本文提出的基于深度学习的异常事件检测算法,在适应多种不同场景的前提下,对多种异常事件检测的准确率很高,表明训练出来的模型对异常事件检测具有极强的泛化能力.     

事件关系检测的语言学资源、评测及研究综述

事件关系是一种客观存在于事件之间的逻辑关系,事件关系检测是一项面向文本信息流进行事件关系判定的自然语言处理技术。事件关系检测的核心任务是以事件为基本语义单元,通过分析事件的篇章结构特征及语义特征,借助语义关系识别和推理,对事件关系进行自动分析与理解。事件关系检测技术在自动文摘,自动问答,信息检索等领域有着广泛的应用。首先介绍事件关系检测的任务定义、语言学资源和评测方法;然后,回顾国内外现有的主要研究方法;最后,给出这一研究的关键问题及技术难点。     

Compound event barrier coverage in wireless sensor network

In wireless sensor networks (WSN),more and more people utilize barrier coverage to monitor compound events.The data of compound event barrier coverage (CEBC) comes from different types of sensors.It will be subject to multi-constraints under complex conditions in real-world application.Aiming at the merging problem of compound event confidence,a computational model based on joint probability density was proposed.In order to solve the optimization problem of compound event barrier coverage under multiple complex constraints,an active set multiplier policy (ASMP) was proposed.The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage.The algorithm can simplify complex problems to reduce the computational load of the network and improve the efficiency of the network.The simulation results demonstrate that the ASMP algorithm is more efficient in the allocation of sensor resources and network optimization.     

基于秩2更新的多维数据流典型相关跟踪算法

 现存的多维数据流典型相关分析(Canonical Correlation Analysis,简称CCA)算法主要是基于近似技术的求解方法,本质上并不是持续更新的精确算法.为了能在时变的环境中持续、快速而精确地跟踪数据流之间的相关性,本文提出一种多维数据流典型相关跟踪算法TCCA.该算法基于秩2更新理论,通过并行方式持续更新样本协方差矩阵的特征子空间,进而实现多维数据流典型相关的快速跟踪.理论分析及仿真实验结果表明,TCCA具有较好的稳定性、较高的计算效率和精度,可以作为基本工具应用于数据流相关性检测、特征融合、数据降维等数据流挖掘领域.     

Word-Representation-Based Method for Extracting Organizational Events from Online Media

Online social media exhibit massive organizational event relevant messages, and the well categorized event information can be useful in many real-world applications. In this paper, we propose a research framework to extract high quality event information from massive online media data. The main contributions lie in two aspects: First, we present an event-extraction and event-categorization system for online media data; second, we present a novel approach for both discovering important event categories and classifying extracted events based on word representation and clustering model. Experimental results with real dataset show that the proposed framework is effective to extract high quality event information.     

Identification of Key Nodes in Microblog Networks

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms — PageRank, Betweeness Centrality, Closeness Centrality, Out‐degree — using a new tweets propagation model — the Ignorants‐Spreaders‐Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.     

基于多种词特征的微博突发事件检测方法

近年来,各领域内频频发生各类突发事件,对社会稳定发展产生了一定程度的影响.本文提出了一种基于多种词特征的微博突发事件检测模型,可以在海量微博数据中对突发事件进行检测,便于相关决策者进行微博监控和舆论引导,尽可能减少突发事件给社会带来的危害.首先根据时间信息对微博数据进行时间切片,对每一个时间窗口内的数据分别计算各个词语的词频特征、话题标签特征和词频增长率特征;然后基于D-S证据理论和层次分析法,确定词的各个特征权重,并进行加权融合得到词的突发特征值,将突发特征值大的词挑选出来构成突发特征词集,构建基于共现度和结合紧密度的突发事件特征词集的耦合度矩阵;最后将该耦合度矩阵作为凝聚式层次聚类算法的输入,生成一棵由突发词为叶子节点的二叉树,并采用内部相似度的二叉树剪枝算法对聚类结果进行划分,即可实现对相应时间窗口突发事件的检测.实验结果表明,基于突发词的事件检测模型在簇内部相似度阈值等于1.1时效果最好,正确率达到0.8462、召回率达到0.8684、F值为0.8571,表明了本文所提方法的有效性.     

基于事件分析的多摄像机鲁棒跟踪算法

提出了一种新的基于事件分析的目标跟踪算法来解决多个目标分离或遮挡时的可靠跟踪问题.首先提出使用仿射变换来获得多个摄像机之间重叠画面的映射关系,实现目标交接,为后面的目标识别奠定基础.然后当单摄像机目标跟踪过程中发生候选目标多于一个或者多个目标对应一个候选目标的情况时,提出一种判别目标出现遮挡事件或分离事件的新方法,并且通过多摄像机的目标交接准确识别出发生遮挡或分离事件的目标标号,解决目标发生遮挡或分离后跟踪失败的问题.实验结果证明:所提出的方法突破了一般跟踪算法受目标底层特征约束的难点,具有更高的鲁棒性.     

Spoofing detection in IEEE 802.15.4 networks based on received signal strength

The shared medium used in wireless networks makes them vulnerable to spoofing attacks, in which an adversary masquerades as one or more legitimate nodes to disturb normal operation of the network. In this paper we present a novel spoofing detection method for static IEEE 802.15.4 networks based on spatial correlation property of received signal strength (RSS). While most existing RSS based techniques directly process RSS values of the received frames and rely on multiple traffic air monitors (AMs) to provide an acceptable detection performance, we extract features of RSS streams to reduce data redundancy and provide a more distinguishable representation of the data. Our algorithm employs two features of RSS streams, summation of detailed coefficients (SDCs) in discrete Haar wavelet transform (DHWT) of the RSS streams and the ratio of out-of-bound frames. We show that in a typical scenario, a single AM with SDC as detection parameter, can theoretically outperform a system with 12 AMs which directly applies RSS values as detection parameter. Using ratio of out-of-bound frames facilitates detection of high rate attacks. In addition, we suggest adaptive learning of legitimate RSS values which enhances the robustness of the attack detector against environmental changes. Using both magnitude and frequency related features, we achieved high detection performance with a single AM; this enables development of preventive measures for spoofing attacks. The performance of our approach was evaluated through an IEEE 802.15.4 testbed in an office environment. Experimental results along with theoretical analysis show that the proposed method outperforms the existing RSS-based spoofing detection solutions. Using a single AM, we were able to attain 94.75% detection rate (DR) with 0.56% false positive rate (FPR). For 4 AMs, the results improved to 99% DR and 0% FPR.