Real-time event detection from the Twitter data stream using the TwitterNews+ Framework

Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed as a result of these research efforts. One of the major challenges faced in this context is the high computational cost associated with event detection in real-time. We propose, TwitterNews+, an event detection system that incorporates specialized inverted indices and an incremental clustering approach to provide a low computational cost solution to detect both major and minor newsworthy events in real-time from the Twitter data stream. In addition, we conduct an extensive parameter sensitivity analysis to fine-tune the parameters used in TwitterNews+ to achieve the best performance. Finally, we evaluate the effectiveness of our system using a publicly available corpus as a benchmark dataset. The results of the evaluation show a significant improvement in terms of recall and precision over five state-of-the-art baselines we have used.     

一种基于有色Petri网的DES的控制方法

通过有色Petri网来实现具有或逻辑形式约束条件的Petri网控制器是非常有效的,但以往的文献通常假设Petri网中的变迁是不能并发的。本文提出的利用有色Petri网控制离散事件系统(DES)的方法,不但实现了或逻辑形式的约束,而且允许Petri网中的所有变迁都可以并发。     

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.     

实现"或逻辑"线性约束的结构型控制器设计方法综述

基于受控Petri网的离散事件监控器按实现形式可分为逻辑型控制器和结构型控制器．由于结构型控制器具有控制动作的计算速度快，被控对象和监控器的描述形式统一等优点，而受到很多研究人员的青睐．然而，由于“或逻辑”线性约束条件的非凸状态空间的本质，用结构型控制器实现该线性约束条件将比较困难．在本文中，作者首先回顾了这一问题的起源及其意义，接着详细讨论了当前用结构型控制器实现该类状态规范的主流方法及它们各自存在的优缺点，并就某些方法提出了改进措施．最后，作者对今后的研究方向进行了预测和展望．     

Robustly distributed diagnosis of discrete-event systems and its application to Alipay transaction systems

In most of existing literature, it is assumed that all of the sensors can work normally. However in some situations, several sensors occur abnormal behavior or stuck at faults such that prior diagnosable decisions may not hold. By this regard, we address the problem of robustly distributed failure diagnosis of discrete-event systems with observation losses in this paper. In order to ensure diagnosability, the notion of robustly diagnosability is proposed in the distributed framework. Motivated by earlier works, new communication models and dilation operators are constructed, based on which the robustly distributed diagnosis problem is converted to a distributed diagnosis problem. One algorithm for the verification of robustly distributed diagnosis is proposed. Followed by it, a necessary and sufficient condition for the robustly diagnosability is presented. Finally, a part of Alipay transaction systems as an application is used to illustrate the construction of some automata and the verification algorithm.     

Extracting temporal and causal relations based on event networks

Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods.     

I-S detectability of partially-observed discrete event systems: a novel matrix-based method

The state estimation problem is always considered to be critical in discrete event systems (DESs). In this paper, two methods are proposed for investigating the initial state estimation problem: one is the matrix-based dimension reduction tracking observation system, and the other is the matrix-based reversal observation system. In our paper, the initial state estimation is treated as the initial-state detectability (I-S detectability). Using the Boolean semi-tensor product method of matrices, the corresponding algebraic forms of the partially-observed DES are separately constructed, where the corresponding computational complexity is reduced to some degree. Based on the newly defined state transition output-event observation matrix, necessary and sufficient criteria are established to determine the I-S detectability of the addressed system. Illustrative examples are also given to show feasibility of the derived results.     

Whose story is it anyway? Automatic extraction of accounts from news articles

Narratives are comprised of stories that provide insight into social processes. To facilitate the analysis of narratives in a more efficient manner, natural language processing (NLP) methods have been employed in order to automatically extract information from textual sources, e.g., newspaper articles. Existing work on automatic narrative extraction, however, has ignored the nested character of narratives. In this work, we argue that a narrative may contain multiple accounts given by different actors. Each individual account provides insight into the beliefs and desires underpinning an actor’s actions. We present a pipeline for automatically extracting accounts, consisting of NLP methods for: (1) named entity recognition, (2) event extraction, and (3) attribution extraction. Machine learning-based models for named entity recognition were trained based on a state-of-the-art neural network architecture for sequence labelling. For event extraction, we developed a hybrid approach combining the use of semantic role labelling tools, the FrameNet repository of semantic frames, and a lexicon of event nouns. Meanwhile, attribution extraction was addressed with the aid of a dependency parser and Levin’s verb classes. To facilitate the development and evaluation of these methods, we constructed a new corpus of news articles, in which named entities, events and attributions have been manually marked up following a novel annotation scheme that covers over 20 event types relating to socio-economic phenomena. Evaluation results show that relative to a baseline method underpinned solely by semantic role labelling tools, our event extraction approach optimises recall by 12.22–14.20 percentage points (reaching as high as 92.60% on one data set). Meanwhile, the use of Levin’s verb classes in attribution extraction obtains optimal performance in terms of F-score, outperforming a baseline method by 7.64–11.96 percentage points. Our proposed approach was applied on news articles focused on industrial regeneration cases. This facilitated the generation of accounts of events that are attributed to specific actors.     

An improved memory-event-triggered control for networked control systems

In this paper, the H_∞ control problem is investigated for a class of networked control systems with network-induced delay. A memory event-triggered scheme (METS) is proposed to reduce the redundant packet transmission in the network channel. Different from the normal event-triggered scheme (ETS), some recent released packets are stored at the event generator and controller sides, which are utilized for the first time to generate the triggered events and design the memory-based controller. The proposed METS has the following two merits. (1) The information of certain recent released signals are first utilized, which helps to improve the triggering instants at the crest or trough of the responses. (2) A state-dependent time-varying threshold parameter is designed, which can adjust the packet transmission rate according to the information of the state. Based on the proposed METS, a memory event-triggered controller is designed, the controller feedback gains and triggering parameters can be co-designed by solving a set of linear matrix inequalities. Finally, an example is given to illustrate the effectiveness of the proposed method.     

Tuning procedure for event-based PI controllers under regular quantization with hysteresis

In this paper a tuning procedure is proposed for event based PI controllers with Regular Quantization with Hysteresis (RQH) sampling law. The RQH is a generalization of Symmetric Send on Delta (SSOD) strategy which decreases the robustness requirements to avoid limit cycle oscillations and reduce the number of events needed for control, improving the overall performance of PI controllers in networked control systems. The tuning procedure takes into account not only classical robustness margins but also takes advantage of some specific robustness measures to avoid limit cycle oscillations induced by the sampler. As the robustness analysis depends on the Describing Function (DF) method, a study evaluating the effect of high order harmonics is provided, showing the validity of the tuning procedure. Some examples are included in which the usefulness of the tuning procedure is shown.     

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.     

SNSJam: Road traffic analysis and prediction by fusing data from multiple social networks

The increased popularity of micro-blogging applications together with the widespread of location-aware devices have resulted in the creation of large streams of geo-tagged data. Such data provides a great opportunity for researchers to explore event detection and prediction. In particular, road traffic detection and prediction are of great importance to various applications, i.e. Intelligent Transportation Systems. Current works proposed traffic jam detection from a single data source with a single language. However, for countries where the residents are speaking two, or more, languages and are interacting with more than one online social platform, single-language and single-source systems are insufficient to capture the necessary online information. Therefore, in this paper, we introduce SNSJam, an effective system to detect and predict road traffic jams using cross-lingual (English and Arabic) data collected from multiple dynamic sources, such as Twitter and Instagram. SNSJam classifier not only detect traffic events, but also identifies the causes of traffic jams. To identify the location of a traffic event, a Location Recognizer is developed that extracts locations from text and GPS of the post. Additionally, the Location Recognizer supports user-defined locations, which are common names among people. Our experiments show that by combining Arabic and English data streams, the accuracies of traffic events detection and prediction are significantly improved as compared with that of the individual languages. Additionally, combining data streams from multiple sources (Twitter and Instagram) further improved the accuracies of event detection and prediction over any individual source. A visualization interface was developed to show the detected spatio-temporal traffic events on a dynamic map. The detection and prediction results are validated against ground truth data obtained from the concerned authorities in the UAE.     

出租车监管措施效果的仿真预测模型

针对我国出租车监管措施的制定主要依据经验而缺乏理论支撑的现状,提出一种基于离散事件仿真的巡游型出租车服务模型。为真实模拟我国出租车路边随机搭载乘客居多的特点,假定出行需求分布在路网的节点和路段上。乘客根据价格和期望出行时间选择是否乘坐出租车,而空车根据有限的路网信息以及驾驶经验寻找乘客。模型能够模拟出租车在路网上搜索乘客并提供出行服务的动态过程,定量反映价格和配车数量与系统性能的关系,并预测改变起步价格、起步里程或者单位里程价格等监管措施的实施效果,为监管部门决策提供可靠的理论支持。     

Use of place information for improved event tracking

The main purpose of topic detection and tracking (TDT) is to detect, group, and organize newspaper articles reporting on the same event. Since an event is a reported occurrence at a specific time and place and the unavoidable consequences, TDT can benefit from an explicit use of time and place information. In this work, we focused on place information, using time information as in the previous research. News articles were analyzed for their characteristics of place information, and a new topic tracking method was proposed to incorporate the analysis results on place information. Experiments show that appropriate use of place information extracted automatically from news articles indeed helps event tracking that identify news articles reporting on the same events.     

Pinning synchronization of delayed complex networks under self-triggered control

In this paper, the pinning synchronization of delayed complex networks (DCNs) is investigated under self-triggered control (STC). The framework of synchronization analysis of DCNs under STC is established. Specifically, a new dynamic event-triggered scheme (DETS) is proposed for the DCNs firstly. The scheme concerns internal dynamic variables, which plays a crucial role in ensuring the exclusion of Zeno behavior. Secondly, to avoid continuous monitoring triggering condition, an effective self-triggered scheme (STS) is proposed. Different from the previous works, based on the extended Grönwall inequality, the lower bound for the inter-event time of the STS is estimated explicitly. A numerical example is provided to demonstrate the effectiveness of the theoretical results.     

ATM: Adversarial-neural Topic Model

Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address the limitations, we propose a neural topic modeling approach based on the Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM) in this paper. To our best knowledge, this work is the first attempt to use adversarial training for topic modeling. The proposed ATM models topics with dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. Besides, to illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. To validate the effectiveness of the proposed ATM, two topic modeling benchmark corpora and an event dataset are employed in the experiments. Our experimental results on benchmark corpora show that ATM generates more coherence topics (considering five topic coherence measures), outperforming a number of competitive baselines. Moreover, the experiments on event dataset also validate that the proposed approach is able to extract meaningful events from news articles.     

Convergent under-approximations of reachable sets and tubes: A piecewise constant approach

In this paper, a method to under-approximate finite-time reachable sets and tubes for a class of continuous-time linear uncertain systems is proposed. The class under consideration is the linear time-varying (LTV) class with time-varying integrable system matrices and uncertain initial and input values belonging to known convex compact sets. The proposed method depends upon the iterative use of constant-input reachable sets, which results in convergent under-approximations in the sense of the Hausdorff distance. As a consequence of the convergence, it is shown that interior points of reachable sets are attainable using piecewise constant inputs. The computational complexity of a zonotopic implementation of the proposed method is discussed and comparisons with existing under-approximation methods are established. Finally, the proposed approach is illustrated through two numerical examples.     

西南地区旱涝演变与ENSO事件的关系

在气候变化大背景下,西南地区旱涝频发,而ENSO（厄尔尼诺-南方涛动）是气候变化的重要指示性因子。本文利用海表温度距平（SSTA）、南方涛动指数（SOI）、西南地区近57年的降水和气温数据,运用小波分析和数理统计方法,分析ENSO事件与降水、气温及旱涝指数的相关关系。结果表明：①ENSO事件存在9 a、24 a和3~4 a左右的准周期变化。②降水量、气温与ENSO事件强度存在比较明显的相关关系,且存在一定的滞后性;在EI Nino（厄尔尼诺）事件发生的年份,西南地区的降水、气温与SSTA在滞后2~4个月时呈显著负相关;在La Nina（拉尼娜）事件发生的年份,西南地区的降水、气温与SSTA在滞后2~4个月时呈显著正相关关系;气温同ENSO事件相关关系更为明显。③ENSO事件强度与旱涝等级存在一定的负相关关系,当EI Nino事件强度增大时对应西南地区的干旱事件增多;当La Nina事件强度增大时,西南地区洪涝事件增多;旱涝指数SPI和SOI也呈极显著的负相关关系,SOI对旱涝的影响存在1~4个月的滞后期,并且在滞后3个月时影响最大。因此,西南地区在强ENSO事件发生的当年或次年易发生旱涝灾害,需要提高对旱涝事件的警惕,加强防汛抗旱工作。本文结果可为西南地区防汛抗旱工作提供参考。     

基于事理图谱的多维特征网络舆情演化路径研究

【目的/意义】基于事理图谱的方法对网络舆情事件进行揭示,能够准确分析舆情事件的发展趋势和脉络, 为政府部门的舆情管控和舆情引导献计献策。【方法/过程】以微博“长征5B失控”的相关主题与评论内容为研究对 象,对提取的数据进行清洗和处理、抽取和泛化,分别生成顺承事件对和因果事件对,并对其顺承关系和因果关系 进行识别,从而构建网络舆情事理图谱。【结果/结论】在网络舆情的发展过程中,可以看出因果事件的演化路径具 有时间发生短,演化路径短的特点,伴随着时间的推移,其演化的趋势也逐步降低;网络舆情顺承事件具有传播的 时间长,传播的路径多且具有多向性的传播特点,在顺承事件的传播过程中,往往伴随着因果事件,且舆情事件的 走向与网民的情绪有很大的关联。【创新/局限】构建基于事理图谱的网络舆情的演化路径,同时揭示网络舆情演化 的传播特点与现实意义,后续有必要扩大研究样本,使得研究结果具有更好地通用性。     

Optimal actuator switching synthesis of observer-based event-triggered state feedback control for distributed parameter systems

The study aims to explore the optimal actuator switching scheme of observer-based event-triggered state feedback control for distributed parameter systems. The performance of distributed parameter systems is improved through the observer-based event-triggered control, in which the state feedback is updated only when a triggered event happens. In such an event-triggered mechanism, the event-based closed-loop system and minimum time interval between consecutive events are bounded. Based on finite horizon linear quadratic regulator (LQR) optimal control, the optimal switching algorithm is proposed based on the event-triggered mechanism during an unfixed time interval. Finally, the proposed scheme is verified through a simulation case.