ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Multimodal Attentive Fusion Network for audio-visual event recognition

Event classification is inherently sequential and multimodal. Therefore, deep neural models need to dynamically focus on the most relevant time window and/or modality of a video. In this study, we propose the Multimodal Attentive Fusion Network (MAFnet), an architecture that can dynamically fuse visual and audio information for event recognition. Inspired by prior studies in neuroscience, we couple both modalities at different levels of visual and audio paths. Furthermore, the network dynamically highlights a modality at a given time window relevant to classify events. Experimental results in AVE (Audio-Visual Event), UCF51, and Kinetics-Sounds datasets show that the approach can effectively improve the accuracy in audio-visual event classification. Code is available at: https://github.com/numediart/MAFnet     

基于Watir的物联网数据处理方法研究

构建基于Watir的物联网Web事件处理系统框架。首先搭建Zigbee无线传感器网络,把终端节点的属性描述到网页并进行服务器部署,其中用微数据来描述每一个传感器的静态属性,用Js文件描述动态属性,然后采用Web自动化测试框架Watir,通过Css Selector定位技术实现对动态页面数据的实时采集,将实时采集的数据通过预处理后添加事件处理,提取关键和有效的事件数据并保存。同时利用Watir对多个事件条件下不同平台的物联网网页进行测试,结果表明基于Watir的物联网Web事件处理方法能准确和高效地获取事件数据。     

Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times

Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail.     

Variability in business processes: Automatically obtaining a generic specification

The existence of different process variants is inevitable in many modern organizations. However, variability in business process support has proven to be a challenge as it requires a flexible business process specification that supports the required process variants, while at the same time being compliant with policies and regulations. Declarative approaches could support variability, by providing rules constraining process behavior and thereby allowing different variants. However, manual specification of these rules is complicated and error-prone. As such, tools are required to ensure that duplication and overlap of rules is avoided as much as possible, while retaining maintainability. In this paper, we present an approach to represent different process variants in a single compound prime event structure, and provide a method to subsequently derive variability rules from this compound prime event structure. The approach is evaluated by conducting an exploratory evaluation on different sets of real-life business process variants, including a real-life case from the Dutch eGovernment, to demonstrate the effectiveness and applicability of the approach.     

Programming with event loops and control loops – From actors to agents

Event loops are a main control architecture to implement actors. In this paper we first analyse the impact that this choice has on the design of actor-based concurrent programs. Then, we discuss control loops as the main architecture adopted to implement agents, and we frame them as an extension of event loops effective to improve the programming of autonomous components that need to integrate both reactive and proactive behaviors, in a modular way.     

Quantifying target spotting performances with complex geoscientific imagery using ERP P300 responses

Geoscientific data interpretation is a challenging task, which requires the detection and synthesis of complex patterns within data. As a first step towards better understanding this interpretation process, our research focuses on quantitative monitoring of interpreters' brain responses associated with geoscientific target spotting. This paper presents a method that profiles brain responses using electroencephalography (EEG) to detect P300-like responses that are associated with target spotting for complex geoscientific data. In our experiment, eight interpreters with varying levels of expertise and experience were asked to detect features, which are likely to be copper–gold rich porphyry systems within magnetic geophysical data. The target features appear in noisy background and often have incomplete shape. Magnetic images with targets and without targets were shown to participants using the “oddball” paradigm. Event related potentials were obtained by averaging the EEG epochs across multiple trials and the results show delayed P3 response to the targets, likely due to the complexity of the task. EEG epochs were classified and the results show reliable single trial classification of EEG responses with an average accuracy of 83%. The result demonstrated the usability of the P300-like responses to quantify the geoscientific target spotting performances.     

Do OPEC announcements influence oil prices?

This paper investigates the effect of OPEC production decisions (increase, cut, maintain) on both WTI and Brent crude oil prices between Q1 1991 and Q1 2015 by employing the event study methodology and by using two indices as benchmarks (BCI and S&P GSCI). We employ an EGARCH model to take into account the high volatility of oil prices and some stylized facts characterizing this volatility. We find that the impact of OPEC’s announcements on oil prices (i)evolves over time and among decisions, (ii) is more significant for production cut and maintain, (iii) is different for WTI and Brent prices, and (iv) is sensitive to the benchmark index. Moreover, OPEC’s decisions depend on the exploration and extraction cost of more expensive/unconventional oil resources.     

Quantifying life safety Part II: Quantification of fire protection systems

In part I of the paper, an assembly building was analyzed in order to compute the failure probabilities, and thus the safety level, of current code-compliant buildings. In this second part, various fire protection systems are modeled within the fire and egress simulations in order to quantify their magnitude of impact. Since all fire protection systems can fail to perform as designed on demand, the potential failure along with its probability is accounted for in an event tree analysis. Comparing the resulting failure probabilities of the performance-based analyses with and without fire protection systems yields information about the magnitude of impact of the fire protection systems on the level of safety and hence allows a direct, objective, and quantitative comparison to other systems and designs. Accounting for the cost of the systems, a direct cost–benefit analysis can be conducted.     

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.