The Transmission Control Protocol.

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

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Processing Social Media Messages in Mass Emergency: A Survey

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

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Detection and Resolution of Rumours in Social Media: A Survey

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Social network analysis

The proposed social media crisis mapping platform for natural disasters uses locations from gazetteer, street map, and volunteered geographic information (VGI) sources for areas at risk of disaster and matches them to geoparsed real-time tweet data streams. The authors use statistical analysis to generate real-time crisis maps. Geoparsing results are benchmarked against existing published work and evaluated across multilingual datasets. Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources.

/pdf/real-time-crisis-mapping-of-natural-disasters-using-social-2u5pi6fpjf.pdf

Real-Time Crisis Mapping of Natural Disasters Using Social Media

Self-Healing is an emerging exigence for Information Systems where processes are more and more complicated and where many autonomous actors are involved. Self-healing mechanisms can be viewed as a set of automatic recovery actions fired at run-time according to the detected fault. These actions can be at infrastructure level (i.e., transparentl to the process), or they can be defined in the workflow model and executed by the workflow engine. Standard recovery mechanisms provided by Ws-BPEL are not enough to implement with reasonable effort lots of suitable recovery actions. The aim of this paper is to present a Self-Healing plug-in for a Ws-BPEL engine that enhances the ability of a standard engine to provide process-based recovery actions.

SH-BPEL: a self-healing plug-in for Ws-BPEL engines

The increasing success of wireless technologies is sustaining the diffusion of mobile information systems, but the youth of the underlying technology and its peculiar characteristics are impacting the development of such systems. For example, the execution of business processes in such a context must cope with the variable and fluctuating bandwidth available to the different devices. This leads the designer to stress the independence of each actor -- by minimizing interactions and knowledge sharing -- to increase the reliability of the whole system.

/pdf/workflow-partitioning-in-mobile-information-systems-17a4nphj9p.pdf

Workflow Partitioning in Mobile Information Systems

Self-Healing is an emerging exigency for Information Systems where processes are everyday more complicated and where many autonomous actors are involved Roughly, self-healing mechanisms can be viewed as a set of automatic recovery actions fired at run-time according to the detected fault These actions can be at infrastructure level, i.e transparently to the process, or they can be defined in the workflow model and executed by the workflow engine In the Service Oriented Computing world Ws-BPEL is the most used language for web-service orchestration, but standard recovery mechanisms provided by Ws-BPEL are not enough to implement, with reasonable effort, lots of suitable recovery actions.

This paper presents an approach where a designer defines a Ws-BPEL process annotated with some information about recovery actions and then a preprocessing phase, starting from this “annotated”Ws-BPEL, generates a “standard” Ws-BPEL, that is a file understandable for a standard Ws-BPEL engine This approach has the advantage of avoiding any change in the engine using the standard capabilities to define specific behaviors that will realize recovery actions, but at the end are still a set of Ws-BPEL basic and structured activities.

Methods for enabling recovery actions in Ws-BPEL

Future complex architectures for Web-Services-based delivery of services and objects require applications able to deal with different levels of Quality of Service (QoS) and able to describe also the quality of the associated object. In order to allow applications adapting in front of degraded quality in a pre-determined way, all the nodes participating in a given business process shall coordinate their efforts in (i) deciding how to deal with service degradation/disruption, and in (ii) managing the overall quality (i.e., not only the Web-Service QoS but also the quality of the objects being delivered through the cooperative process). According to this rationale, one of the main goals of the Quadrantis project has been to investigate how to provide combined quality measurement of services and delivered objects, such as videos, voice, texts, or images. QoS modelling and management according to users' needs are presented. Users will access services according to QoS Contracts negotiated with service providers, while the streaming content is based on user profiles, to face a more dynamic management of QoS deviations in the Real Time provisioning level. In our approach, applications can request QoS, and react and repair to violations of the terms set forth in contracts or in expected delivery parameters, such as bandwidth or frame rate. To this extent, we present a framework for providing applications and services with QoS adaptation functionality, through the step-wise induction of a monitoring and recovery solutions at both Web-Services and streaming layers. The foundations of the merging between the two layers, which deal with very different aspects and protocols, are based on quality.

Stefano Modafferi

Papers

Real-Time Crisis Mapping of Natural Disasters Using Social Media

SH-BPEL: a self-healing plug-in for Ws-BPEL engines

Workflow Partitioning in Mobile Information Systems

Methods for enabling recovery actions in Ws-BPEL

Analysis of QoS in cooperative services for real time applications