EAIMS: Emergency Analysis Identification and Management System

Social media has great potential as a means to enable civil protection and law enforcement agencies to more effectively tackle disasters and emergencies. However, there is currently a lack of tools that enable civil protection agencies to easily make use of social media. The Emergency Analysis Identification and Management System (EAIMS) is a prototype service that provides real-time detection of emergency events, related information finding and credibility analysis tools for use over social media during emergencies. This system exploits machine learning over data gathered from past emergencies and disasters to build effective models for identifying new events as they occur, tracking developments within those events and analyzing those developments for the purposes of enhancing the decision making processes of emergency response agencies

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Opportunities and Risks of Disaster Data from Social Media: A Systematic Review of Incident Information

Compiling and disseminating information about incidents and disasters is key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether, and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 large disaster and incident databases covering 27 incident types, organize the contained data and its collection process, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: impact assessment and verification of model predictions, narrative generation, enabling enhanced citizen involvement, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance. Aside from this analysis, we discuss the advantages and disadvantages of the use of social media data for closing information gaps related to incidents and disasters

What happens where during disasters? A Workflow for the multifaceted characterization of crisis events based on Twitter data

Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster- and event-related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions

Review article: Detection of actionable tweets in crisis events

Messages on social media can be an important source of information during crisis situations. They can frequently provide details about developments much faster than traditional sources (e.g., official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of relevant messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowdsourced methods. Lately, approaches have shifted towards an automatic analysis of messages. A major stumbling block here is the question of exactly what messages are considered relevant or informative, as this is dependent on the specific usage scenario and the role of the user in this scenario. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, leading into the concept of use case-dependent actionability that has recently become more popular and is the focal point of the review paper. This is followed by an overview of existing crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning technique. We analyze their suitability and limitations of the approaches with regards to actionability. We then point out particular challenges, such as the linguistic issues concerning social media data. Finally, we suggest future avenues of research and show connections to related tasks, such as the subsequent semantic classification of tweets

THE INFLUENCE OF REGULAR BREAST MILK PUMPNG IN THE EARLY POSTPARTUM PERIOD ON BREASTFEEDING DURATION AND ACHIEVEMENT OF BREASTFEEDING INTENTION AMONG WOMEN IN THE U.S.

Most mothers do not breastfeed in accordance with health recommendations. Returning to work can be a barrier to breastfeeding due to mother-infant separation. Expressing breast milk (pumping) is a way for mothers to continue breastfeeding when separated from their infants, but it is not known if regular pumping influences breast milk feeding duration and achievement of breastfeeding intentions. I estimated effects of regular pumping compared to non-regular/not pumping (noted as “non-regular”) reported at month 2 on time to breast milk feeding (BMF) cessation (to 12 months) and time to exclusive BMF cessation (to six months), as well as achievement of breastfeeding and exclusive breastfeeding intentions, overall and for working and non-working women. I imputed missing data with 100 replications and used statistical weights to control for confounding and to address selection bias from the study design and drop-outs. Overall, regular pumpers were more likely to stop BMF and exclusive BMF (weighted hazard ratio (wHR) 1.62; 95% confidence interval (CI) 1.47 – 1.78 for BMF and wHR 1.14; 95% CI 1.03 – 1.25 for exclusive BMF), and less likely to meet their breastfeeding intentions than non-regular pumpers (weighted risk ratio (wRR) 0.79; 95% CI 0.67 – 0.94). I observed no difference in achievement of exclusive BMF intention (wRR 1.05; 95% CI 0.84 – 1.31). Among working women, I observed no effect of regular pumping compared to non-regular pumping, in terms of time to BMF cessation (wHR 0.90; 95% CI 0.75 – 1.07), exclusive BMF cessation (wHR 1.15; 95% CI 0.96 – 1.37), or achievement of breastfeeding intention (wRR 1.08; 95% CI 0.81 – 1.43) or exclusive breastfeeding intention (wRR 1.36; 95% CI 0.95 – 1.95). Among non-working women, regular pumpers had an increased hazard of BMF cessation (wHR 2.05; 95% CI 1.84 – 2.28), but not of exclusive BMF cessation (wHR 1.10; 95% CI 0.98 – 1.22), compared to non-regular pumpers. Regular pumpers were less likely to meet breastfeeding intentions (wRR 0.69; 95% CI 0.56 – 0.85), but had no difference in risk of not meeting exclusive breastfeeding intentions (wRR 0.70; 95% CI 0.69 – 1.22), than non-regular pumpers. Regular pumpers may need specialized support to maintain BMF and to achieve their intentions.Doctor of Philosoph

Air Traffic Management Abbreviation Compendium

As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages. In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation

Deliverable D6.3. Gap analysis final report including prioritization

The document describes the outcomes of the gap analysis carried out in the ConnectinGEO projec

Why do aid information management systems fail? Understanding global diffusion of data-driven development initiatives and sustainability failure in the case of Indonesia

Aid information management systems (AIMS) have been implemented in aidreceiving countries with the hope that they will enable donors and recipient governments to share aid information, enhance data governance and aid coordination among stakeholders. Despite the global popularity of data-driven development initiatives and heavy investment in AIMS, many systems have not fulfilled the expected outcomes. This research seeks to explain this failure from an information systems perspective. Building on a historical overview of AIMS implementation, I first develop an understanding of how such systems evolved and how the visions of aid effectiveness norms that AIMS inscribed have changed over time alongside the shifting global aid governance. This overview clearly shows that, in many cases, AIMS did not attain the result anticipated, and often failed to reach sustainability. I then investigate this sustainability failure, through an interpretive case study of Indonesian AIMS. I trace the change of international and domestic aid governance that shaped the unique context of AIMS in the emerging economy. Investigating the role of state actor, I argue that understanding the failure of AIMS requires a shift of attention from the process of aid management within a country to the global level. It needs to be seen as a result of macro-level events occurring in the global field of aid. In the dynamics of global power relations, the role of technology is multifaceted—a mixture of managerial and rationalizing, as well as symbolic and political roles

Queensland University of Technology: Handbook 2001

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT