SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies

Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap

Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with Application

A Web-based Database System for Providing Technical Information on ATM Networking Platforms

This paper presents a Web-based database hosting technical information about pioneering ATM networking platforms, associated research activities engaging these platforms, and related important trials conducted in the framework of these research activities. The paper outlines the organisation and structure of the information content in the database and discusses methods of access through the WWW interface. Besides the “static” information offered by the database, other Java-based tools provide for the on-line monitoring of the status of the ATM platforms and for manipulating data arising from technological trials on these platforms. The integration of these tools with the database, under a common WWW interface is discussed

Towards Interoperable IoT Deployments inSmart Cities - How project VITAL enables smart, secure and cost- effective cities

International audienceIoT-based deployments in smart cities raise several challenges, especially in terms of interoperability. In this paper, we illustrate semantic interoperability solutions for IoT systems. Based on these solutions, we describe how the FP7 VITAL project aims to bridge numerous silo IoT deployments in smart cities through repurposing and reusing sensors and data streams across multiple applications without carelessly compromising citizens’ security and privacy. This approach holds the promise of increasing the Return-On-Investment (ROI), which is associated with the usually costly smart city infrastructures, through expanding the number and scope of potential applications

Towards an interoperability certification method for semantic federated experimental IoT testbeds

IoT deployments and then related experiments tend to be highly heterogeneous leading to fragmented and non-interoperable silo solutions. Yet there is a growing need to interconnect such experiments to create rich infrastructures that will underpin the next generation of cross sector IoT applications in particular as using massive number of data. While research have been carried out for IoT test beds and interoperability for some infrastructures less has been done on the data. In this paper, we present the first step of the FIESTA certification method for federated semantic IoT test bed, which provides stakeholders with the means of assessing the interoperability of a given IoT testbed and how it can be federated with other ones to create large facility for experimenter. Focus is given on data and semantic context of the test beds and how they can interoperate together for larger experiments with data

Connecting physical things to a SmartCity-OS

International audienceA Smart City can be seen as a system in which different Internet of Things (IoT) solutions coexist and cooperate. According with this vision, the number of IoT deployments is, nowadays, in continuous expansion and it involves disparate scenarios, from street lighting, waste management, etc. However those initiatives are standalone, based on different protocols and standards, while the Smart City concept requires, on the other hand, integration and interoperability among all its stakeholders. To face this problem, in this paper we introduce the VITAL-OS architecture, that can monitor, visualize, and control all the operations of a city. Then, we present a practical use case of connecting a Sensor Network to this OS and we describe eCACHACA, a ranking mechanism that facilitates the discovery of services provided by each sensor. Performance has been evaluated via experimentation on the FIT IoT-LAB, and results demonstrate the effectiveness in the discovery of resources

Integrating Wireless Sensor Networks within a City Cloud

International audienceSmart City solutions are currently based on multiple architectures, standards and platforms, which have led to a highly fragmented landscape. In order to allow cities to share data across systems and coordinate processes across domains, it is essential to break these silos. A way to achieve the purpose is sensor virtualization, discovery and data restitution. In this paper, a federation of FIT IoT-LAB within OpenIoT is presented. OpenIoT is a middleware that enables the collection of data streams from multiple heterogeneous geographically dispersed data sources, as well as their semantic unification and streaming with a cloud infrastructure. Future Internet of Things IoT-LAB (FIT IoT-LAB) provides a very large scale infrastructure facility suitable for testing small wireless sensor devices and heterogeneous communicating objects. The integration proposed represents a way to reduce the gap existing in the IoT fragmentation, and, moreover, allows users to develop smart city applications by interacting directly with sensors at different layers. We illustrate it trough a basic temperature monitoring application to show its efficiency

Utility Metrics Specifications. OpenIoT Deliverable D422

This deliverable specifies the utility metrics that are considered and used in the scope of the OpenIoT project. These utility metrics are recorded as part of the implementation of the Utility Manager component of the OpenIoT platform, while they have also been used to drive the utility based optimization mechanisms of the project. In particular we provided the following contributions: We provide an analysis and summary of utility metrics for different data providers and environments, including physical sensors, sensor networks, and virtual sensors. These metrics can be used to measure utility for interconnected objects. We proposed utility functions that use metrics in order to compute valuation and cost functions. These functions can be used by utility-based optimization techniques. The utility based schemes proposed provide means and algorithms that can help selecting virtual sensors for efficient data collection. We describe utility metrics, tailored specifically for the OpenIoT use cases, indicating the relevant parameters (e.g. location, bandwidth, availability, privacy), and cost and valuation functions (if applicable)

Visual Development Environment for Semantically Interoperable Smart Cities Applications

International audienceThis paper presents an IoT architecture for the semantic interoperability of diverse IoT systems and applications in smart cities. The architecture virtualizes diverse IoT systems and ensures their modelling and representation according to common standards-based IoT ontologies. Furthermore, based on this architecture, the paper introduces a first-of-a-kind visual development environment which eases the development of semantically interoperable applications in smart citites. The development environment comes with a range of visual tools, which enable the assembly of non-trivial data-driven applications in smart cities, including applications that leverage data streams from diverse IoT systems. Moreover, these tools allow developers to leverage the functionalities and building blocks of the presented architecture. Overall, the introduced visual environment advances the state of the art in IoT developments for smart cities towards the direction of semantic interoperability for data driven application

Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes:The PeRSonal GDM model

BACKGROUND: The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS: A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis. FINDINGS: The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome. INTERPRETATION: The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria. FUNDING: This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847