Roaming Service for Electric Vehicle Charging Using Blockchain-Based Digital Identity

 

Abstract

We present a suitable approach to address the electric vehicle charging roaming problem (e-roaming). Blockchain technologies are applied to support the identity management process of users charging their vehicles and to record energy transactions securely. At the same time, off-chain cloud-based storage is used to record the transaction details. A user wallet settled on a mobile application stores user verified credentials; a backend application in the vehicle charging station validates the user credentials to authorize the energy transaction. The current model can be applied to similar contexts where the user may be required to keep several credentials from different providers to authenticate digital transactions. View Full-Text

 

Keywords: roaming; electric vehicle; EV charging process; blockchain; IoT; mobile app

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

 

Abstract

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events. View Full-Text

Keywords: transportation; traffic; accidents; data-driven; data visualization; smart cities

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Planning process for an operational management platform for a public transport

 Abstract

This work addresses the planning process of a public passenger transport operator, including the generation of schedules and services for vehicles and drivers, in the framework of a previously agreed service. This problem will be studied in the context of all stages of the planning process: parameterization, preparation, production of performance indicators and the generation of results for different operational scenarios. View Full-Text

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Context aware advisor for public transportation

 Abstract

This paper presents an Android-based mobile app designed to provide real time context aware public transportation information and advice to its users through the combination of the user’s preferences and geographic context with data retrieved from a public transportation information system called XTraN Passenger. Thus, this mobile app contributes to fulfill the necessities of the passengers, and also provides an incentive for people to use the public transportation infrastructure more frequently. The proposed mobile app allows the users to benefit from the access to real time public transportation data in a simple and intuitive way. The validation of the features and operation of the developed app was assessed with results from use cases and real-world experimental tests using public transportation data from a Brazilian bus fleet operator. View Full-Text

Index Terms—Geographic Information System, Global Positioning System, Intelligent Transportation System, Mobile App, Personalization,Real Time Information.

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Extracting Clinical Knowledge from Electronic Medical Records

 Abstract

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources’ importance increases because of the clinical information they contain about patients. However, the unstructured information in the form of clinical narratives present in those records, makes it hard to extract and structure useful clinical knowledge. This unstructured information limits the potential of the EMRs, because the clinical information these records contain can be used to perform important tasks inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These tasks can only be done if the unstructured clinical information from the narratives is properly extracted, structured and transformed in clinical knowledge. Usually, this extraction is made manually by healthcare practitioners, which is not efficient and is error-prone. This research uses Natural Language Processing (NLP) and Information Extraction (IE) techniques, in order to develop a pipeline system that can extract clinical knowledge from unstructured clinical information present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential.  View Full-Text

 Keywords— Information Extraction, Knowledge Extraction, Natural Language Processing, Text Mining

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

An Energy Management Platform for Public Buildings

 

Abstract

This paper describes the development and implementation of an electronic platform for energy management in public buildings. The developed platform prototype is based on the installation of a network of wireless sensors using the emerging Long Range (LoRa) low power long-range wireless network technology. This network is used to collect sensor data, which is stored online and manipulated to extract knowledge and generate actions toward energy saving solutions. In this process, gamification approaches were used to motivate changes in the users’ behavior towards more sustainable actions in public buildings. These actions and the associated processes can be implemented as public services, and they can be replicated to different public buildings, contributing to a more energy-sustainable world. The developed platform allows the monitoring and management of the heating/cooling, electric power consumption, and lighting levels. In order to validate the proposed electronic platform, sensor information was collected in the context of a university campus, which was used as an application scenario in public buildings. View Full-Text

 

Keywords: energy management; sustainability; Internet of Things (IoT); LoRa network; wireless sensor network

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Disaster Management in Smart Cities

 

Abstract

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach. View Full-Text

 

Keywords: risk assessment; security; emergency systems; smart cities; disaster management; critical infrastructures

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

 

 

Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis

 

Abstract

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient’s monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images—being always the darker region—blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images. View Full-Text

 

Keywords: ultrasound images; coronary artery disease; echocardiograms; CT-scan; computed tomography; coronary artery calcium; feature extraction; image classification; computer vision 

 

 

 

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

Summer School 2021 – IoT for Heatlhy Smart Cities

 

ISTAR, an R&D Unit of ISCTE along with its Internet of Everything Lab, will provide a 2-week intensive Summer School on 

IoT technologies and applications with focus on Health applications for Smart Cities. 

See: https://istar.iscte-iul.pt/summerschool2021/ 

  

Participantes will be acquainted with emergent computing paradigms, technologies, 

services and business models associated with Internet of Things (IoT). 

  

The  objective  of  this  school  is  provide interested participants with theoretical-practical  and 

 experimental  knowledge  about  key IoT technologies and user-centered co-creation design methodologies, for development of intelligent systems towards healthy smart cities. 

  

The school provides an overview of future trends and ongoing research in this fast-growing area: 

·         Study the principles, research problems and applications of device, software and IoT 

·         Acquire experience with IoT technologies and operating systems 

·         Understand, in practical experience, how to run user-centered projects based in Design Thinking 

·         Help students to develop self-study skills to keep up with the rapidly changing technologies, tools and techniques in this knowledge area. 

  

  

The school runs  from September 6^th to 17^th 2021, and consists of 10 hours of expert lectures, anchored by 19 1/2 hours of hands-on training on key topics that include: 

·         Introduction to  IoT 

·         Design Thinking 

·         Security 

·         Tiny Machine Learning and Applications 

·         Applications and challenges on Health and Crowdsourcing 

·         There is an industrial session with presentations by three companies in Portugal that develop and apply IoT techniques. 

  

Participants will work on supervised, hands-on projects, developed at the ISTAR Internet of Everything Lab, 

as well as provided basic training on:  

·         IoT frameworks (e.g. Arduino, LORA communications, and sensors) 

·         Data science tools (Python, Data Analytics introduction) 

·         Design Thinking methodologies. 

  

Duration: 10 days | 6 ECTS 

  

More information:  https://istar.iscte-iul.pt/summerschool2021/ 

 

Pre-Registration is already open!  Express your interest at 

https://forms.office.com/pages/responsepage.aspx?id=YOgwYsW_lUCmvBBHIa3W5qX3JiiyZRBCsWIM63R8PKpUQ1dFUDgxSzJENDNKS0laNjVDQjRaUlg5Ny4u 

 

 Questions?  pre-award.istar@iscte-iul.pt 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

 

 

DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot

Abstract

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management. View Full-Text

Publication regarding the second artifact (work held in Chapter 4 and the artifact’s validation of Chapter 5) of the MSc thesis in MIBIS, from Boné J.

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

DisKnow: A Social-Driven Disaster Support Knowledge Extraction System

Abstract

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings. View Full-Text

Publication regarding the first artifact (work held in Chapter 3) of the MSc thesis in MIBIS, from Boné J.

 

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Data-Driven Approach for Incident Management in a Smart City

Abstract

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL