Browsing by Author "Caldeira, Filipe"
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- An Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers EmotionsPublication . Alves, Leandro; Oliveira, Pedro; Henriques, João; Bernardo, Marco V.; Wanzeller, Cristina; Caldeira, FilipeThe advertising industry is continuously looking up for effective ways to communicate to customers to impact their purchasing. Usually, profiling them is a time-consuming offline activity. Therefore, it becomes necessary to reduce costs and time to address consumers’ needs. This work proposes a scalable framework enabled by a Machine Learning (ML) model to profile customers to identify their emotions to help to drive campaigns. A multi-platform mobile application continuously profiles consumers crossing the front stores. Profiling customers according to their age and hair color, the color of their eyes, and emotions (e.g. happiness, sadness, disgust, fear) will help companies to make the most suitable advertisement (e.g. to predict whether the advertising tones on the front store are the adequate ones). All that data are made available in web portal dashboards, wherein subscribers can take their analysis. Such results from the analysis data help them to identify tendencies regarding the culture and age, and drive companies to fit front stores accordingly (e.g. to discover the right tones for the season). This framework can help to develop new innovative cost-effective business models at scale by driving in real-time the advertisements to a huge number of consumers to maximize their impact and centralizing the data collected from a large number of stores to design future campaigns.
- An automated closed-loop framework to enforce security policies from anomaly detectionPublication . Henriques, João; Caldeira, Filipe; Cruz, Tiago; Simões, PauloDue to the growing complexity and scale of IT systems, there is an increasing need to automate and streamline routine maintenance and security management procedures, to reduce costs and improve productivity. In the case of security incidents, the implementation and application of response actions require significant efforts from operators and developers in translating policies to code. Even if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend. To overcome those issues, in this paper we propose an automated closed-loop process with three stages. The first stage focuses on obtaining the Decision Trees (DT) that classify anomalies. In the second stage, DTs are translated into security Policies as Code based on languages recognized by the Policy Engine (PE). In the last stage, the translated security policies feed the Policy Engines that enforce them by converting them into specific instruction sets. We also demonstrate the feasibility of the proposed framework, by presenting an example that encompasses the three stages of the closed-loop process. The proposed framework may integrate a broad spectrum of domains and use cases, being able for instance to support the decide and the act stages of the ETSI Zero-touch Network & Service Management (ZSM) framework.
- An Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision MakingPublication . Martins, Anthony; Martins, Pedro; Caldeira, Filipe; Sá, Filipe; Rocha, {\'AAnalyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization. This article reviews data warehouse concepts and their appropriate use in business intelligence projects with a focus on large amounts of information. Nowadays, data volume is more significant and critical, and proper data analysis is essential for a successful project. From importing data to displaying results, there are crucial tasks such as extracting information, transforming it analyzing, and storing data for later querying. This work contributes with the proposition of a Big Data Business Intelligence architecture for an efficiently BI platform and the explanation of each step in creating a Data Warehouse and how data transformation is designed to provide useful and valuable information. To make valuable information useful, Business Intelligence tools are presented and evaluates, contributing to the continuous improvement of business results.
- An evolved security architecture for distributed industrial automation and control systemsPublication . Rosa, L.; Proença, J.; Henriques, João; Graveto, V.; Cruz, T.; Simões, P.; Caldeira, Filipe; Monteiro, E.Over the recent years, control and sensor systems used for IACS (Industrial Automation and Control Systems) have become more complex, due to the increasing number of interconnected distributed devices, sensors and actuators. Such components are often widely dispersed in the field – this is the case for microgeneration (wire-to-water generation, solar or wind), smart metering, oil and gas distribution or smart water management, among others. This IoT (Internet of Things)-centric IACS paradigm expands the infrastructure boundaries well beyond the single or aggregated-plant, mono-operator vision (mostly associated with geographically constrained systems topologies), being dispersed over a large geographic area, with increasingly small areas of coverage as we progress towards its periphery. This situation calls for a different approach to cyber threat detection, which is one of the most relevant contributions of the ATENA (Advanced Tools to assEss and mitigate the criticality of ICT components and their dependencies over critical infrAstructures) H2020 project (ATENA 2016). This paper presents and describes the ATENA cyber-security architecture, designed for the emerging generation of distributed IoT IACS, leveraging technologies such as Software Defined Networking/Network Function Virtualization and Big data event processing) within the scope of a cyber-detection architecture designed to deal with the inherent challenges of dispersed IACS, involved different operator domains.
- An Intelligent and Scalable IoT Monitoring Framework for Safety in Civil Construction WorkspacesPublication . Ferreira, Carolina; Correia, Luciano; Lopes, Manuel; Henriques, João; Martins, Pedro; Wanzeller, Cristina; Caldeira, FilipeKeeping civil construction workers safe is an important challenge due to working conditions and low technological support due to the inherent costs. This work surveys the literature and proposes a scalable framework for monitoring workers to minimize the response time with real-time warnings in hazardous situations or safety incidents. From the literature, it was possible to devise a gap in business addressing this problem. To address this problem, this work proposes an IoT scalable framework able to scale to a large number of civil construction companies with a large number of workers in order. The results from this work demonstrate the feasibility of the proposed framework and the low cost of the IoT solution and the scalability of the framework offers the opportunity to leverage new innovative business models capable to leverage their revenues.
- An Overview on Cloud Services for Human TrackingPublication . Martins, Manuel; Mota, David; Martins, Pedro; Abbasi, Maryam; Caldeira, FilipeThis paper reflects the intention to test the use of public cloud services to assess the presence of humans in a given space, more precisely, multiple stores, with the least effort and in the fastest way. It is also intended to demonstrate that the use of the public cloud can be an instrument of added value in business areas and research areas. In the specific case, the cloud was used to train and use artificial intelligence models.
- An overview on how to develop a low-code application using OutSystemsPublication . Martins, Ricardo; Caldeira, Filipe; Sá, Filipe; Abbasi, Maryam; Martins, PedroThe motivation for developing a self-service platform for employees arises precisely from the idea that in all organizations there are tasks that could be automated in order to redirect work resources to more important tasks. The proposed application consists of the development of a self-service platform, for personal information and scheduling tasks, aimed at the employees instead of all the solutions that are in the market that aim their platform to the Human Resources. We focus on the employers giving them more responsibility to make their own personal management like, change their personal info, book their vacations and other, giving to the Human Resources the tasks of managing all these actions made by the employers. At the end of the work, it is expected that the final solution to be considered as an example of success with regards to the theme of business automation and innovation, using the low-code application Outsystems to perform the full proposed application development.
- Analysis and real-time data of meteorologic impact on home solar energy harvestingPublication . Ferreira, João; Lourenço, Ismael; Henriques, João; Pires, Ivan Miguel; Caldeira, Filipe; Wanzeller, CristinaSolar energy production increased in the world from 0 TWh in 1965 to 724.09 TWh in 2019. Solar energy is adopted as a source for residential renewable energy sources because, besides Biomass sources, it’s the only one that can be installed and maintained at home. Operating efficiency is an important consideration when evaluating the application of photovoltaic panels (PV) technology. A real-time system monitoring is required to analyse the current production and understand the impact of the weather conditions on PV production. This paper extends the literature on the residence solar energy harvesting subject, by providing a scalable architecture that can be used as starting point on data analysis on PV panels efficiency and how weather conditions impact energy production. A dataset was collected related to PV panel energy production, the residence energy consumption and that’s reading weather conditions. Wind intensity and direction, temperature, precipitation, humidity, atmospheric pressure and radiation were weather conditions analysed. Moreover, this data was analysed and interpreted in order to evaluate the pros and cons of the architecture as well as how the weather impacted the energy production.
- Assurance and trust indicators to evaluate accuracy of on-line risk in critical infrastructuresPublication . Schaberreiter, Thomas; Caldeira, Filipe; Aubert, Jocelyn; Monteiro, Edmundo; Khadraoui, Djamel; Simoes, PauloCritical infrastructure (CI) services are consumed by the society constantly and we expect them to be available 24 hours a day. A common definition is that CIs are so vital to our society that a disruption or destruction would have a severe impact on the social well-being and the economy on national and international levels. CIs can be mutually dependent on each other and a failure in one infrastructure can cascade to another (inter)dependent infrastructure and cause service disruptions. Methods to better assess and monitor CIs and their (inter)dependencies at run-time in order to be able to evaluate possible risks have to be developed. Furthermore, methods to ensure the validity of evaluated risk have to be investigated. In this work, we build on existing work of CI security modelling, a CI model that allows modelling the risks of CI services at run-time. We conduct a study of indicators allowing to evaluate the correctness of calculated service risk, taking into account various sources contributing to this evaluation. Trust-based indicators are introduced to capture the dynamically changing behaviour of a system.
- Beacons positioning detection, a novel approachPublication . Morgado, Francisco; Martins, Pedro; Caldeira, FilipeRecent Bluetooth Low Energy (BLE) beacons provide new opportunities to explore positioning. Beacon positioning determination using current approaches is supported by pre-calculated formulas, for generic beacons, whereas the position can be accurately estimated with a low error up to a small distance; or based on fingerprinting the signal for the given space. In both cases, the accuracy variate depending on hardware specifications and other conditions such as beacon brand, wrap material, temperature, wind, location, surrounding interference, battery strength, among others. This paper introduces a method for beacon-based positioning, based on signal strength measurements at key distances for each beacon. This method allows for different beacon types, brands, and conditions. Depending on each situation (i.e., hardware and location) it is possible to adapt the distance measuring curve to minimize errors and support higher distances, while at the same time keeping good precision. Moreover, this paper also presents a comparison with traditional positioning method, using formulas for distance estimation, and then position triangulation. Performed tests took place at the library of the campus of the Polytechnic Institute of Viseu. Experimental results show that the proposed position technique has 13.2% better precision than triangulation, for distances up to 10 meters.