This study contributes to the knowledge of the reality, the risk assessment and the definition of fraud prevention strategies associated with the use of European funds in Portugal.  The main objectives of this study are: * To collect information, process, systematise and analyse data on European funds in Portugal, particularly regarding situations of irregularities in their use; * To identify opportunities for improvement in the process of data collection, in the management of information systems, and in the sharing of information between the various organisations involved;  * To increase transparency in the use of funds in Portugal.  Entities involved: Iscte, NovaSBE, ADC, IFAP, PGR Project starting date: 1 April 2022 Duration: 12 months

The object of study is the timely prediction of the possibility of non-compliance in terms of timings or financial targets. This research will test the potential of a system capable of generating an alerts for the possibility of non-compliance based on known data at the time of application or at key moments in the project's monitoring. This alert should be substantiated according to the studied variables that are directly involved in this result, in order to support a properly informed decision.   

The ECI4.0 project aims to develop and validate a prototype of multimodal platform for intelligent analysis of costumer behvior in comercial spaces using computer-vision, sensor fusion, and machinel learning techniques to enable an advancement in terms of the ambient intelligence for specialized retail spaces.

The project aims to improve incentive management, using machine learning approaches to identify operational and strategic risk levels in the analysis and verification phases of project payment requests.

Mobile security faces serious challenges, with alarming threat levels of malicious applications (malware). Malware applications attempt to capture user’s private data for illicit purposes, namely financial data, of personal context (such as location), business / corporate or other kinds of valuable information.  To address this problem the AppSentinel project proposes that App Stores should incorporate proactive and intelligent anti-malware mechanisms themselves, given its privileged position between developers and end-users. In this sense, we propose to research and develop an intelligent anti-malware system for Android App Stores, capable of performing static and dynamic analysis of malicious applications from several sources and understand their behavior patterns, which will then be used in testing new applications submitted to these stores. Moreover, these new applications will also be tested regarding good practices in secure mobile software development, which will lead to educational feedbacks to developers. Finally, a supervised machine learning system will be investigated and developed for efficient detection of new malicious applications based on users’ feedback. With these technological innovations we intend to reduce the incidence of malware on mobile devices, increase the efficiency in the analysis of virus reported by users and accelerate the reaction to new threats, and contribute to the adoption of secure mobile software development practices by developers.

IT tasks:a) Support in the definition of the technologies to apply in the Decision Support System to implement in the project;b) Support, the implementation, with technical revisions;c) Coordination of the MsC. thesis associated to the project;d) Coordination of the scientific dissemination activities.