1,722,000 research outputs found
IoT to Enhance Understanding of Cultural Heritage: Fedro Authoring Platform, Artworks Telling Their Fables
No full textCultural Heritage has got great importance in recent years, in order to preserve countries history and traditions and to support social and economic improvements. Typical IoT smart technologies represent an effective mean to support understanding of Cultural Heritage, by their capability to involve different users and to catch their explicit and implicit preferences, behaviors and contributions. This paper presents FEDRO, an authoring platform, as part of the intelligent infrastructures developed in DATABENC to support a cultural exhibition of “talking” sculptures held in the Southern Italy, in 2015. FEDRO aims to automatically generate textual and users profiled artworks biographies, employed to feed a smart app for guiding visitors during the exhibition. A preliminary experimentation revealed a tangible improvement in the users’ experience appreciation during the visit. Quality estimations of generated output were also computed exploiting users’ feedbacks, collected through a manual questionnaire, subscribed at the end of their visit
Recovering traceability links between business activities and software components
No full textThe relationships existing between a business process and the supporting software system is a critical concern for the organizations, as it directly affects their performance. The research described in this paper is concerned with the use of information retrieval techniques to software maintenance and, in particular, to the problem of recovering traceability links between the business process models and the components of the supporting software system. An information retrieval approach is introduced based on two processing phases including syntactic and semantic analysis. The usefulness of the approach is discussed through a case study. © 2010 Springer-Verla
Adversarial deep learning for energy management in buildings
No full textDeep learning is a powerful means to classify and thus optimize Energy management in Buildings. Deep learning is effective especially when the training dataset has a reduced volume or when the test set changes at a higher frequency than the training set. Notwithstanding these favourable properties, the classification with deep learning could be distorted by an adversary who can be interested to alter the classification of the energy consumption. Several kinds of fraud could require this attack, as those aimed at energy theft. In this paper we will provide experimental implants where a dataset is tampered with in order to lead the classifier to acquire it as valid, while it contains samples attributable to energy thefts
Recovering traceability links between business process and software system components
No full textThe relationships existing between a business process and the supporting software system is a critical concern for the organizations, as it directly affects their performance. The research described in this paper is concerned with the use of information retrieval techniques to software maintenance and, in particular, to the problem of recovering traceability links between the business process models and the components of the supporting software system. © 2010 IEEE
On managing security in smart e-health applications
No full textDistributed machine learning can give an adaptable but strong shared condition for the design of trusted AI applications; this is mainly due to lack of privacy of centralised remote learning mechanisms. This notwithstanding, also distributed approaches have been compromised by several attack models (mainly data poisoning): In such a situation, a malicious member of the learning party may inject bad data. As such applications are growing in criticality, learning models must face with security and protection just as with versatility issues. The aim of the paper is to improve these applications by providing extra security features for distributed and federated learning mechanisms: More in the details, the paper examines specific concerns such as the utilisation of blockchain, homomorphic cryptography and meta-modelling techniques to ensure protection as well as other non-functional properties
Cyber resilience meta-modelling: The railway communication case study
Full text linkRecent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements
Internet of things for driving human-like interactions
No full textCurrent smart IoT technologies have the potential to make a breakthrough in the support of Cultural Heritage (CH), by providing information and communication technology to enhance effectively current models of art recreation and enjoyment. To turn such potential into reality, IoT-based technological solutions for CH should be designed by taking into account two main factors: on the one hand, they must be able to involve and attract different types of users, on the other they must avoid focusing users' attention solely on the smartness and novelty of the supporting technologies, thus diverting them from living the experience of being in a cultural site. To this aim, endowing IoT applications with anthropic interfaces seems a promising way to explore, and most prominent among such interfaces are those based on capabilities for Natural Language Understanding and Generation. In this paper we propose a preliminary case study describing an IoT infrastructure supporting Human Computer Interaction (HCI) Models, designed for art recreation. An IoT infrastructure supports a system for Holographic Projections, driven by an NLP interaction, for users' enjoyment in cultural sites. Users' experiences were collected for supporting further analysis and improving the system tuning. Copyright is held by the owner/author(s)
Cosmological constraints from a joint analysis of cosmic growth and expansion
Full text linkCombining measurements on the expansion history of the Universe and on the growth rate of cosmic structures is key to discriminate between alternative cosmological frameworks and to test gravity. Recently, Linder proposed a newdiagram to investigate the joint evolutionary track of these two quantities. In this letter, we collect the most recent cosmic growth and expansion rate data sets to provide the state-of-the-art observational constraints on this diagram. By performing a joint statistical analysis of both probes, we test the standard Îcold dark matter model, confirming a mild tension between cosmic microwave background predictions from Planck mission and cosmic growth measurements at low redshift (z < 2). Then we test alternative models allowing the variation of one single cosmological parameter at a time. In particular, we find a larger growth index than the one predicted by general relativity Î3 = 0.65+0.05-0.04.However, also a standard model with total neutrino mass of 0.26 ± 0.10 eV provides a similarly accurate description of the current data. By simulating an additional data set consistent with next-generation dark-energy mission forecasts, we show that growth rate constraints at z > 1 will be crucial to discriminate between alternative models
A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking
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