87,847 research outputs found
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)
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
Cosmological constraints from a joint analysis of cosmic growth and expansion
No full textCombining 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
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
Lung transplantation for emphysema.
No full textEmphysema is a major cause of morbidity and mortality worldwide. Despite optimal medical therapy, smoking cessation, and pulmonary rehabilitation, a large number of patients remain symptomatic with a poor quality of life. A pool of patients with end-stage disease can benefit from surgical treatments like bullectomy, lung volume reduction, or lung transplantation. Emphysema represents the most common indication leading to lung transplantation. A functional improvement and better quality of life are clear benefits deriving from lung transplantation, while a survival advantage has not yet been proven
Evaluating Convolutional Neural Network for Effective Mobile Malware Detection
No full textIn last years smartphone and tablet devices have been handling an increasing variety of sensitive resources. As a matter of fact, these devices store a plethora of information related to our every-day life, from the contact list, the received email, and also our position during the day (using not only the GPS chipset that can be disabled but only the Wi-Fi/mobile connection it is possible to discover the device geolocalization).This is the reason why mobile attackers are producing a large number of malicious applications targeting Android (that is the most diffused mobile operating system), often by modifying existing applications, which results in malware being organized in families, where each application belonging to the same family exhibit the same malicious behaviour. These behaviours are typically information gathering related, for instance a very widespread malicious behaviour in mobile is represented by sending personal information (as examples: the contact list, the received and send SMSs, the browser history) to a remote server managed by the attackers.In this paper, we investigate whether deep learning algorithms are able to discriminate between malicious and legitimate Android samples. To this end, we designed a method based on convolutional neural network applied to syscalls occurrences through dynamic analysis. We experimentally evaluated the built deep learning classifiers on a recent dataset composed of 7100 real-world applications, more than 3000 of which are widespread malware belonging to several different families in order to test the effectiveness of the proposed method, obtaining encouraging results. (C) 2017 The Authors. Published by Elsevier B.V
Superior vena cava replacement for lung cancer using a heterologous (bovine) prosthesis: Preliminary results
No full textSuperior vena cava replacement for lung cancer using a heterologous (bovine) prosthesis: Preliminary result
- …
