Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid

Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR

Proposal of a correlation for boundary layer flow over real roughened surfaces

The effects of surface roughness on boundary layer features are analyzed through scaled facsimiles (flat plates) of real turbine blades. The main aim of the present work is to exploit the effect of surface roughness with particular reference to the steam turbine applications. Four rough plates and a smooth plate were tested at different high Reynolds number conditions and for different incidence angles of the flow respect to the plates. Roughness characteristics of the plates are typical of the turbine blades, in order to fully understand the aerodynamic efficiency improvement induced by a specific surface texture and to estimate the availability and convenience of manufacturing. All experimental results regarding the smooth plate are compared with the numerical ones obtained from the 2D cascade code MISES.Velocity profiles and boundary layer features are presented for all plates. Skin friction coefficient is estimated and laws of the wall are proposed for different roughness profiles. The effect of the grooves (riblets) is also investigated. The experimental data related to surface texture are analyzed and the most representative roughness parameters are underlined; it is found one unique roughness parameter which is representative of the real surface texture. The law of the wall for rough surfaces is correlated with the roughness parameter proposed. Moreover, a correlation between technical (sandgrain) and real surface roughness is proposed. Pressure gradient effect is also analyzed

ANALYSIS OF SPATIO-TEMPORAL DYNAMICS OF AEOLIAN PROCESSES IN ARID AND SEMI-ARID AREAS USING REMOTE SENSING

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Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?

Machine-learning algorithms trained on features extracted from static code analysis can successfully detect Android malware. However, these approaches can be evaded by sparse evasion attacks that produce adversarial malware samples in which only few features are modified. This can be achieved, e.g., by injecting a small set of fake permissions and system calls into the malicious application, without compromising its intrusive functionality. To improve adversarial robustness against such sparse attacks, learning algorithms should avoid providing decisions which only rely upon a small subset of discriminant features; otherwise, even manipulating some of them may easily allow evading detection. Previous work showed that classifiers which avoid overemphasizing few discriminant features tend to be more robust against sparse attacks, and have developed simple metrics to help identify and select more robust algorithms. In this work, we aim to investigate whether gradient-based attribution methods used to explain classifiers' decisions by identifying the most relevant features can also be used to this end. Our intuition is that a classifier providing more uniform, evener attributions should rely upon a larger set of features, instead of overemphasizing few of them, thus being more robust against sparse attacks. We empirically investigate the connection between gradient-based explanations and adversarial robustness on a case study conducted on Android malware detection, and show that, in some cases, there is a strong correlation between the distribution of such explanations and adversarial robustness. We conclude the paper by discussing how our findings may thus enable the development of more efficient mechanisms both to evaluate and to improve adversarial robustness

A Pilot Power Plant Based on Concentrating Solar and Energy Storage Technologies for Improving Electricity Dispatch

AbstractThis paper presents the main features and the expected performance of the pilot solar power plant under construction in Ottana (Sardinia-Italy). The facility is based on a 600 kWe concentrating solar power (CSP) plant with thermal energy storage, and a 400 kWe concentrating photovoltaic (CPV) plant with electrochemical storage. The CSP plant uses linear Fresnel collectors, thermal oil as heat transfer fluid, a two-tank direct storage system and an ORC module. The CPV plant consists of 37 dual-axis trackers integrated with Sodium-Nickel batteries. The facility is characterised by the integration of different concentrating solar and storage technologies. The pilot power plant has been designed in order to produce electricity with scheduled profiles according to weather forecast

Nanoporous Au Behavior in Methyl Orange Solutions

Nanoporous (NP) gold, the most extensively studied and efficient NP metal, possesses exceptional properties that make it highly attractive for advanced technological applications. Notably, its remarkable catalytic properties in various significant reactions hold enormous potential. However, the exploration of its catalytic activity in the degradation of water pollutants remains limited. Nevertheless, previous research has reported the catalytic activity of NP Au in the degradation of methyl orange (MO), a toxic azo dye commonly found in water. This study aims to investigate the behavior of nanoporous gold in MO solutions using UV-Vis absorption spectroscopy and high-performance liquid chromatography. The NP Au was prepared by chemical removal of silver atoms of an AuAg precursor alloy prepared by ball milling. Immersion tests were conducted on both pellets and powders of NP Au, followed by examination of the residual solutions. Additionally, X-ray photoelectron spectroscopy and electrochemical impedance measurements were employed to analyze NP Au after the tests. The findings reveal that the predominant and faster process involves the partially reversible adsorption of MO onto NP Au, while the catalytic degradation of the dye plays a secondary and slower role in this system

Preoperative Evaluation of Patients Undergoing Lung Resection Surgery: Defining the Role of the Anesthesiologist on a Multidisciplinary Team

IN THE FIELD of thoracic surgery, one of the key problems in lung resection is the management and function of the residual lung, which has the potential to interfere with both the pulmonary and cardiovascular systems, and, therefore, influence surgical outcome in terms of morbidity and mortality. Between 2007 and 2013, 5 papers addressing preoperative evaluation and risk stratification were published.1-5 However, the members of the task forces responsible for these documents did not include all the professionals involved in the preoperative surgical evaluation, and the documents mainly addressed the stratification of respiratory risk

Progetto Geosoglie

Il Centro Funzionale Decentrato (CFD) della Regione Sardegna si propone di sviluppare con il Progetto GEOSOGLIE una attività di ricerca e sviluppo sui temi del rischio idrogeologico e in particolare dei processi di innesco dei fenomeni franosi in funzione di specifici valori pluviometrici di soglia e della propensione al dissesto dei versanti. In questo lavoro viene presentata la struttura e lo sviluppo del progetto nelle sue fasi funzionali.The Decentralized Functional Centre (Centro Funzionale Decentrato, CFD) of Regione Autonoma della Sardegna, through the Project GEOSOGLIE, aims to develop research activities on hydrological risk issues and, in particolar, on the trigger processes of landslides according to specific rainfall thresholds. Moreover, the susceptivity to landslide will be mapped. In this paper we present the structure and the operational development of the project describing its functional phases

FLOWERED-GeoDBapp: applicazione per mobile basata sui crowd- generating data

Questo studio è parte del progetto H2020 FLOWERED (de-FLuoridation technologies for imprOving quality of WatEr and agRo-animal products along the East African Rift Valley in the context of aDaptation to climate change), coordinato dal Dipartimento di Scienze chimiche e geologiche dell’Università di cagliari, che vede la partecipazione di 14 partner di cui 5 provenienti dalla regione della Rift Valley africana (Etiopia, Kenya e Tanzania). Obiettivo generale del progetto iniziato nel 2016 e di durata triennale, è lo studio nelle tre aree test africane di un sistema di de - fluorizzazione dell’acqua contaminata per cause naturali basato sia su tecnologie adattabili al contesto sociale che su un attento sistema di gestione della risorsa acqua. Pertanto uno degli obiettivi del progetto è il coinvolgimento delle comunità locali nel processo sia di acquisizione delle conoscenze che di proposta dei sistemi di intervento. In questo ambito è stato proposto lo sviluppo di una piattaforma di condivisione di dati ambientali e socio- economici basata sula raccolta di dati da parte dei tecnici locali. La disponibilità e diffusione di tecnologie di accesso alla rete internet anche in territori remoti e con particolari condizioni sociali può consentire pertanto l’utilizzo di strumenti di gestione di dati geografici attraverso esperienze di PPGIS (Public Participation Geographic Information System) e VGI (Voluntary Geographic Information). In questo ambito si inserisce l’applicazione FLOWERED-GeoDBapp, che attraverso un processo di crowd-generating data intende proporre un sistema di passaggio land cover-land use attraverso il popolamento di un GEO DB basato su una mappa di land cover da classificazione di dati da satellite ad alta risoluzione con dati specifici sull’uso del territorio inseriti localmente. Sarà pertanto proposta una legenda di land use basata sulla raccolta di dati non solo di natura ambientale ma anche sociale, culturale ed economica, utilizzando le conoscenze locali e rendendo poi disponibili ai tecnici locali e agli amministratori le informazioni necessarie alla gestione delle risorse e in particolare dell’acqua