IMAGE AND FACE ANALYSIS FOR PERSONAL PHOTO ORGANIZATION

In recent years, digital cameras are becoming very commonplace and users need tools to manage large personal photo collections. In a typical scenario, a user acquires a certain number of pictures and then transfers this new photo sequence to his PC. Thus, before being added to the whole personal photo collection, it would be desirable that this new photo sequence is processed and organized. For example, users may be interested in using (i.e., browsing, saving, printing and so on) a subset of stored data according to some particular picture properties. For these reasons, automatic techniques for content-based description of personal photos are needed. Tools enabling an incremental organization of the photo album should take advantage from the particular properties of digital library. Indeed, personal photo collections show peculiar characteristics as compared to generic image collections, namely, a relatively small number of different individuals can be detected across the whole collection and, generally, it is possible to group the photos based on specific attributes. In such a scenario, the user is mainly interested in who is in the picture and where and when the picture was shot. Considering Who, where and when as the fundamental aspects of photo information, in this thesis it will be given a detailed description of novel approaches for content-based image retrieval. Novel image analysis techniques will be presented, focusing in particular on face information. Then, two novel frameworks for personal photo organization will be shown

SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications

The possibility of performing complex data analysis through sets of cooperating personal smart devices has recently encouraged the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis towards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. Unfortunately, because both of their distributed nature and high degree of modularity, edge-fog-cloud computing systems are particularly prone to cyber security attacks that can be performed against every element of the infrastructure. In order to address this issue, in this paper we present SMCP, a Secure Mobile Crowdsensing Protocol for fog-based applications that exploit lightweight encryption techniques that are particularly suited for low-power mobile edge devices. In order to assess the performance of the proposed security mechanisms, we consider as case study a distributed human activity recognition scenario in which machine learning algorithms are performed by users’ personal smart devices at the edge and fog layers. The functionalities provided by SMCP have been directly compared with two state-of-the-art security protocols. Results show that our approach allows to achieve a higher degree of security while maintaining a low computational cost

SpADe: Multi-Stage Spam Account Detection for Online Social Networks

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time

Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique

Unexpected different chemoselectivity in the aerobic oxidation of methylated planar catechin and bent epicatechin derivatives catalysed by the Trametes villosa laccase/1-hydroxybenzotriazole system

Unreported methylated catechin and epicatechin derivatives 5 and 6 were synthesized by an oxa-Pictet- Spengler reaction. Catechin 5 shows the B and C rings coplanar because of the formation of a trans junction between the C ring and the newly generated six-term cycle D, in turn condensed to ring B. In contrast, epicatechin 6 presents a bent geometry due to the establishment of a cis junction between the C ring and the newly formed cycle D. The oxidation of compounds 5 and 6 in the presence of the Trametes villosa laccase/1-hydroxybenzotriazole (HBT) system was investigated under aerobic conditions in both a biphasic system and a reverse micelle. The unexpected different chemoselective oxidation at the benzylic position of catechin and epicatechin derivatives 5 and 6 has been rationalized using a molecular modelling approach. These results demonstrate that the Trametes villosa laccase/HBT system represents a useful tool to functionalize the C-2 or C-4 position of phenolic compounds depending on the structural features

Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: a DES instance

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers

Contrast agents for hepatic magnetic resonance imaging.

The current availability of liver-specific contrast media (LSCM) allows the possibility to obtain an accurate diagnosis when studying focal liver lesions (FLL). It is necessary to have an in-depth knowledge of the biologic and histologic characteristics of FLL and the enhancement mechanism of LSCM to gain significant accuracy in the differential diagnosis of FLL. It is possible to subdivide FLL into three main groups according to the kinetics of contrast enhancement: hypervascular FLL, hypovascular FLL, and FLL with delayed enhancement. Dynamic contrast-enhanced magnetic resonance imaging is an important tool in the identification and characterization of FLL. LSCM with a first phase of extracellular distribution give both dynamic (morphologic) and late phase (functional) information useful for lesion characterization. With LSCM it is possible to differentiate with high accuracy benign from malignant lesions and hepatocellular from nonhepatocellular lesions. To understand contrast behavior after injection of LSCM, it is necessary to correlate contrast enhancement with the biologic and histologic findings of FLL

Nanoparticles exhibiting self-regulating temperature as innovative agents for Magnetic Fluid Hyperthermia

During the last few years, for therapeutic purposes in oncology, considerable attention has been focused on a method called magnetic fluid hyperthermia (MFH) based on local heating of tumor cells. In this paper, an innovative, promising nanomaterial, M48 composed of iron oxide-based phases has been tested. M48 shows self-regulating temperature due to the observable second order magnetic phase transition from ferromagnetic to paramagnetic state. A specific hydrophilic coating based on both citrate ions and glucose molecules allows high biocompatibility of the nanomaterial in biological matrices and its use in vivo. MFH mediator efficiency is demonstrated in vitro and in vivo in breast cancer cells and tumors, confirming excellent features for biomedical application. The temperature increase, up to the Curie temperature, gives rise to a phase transition from ferromagnetic to paramagnetic state, promoting a shortage of the r2 transversal relaxivity that allows a switch in the contrast in Magnetic Resonance Imaging (MRI). Combining this feature with a competitive high transversal (spin-spin) relaxivity, M48 paves the way for a new class of temperature sensitive T2 relaxing contrast agents. Overall, the results obtained in this study prepare for a more affordable and tunable heating mechanism preventing the damages of the surrounding healthy tissues and, at the same time, allowing monitoring of the temperature reached