Giancarlo Sperlì

Multimedia Social Network Modeling: A Proposal

In this paper we present a preliminary work concerning the definition of a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within a related environment. The proposed model is based on the hypergraph structure and allows us to represent in a simple way all the different kinds of relationships that are typical of a social network, in particular between multimedia content, between users and multimedia content and between users themselves, at the same time supporting several kinds of applications by means of the introduction of several ranking functions. In addition, we provide a strategy for mapping the proposed model into an object relational data model, for efficiently storing the related data. We also describe a first prototype that can store and integrate information from different OSNs (Facebook, Twitter) and multimedia sharing systems (Flickr, Lastfm), providing some facilities for browsing the entire hypergraph.

A Lexicon-Grammar Based Methodology for Ontology Population for e-Health Applications

Nowadays, the need for well-structured ontologies in the medical domain is rising, especially due to the significant support these ontologies bring to a number of groundbreaking applications, such as intelligent medical diagnosis system and decision-support systems. Indeed, the considerable production of clinical data belonging to restricted sub domains has stressed the need for efficient methodologies to automatically process enormous amounts of un-structured, domain specific information in order to make use of the knowledge these data provide. In this work, we propose a lexicon-grammar based methodology for efficient information extraction and retrieval on unstructured medical records in order to enrich a simple ontology descriptive of such a kind of documents. We describe the NLP methodology for extracting RDF triples from unstructured medical records, and show how an existing ontology built by a domain expert can be populated with the set of triples and then enriched through its linking to external resources.

Centrality in heterogeneous social networks for lurkers detection: An approach based on hypergraphs

Nowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure—based on the concept of “neighborhood” among users—that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in “interest‐based” social networks. Some experiments using the Yelp dataset are discussed.

KIRA: A System for Knowledge-Based Access to Multimedia Art Collections

One of the most important research area that during the last decade has taken advantage by the multimedia technologies is certainly the Cultural Heritage Information Management. In this paper, we present KIRA (Knowledge-based Information Retrieval from Art collections), a system to query, browse and analyze cultural digital contents from a set of distributed and heterogeneous multimedia repositories. KIRA provides all the necessary retrieval and presentation functionalities to search information of interest and present it to the users in a suitable format and according to their needs. By means of a set of ad-hoc APIs, our system can also support several applications: mobile multimedia guides for cultural environments, web portals to promote the cultural heritage of a given organization, multimedia recommender and storytelling systems and so on. We describe the main ideas that support the system, showing its use for several applications.

Recognizing Unexplained Behavior in Network Traffic

Intrusion detection and alert correlation are valuable and complementary techniques for identifying security threats in complex networks. Intrusion detection systems monitor network traffic for suspicious behavior, and trigger security alerts. Alert correlation methods can aggregate such alerts into multi-step attacks scenarios. However, both methods rely on models encoding a priori knowledge of either normal or malicious behavior. As a result, these methods are incapable of quantifying how well the underlying models explain what is observed on the network. To overcome this limitation, we present a framework for evaluating the probability that a sequence of events is not explained by a given a set of models. We leverage important properties of this framework to estimate such probabilities efficiently, and design fast algorithms for identifying sequences of events that are unexplained with a probability above a given threshold. Our framework can operate both at the intrusion detection level and at the alert correlation level. Experiments on a prototype implementation of the framework show that our approach scales well and provides accurate results.

Recommendation in Social Media Networks

In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising results.

Multimedia Social Network Modeling using Hypergraphs

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

A Multimedia Summarizer Integrating Text and Images

We present a multimedia summarizer system for retrieving relevant information from some web repositories based on the extraction of semantic descriptors of documents. In particular, semantics attached to each document textual sentences is expressed as a set of assertions in the \(\langle subject,verb,object \rangle \) shape as in the RDF data model. While, images’ semantics is captured using a set of keywords derived from high level information such as the related title, description and tags. We leverage an unsupervised clustering algorithm exploiting the notion of semantic similarity and use the centroids of clusters to determine the most significant summary sentences. At the same time, several images are attached to each cluster on the base of keywords’ term frequency. Finally, several experiments are presented and discussed.

Multimedia summarization using social media content

In this work, we propose a novel multimedia summarization technique from Online Social Networks (OSNs). In particular, we model each Multimedia Social Network (MSN)—i.e. an OSN focusing on the management and sharing of multimedia information—using an hypergraph based approach and exploit influence analysis methodologies to determine the most important multimedia objects with respect to one or more topics of interest. Successively, we obtain from the list of candidate objects a multimedia summary using a summarization model together with an heuristics that aims to generate summaries with priority (with respect to some user keywords), continuity, variety and not receptiveness features. The performed experiments on Flickr shows the effectiveness of proposed approach.

A Recommender System for Multimedia Art Collections

In this paper we present a novel user-centered recommendation approach for multimedia art collections. In particular, preferences (usually coded in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a particular sentiment), behavior (in the majority of cases logs of past items’ observations and actions made by users in the environment), and feedbacks (usually expressed in the form of ratings) are considered and integrated together with items’ features and context information within a general and unique recommendation framework that can support an intelligent browsing of any multimedia repository. Preliminary experiments show the utility of the proposed strategy to perform different browsing tasks.