Fabio Persia

A Multimedia Recommender System

The extraordinary technological progress we have witnessed in recent years has made it possible to generate and exchange multimedia content at an unprecedented rate. As a consequence, massive collections of multimedia objects are now widely available to a large population of users. As the task of browsing such large collections could be daunting, Recommender Systems are being developed to assist users in finding items that match their needs and preferences. In this article, we present a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem. In social choice theory, a set of voters is called to rank a set of alternatives, and individual rankings are aggregated into a global ranking. In our formulation, the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We first define what constitutes a choice in the browsing domain and then define a mechanism to aggregate individual choices into a global ranking. The result is a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users. Recommendations are ranked using an importance ranking algorithm that resembles the well-known PageRank strategy. Experiments conducted on a prototype of the proposed system confirm the effectiveness and efficiency of our approach.

Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data

There are numerous applications where we wish to discover unexpected activities in a sequence of time-stamped observation data-for instance, we may want to detect inexplicable events in transactions at a website or in video of an airport tarmac. In this paper, we start with a known set A of activities (both innocuous and dangerous) that we wish to monitor. However, in addition, we wish to identify “unexplained” subsequences in an observation sequence that are poorly explained (e.g., because they may contain occurrences of activities that have never been seen or anticipated before, i.e., they are not in A). We formally define the probability that a sequence of observations is unexplained (totally or partially) w.r.t. A. We develop efficient algorithms to identify the top-k Totally and partially unexplained sequences w.r.t. A. These algorithms leverage theorems that enable us to speed up the search for totally/partially unexplained sequences. We describe experiments using real-world video and cyber-security data sets showing that our approach works well in practice in terms of both running time and accuracy.

Modeling recommendation as a social choice problem

In the classical theory of social choice, a set of voters is called to rank a set of alternatives and a social ranking of the alternatives is generated. In this paper, we model recommendation in the context of browsing systems as a social choice problem, where the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We then propose an importance ranking method that strongly resembles the well known PageRank ranking system, and takes into account both the browsing behavior of the users and the intrinsic features of the objects in the collection. We apply the proposed approach in the context of multimedia browsing systems and show that it can generate effective recommendations and can scale well for large data collections.

Finding unexplained activities in video

Consider a video surveillance application that monitors some location. The application knows a set of activity models (that are either normal or abnormal or both), but in addition, the application wants to find video segments that are unexplained by any of the known activity models -- these unexplained video segments may correspond to activities for which no previous activity model existed. In this paper, we formally define what it means for a given video segment to be unexplained (totally or partially) w.r.t. a given set of activity models and a probability threshold. We develop two algorithms - FindTUA and FindPUA - to identify Totally and Partially Unexplained Activities respectively, and show that both algorithms use important pruning methods. We report on experiments with a prototype implementation showing that the algorithms both run efficiently and are accurate.

A System for Automatic Image Categorization

Traditional multimedia classification techniques are based on the analysis of either low-level features or annotated textual information. Instead, the semantic gap between rough data and its content is still a challenging task. In this paper, we describe a novel solution which automatically associates the image analysis and processing algorithms to keywords and human annotation. We use the well known \FLICK\ system, that contains images, tags, keywords and sometimes useful annotation describing both the content of an image and personal interesting information describing the scene. We have carried out several experiments demonstrating that the proposed categorization process achieves quite good performances in terms of efficiency and effectiveness.

Challenge: Processing web texts for classifying job offers

Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.

A Low-Cost Haptic System for Wrist Rehabilitation

While the regular treatment for wrist stiffness is physical therapy or surgery, researchers are looking for an alternative, more efficient and automatic procedure by means of robotic applications. In this paper, we propose a low-cost system exploiting a haptic interface aided by a glove sensorized on the wrist allowing the identification of the wrist orientation, in this way, by using virtual reality, the patient is able to make some motions that are very essential for his/her rehabilitation process and, by exploiting the wrist orientation tracking, he/she can be trained and guided to reach different goals of increasing complexity.

A ranking method for multimedia recommenders

In the last few years, recommender systems have gained significant attention in the research community, due to the increasing availability of huge data collections, such as news archives, shopping catalogs, or virtual museums. In this scenario, there is a pressing need for applications to provide users with targeted suggestions to help them navigate this ocean of information. However, no much effort has yet been devoted to recommenders in the field of multimedia databases. In this paper, we propose a novel approach to recommendation in multimedia browsing systems, based on an importance ranking method that strongly resembles the well known PageRank ranking system. We model recommendation as a social choice problem, and propose a method that computes customized recommendations by originally combing intrinsic features of multimedia objects, past behavior of individual users and overall behavior of the entire community of users. We implemented a prototype of the proposed system and preliminary experiments have shown that our approach is promising.

High-Level Surveillance Event Detection Using an Interval-Based Query Language

We propose a language based on relational algebra extended by intervals for detecting high-level surveillance events from a video stream. The operators we introduce for describing temporal constraints are based on the well-known Allens interval relationships. The semantics of our language are clearly defined and we illustrate its usefulness by expressing typical events in it and showing the promising results of an experimental evaluation.

Human-Computer Interaction in Healthcare: How to Support Patients during Their Wrist Rehabilitation

The increasing use of IT/Informatics within the healthcare context is more and more helpful for both medical doctors and patients in all the surgical specialities. In this paper, we propose a low-cost system exploiting a haptic interface aided by a glove sensorized on the wrist allowing the identification of the wrist orientation for supporting patients during their wrist rehabilitation.