Ontology-based explanation of classifiers

The rise of data mining and machine learning use in many applications has brought new challenges related to classification. Here, we deal with the following challenge: how to interpret and understand the reason behind a classifier's prediction. Indeed, understanding the behaviour of a classifier is widely recognized as a very important task for wide and safe adoption of machine learning and data mining technologies, especially in high-risk domains, and in dealing with bias.We present a preliminary work on a proposal of using the Ontology-Based Data Management paradigm for explaining the behavior of a classifier in terms of the concepts and the relations that are meaningful in the domain that is relevant for the classifier

Visual exploration and retrieval of XML document collections with the generic system X2

This article reports on the XML retrieval system X2 which has been developed at the University of Munich over the last five years. In a typical session with X2, the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed

Interaction protocols for human-driven crisis resolution processes

This work aims at providing a crisis cell with process-oriented tools to manage crisis resolutions. Indeed, the crisis cell members have to define the crisis resolution process, adapt it to face crisis evolutions, and guide its execution. Crisis resolution processes are interaction-intensive processes: they not only coordinate the performance of tasks to be undertaken on the impacted world, but they also support regulatory interactions between possibly geographically distributed crisis cell members. In order to deal with such an interweaving, this paper proposes to use Interaction Protocols to both model formal interactions and ease a cooperative adaptation and guidance of crisis resolution processes. After highlighting the benefits of Interaction Protocols to support this human and collective dimension, the paper presents a protocol meta-model for their specification. It then shows how to suitably integrate specified protocols into crisis resolution processes and how to implement this conceptual framework into a service oriented architecture

Towards a Pervasive Access Control within Video Surveillance Systems

Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013)International audienceThis paper addresses two emerging challenges that multimedia distributed systems have to deal with: the user’s constant mobility and the information’s sensitivity. The systems have to adapt, in real time, to the user’s context and situation in order to provide him with relevant results without breaking the security and privacy policies. Distributed multimedia systems, such as the oneproposed by the LINDO project, do not generally consider both issues. In this paper, we apply an access control layer on top of the LINDO architecture that takes into consideration the user’s context and situation and recommends alternative resources to the user when he is facing an important situation. The proposed solution was implemented and tested in a video surveillance use case

End-user Algorithmic Auditing for Music Discoverability: A Research Roadmap

The evolution of online platforms over the past decades has radically transformed the way people discover music, and thanks to social media and music streaming services nowadays listeners have access to an ever-increasing amount of tracks and artists. Within these platforms, one of the goals of recommender systems is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available. However, these systems have come under scrutiny from the scientific community, policy-makers, and civil society due to their potential negative societal impact, notably with regard to issues of fairness, non-discrimination, inclusion and diversity. Algorithmic auditing has emerged as a tool to analyse the problematic behaviours exhibited by recommenders, and to offer remedies that can limit their negative impact. In this position paper, we advocate for the involvement of end-users in the auditing process, which can contribute to the recognition, analysis, and mitigation of problematic behaviours which may arise while discovering music. Highlighting how recommenders, by influencing the discoverability of music, may impact listeners’ exposure to culturally diverse content, we seek to address the challenges posed by music recommender systems problematic behaviours, ultimately with the goal of fostering a more inclusive and diverse environment for music discovery within the digital landscape

A game-based learning experience for improving cybersecurity awareness

The use of videogames is an established tool to train a systematic way of thinking that allows users to learn by gaming. In this paper, to address the increasing need of awareness in cybersecurity related issues, we present the realization of a Virtual Reality (VR) videogame targeted towards educating users in the context of cybersecurity

EDD: A Web-Based Editor for Declarative Process Models Using easyDeclare

In process mining, declarative process discovery aims to extract declarative process models from event logs. Declare is a declarative process modeling language that provides an extensible set of standard templates for modeling declarative processes imposing temporal constraints on the sequence of process activities. The easyDeclare graphical notation has recently been developed to represent Declare models. This graphical notation has been proven to enhance human understandability of declarative process discovery results and reduce the cognitive load required to interpret these models compared to the original Declare graphical notation. This paper presents EDD, a web-based editor for declarative process models using the easyDeclare notation. Researchers and practitioners in the process mining field have evaluated the usability of EDD, resulting in a high usability rating. The tool can be used directly within a web browser or by cloning its GitHub repository. As an open-source project, EDD is available to the research community for use and further improvement

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management