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

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

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

Pancreatic enzyme supplementation after gastrectomy for gastric cancer: a randomized controlled trial

Background: Gastrectomy for gastric cancer is a significant cause of secondary exocrine pancreatic insufficiency. Pancreatic enzyme replacement therapy may influence nutritional status and quality of life after gastrectomy, but the pertinent clinical research to date remains controversial. A randomized controlled trial to test this hypothesis was carried out. Methods: After gastrectomy, 43 patients with gastric cancer were randomly assigned to a normal diet (Normal-d; n = 21) or to a pancreatic enzyme supplementation diet (PES-d; n = 22) and were followed up during a 12-month period, assessing nutritional status and quality of life through body mass index (BMI), instant nutritional assessment (INA) class status, serum pre-albumin (SPA) values, and GastroiIntestinal Quality of Life Index (GIQLI). Results: BMI was not significantly influenced by the type of diet; INA class status was significantly improved in the PES-d arm, particularly during the first 3 months after gastrectomy; SPA levels increased in both arms at 6 months after gastrectomy, reaching significantly higher values in the PES-d arm at 12 months. GIQLI was not significantly influenced by the type of diet throughout the follow-up period; however, this index significantly improved in the PES-d arm between the first and third month after gastrectomy. Conclusions: PES-d improves nutritional status and quality of life after gastrectomy for gastric cancer, particularly within 3 months from the operation. A larger, multicenter trial is necessary to address the potential influence of several confounding variables such as disease stage and adjuvant treatments

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

Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems

High adherence to enhanced recovery pathway independently reduces major morbidity and mortality rates after colorectal surgery: A reappraisal of the iCral2 and iCral3 multicenter prospective studies

Background: Enhanced recovery after surgery (ERAS) offers lower overall morbidity rates and shorter hospital stay after colorectal surgery (CRS); high adherence rates to ERAS may significantly reduce major morbidity (MM), anastomotic leakage (AL), and mortality (M) rates as well. Methods: Prospective enrollment of patients submitted to elective CRS with anastomosis in two separate 18- and 12-month periods among 78 surgical centers in Italy from 2019 to 2021. Adherence to ERAS pathway items was measured upon explicit criteria in every case. After univariate analysis, independent predictors of primary endpoints (MM, AL, and M rates) were identified through logistic regression analyses, presenting odds ratios (OR) and 95% confidence intervals. Results: An institutional ERAS status was declared by 48 out of 78 (61.5%) participating centers. The median overall adherence to ERAS was 75%. Among 8,359 patients included in both studies, MM, AL, and M rates were 6.3%, 4.4%, and 1.0%, respectively. Several patient-related and treatment-related variables showed independently higher rates for primary endpoints: male gender, American Society of Anesthesiologists class III, neoadjuvant treatment, perioperative steroids, intra- and/or postoperative blood transfusions, length of the operation >180’, surgery for malignancy. On the other hand, ERAS adherence >85% independently reduced MM (OR, 0.91) and M (OR, 0.25) rates, whereas no mechanical bowel preparation independently reduced AL (OR, 0.68) rates. Conclusions: Among other patient- or treatment-related variables, ERAS adherence >85% independently reduced MM and M rates, whereas no mechanical bowel preparation independently reduced AL rates after CRS

Emergent semantics in distributed knowledge management

Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context- and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (e.g., during data interchange) may dramatically increase their business value. This chapter introduce and discuss the notion of Emergent Semantics (ES), where both the representation of semantics and the discovery of the proper interpretation of symbols are seen as the result of a selforganizing process performed by distributed agents, exchanging symbols and adaptively developing the proper interpretation via multi-party cooperation and conflict resolution. Emergent data semantics is dynamically dependent on the collective behaviour of large communities of agents, which may have different and even conflicting interests and agendas. This is a research paradigm interpreting semantics from a pragmatic prospective. The chapter introduce this notion providing a discussion on the principles, research area and current state of the art